Meg Ma Ping Yu The SMARTLHSONTO comprehensively represents the knowledge of Learning Health System Framework, including the key concepts and their complex relationships as identified in the existing mainstream framework theories. It provides the foundation model for the organisational researchers and practitioners to organize data in accordance with the identified relationships in the established theory and to visually understand the complex concepts and their interconnected relationships. Smart Learning Health System Framework © 2024 by Meg Ma and Ping Yu is licensed under CC BY-NC-SA 4.0 https://w3id.org/smartlhs Smart Learning Health Systems Ontology - SMARTLHSONTO Ontology: SMARTLHSONTO Version: 3.0 Description: BFO-aligned version of the SMART Learning Health System Ontology Release date: 2026 Previous version: SMARTLHSONTOV1 Relates an entity in the ontology to the name of the variable that is used to represent it in the code that generates the BFO OWL file from the lispy specification. Really of interest to developers only BFO OWL specification label Relates an entity in the ontology to the term that is used to represent it in the the CLIF specification of BFO2 Person:Alan Ruttenberg Really of interest to developers only BFO CLIF specification label editor preferred label editor preferred term editor preferred term editor preferred term~editor preferred label The concise, meaningful, and human-friendly name for a class or property preferred by the ontology developers. (US-English) editor preferred label editor preferred term editor preferred term editor preferred term~editor preferred label example of usage definition definition textual definition The official OBI definition, explaining the meaning of a class or property. Shall be Aristotelian, formalized and normalized. Can be augmented with colloquial definitions. The official definition, explaining the meaning of a class or property. Shall be Aristotelian, formalized and normalized. Can be augmented with colloquial definitions. definition definition textual definition editor note term editor alternative label A label for a class or property that can be used to refer to the class or property instead of the preferred rdfs:label. Alternative labels should be used to indicate community- or context-specific labels, abbreviations, shorthand forms and the like. alternative term definition source curator note imported from elucidation has associated axiom(nl) has associated axiom(fol) has axiom label label label has specified input has specified output part of has part bearer of Inverse of characteristic_of bearer_of is bearer of participates in a relation between a continuant and a process, in which the continuant is somehow involved in the process participates_in has participant a relation between a process and a continuant, in which the continuant is somehow involved in the process has_participant has role a relation between an independent continuant (the bearer) and a role, in which the role specifically depends on the bearer for its existence has_role has role Indicates that Governance is accountable for Organisations. accoutable For Organisation LHS need to align health equity with key components of LHS. align Health Equity Indicates that a pillar aligns its strategies, standards, or processes with a theme. aligns With Indicates that data is embedded within technology such as clinical workflow in EHR systems. are Ebmedded In Technology Indicate that consumers can assess some standards. assess Standards Indicates that the design of the model of care are associated with LHS benefits associate With Benefits Indicates that Leadership collaborates with workforce in achieving LHS values collaborate With Workforce Indicates that Data can be used for comparing benefits as LHS evaluation. compare Benefits Indicate that community are composed of consumers in Learning Health System. composed Of Consumer Indicate that some LHS pillars conforms with Governance and Leadership conforms With Indicates that Leadership consults Workforce in defining culture and promoting culture consult Workforce Indicates that consumer can contribute some data such as self assessment and self monitoring. contribute Data Indicates that some LHS pillars create values for "Values Creation and Benefits" creates Value For Indicates that LHS policies define benefits that should be realised. define Benefits Indicates that Leadership defines LHS Culture define Culture Indicates that some LHS pillars define "Culture and Change Management" defines Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. People and resources must be organisedto deliver the strategy. deliver Strategies Indicates that pillars depend on cross-cutting themes to address ethical, evaluative, or value-based concerns. depends On indicates that LHS deploy implementation process deploy Implementation Process Indicates that strategies should design LHS benefits. design Benefits Indicates that Governance decides organisation cultur and organisations develop culture. develop Culture Indicates that organisations develop LHS policies. develop Policy Indicates that workforce should develop LHS required skillsets. develop Skillsets Indicates that organisations partner with workforce develop LHS strategies. develop Strategy Indicates that some LHS pillars develop "Workforce and Capacity Building" develops Indicates that Governance to communicate organization culture in various channels. disseminate Culture Indicates that technology embed LHS data. embed Data Indicates that workforce should embrace LHS culture. embrace Culture Indicate the relatonship between top pillars embraces Indicates that organisations and workforce employ LHS required technology. employ Technology indicates that sometimes, implementing a LHS require to follow a certain type of LHS Framework. employ LHS Framework to indicate the relationship between top pillars employs Indicates that Leadership empower workforce to promote LHS Culture. empower Workforce indicates that some LHS pillars empower 'Workforce and Capacity Building" empowers Indicates that some LHS pillar enables Care Delivery and Organisational Strategies enables Indicates that LHS policies should enforce standards. enforce Standard Indicates culture is to engage some consumers such as promoting patient centred care to involve patients in shared care decision making. engage Consumer Indicates that cross-cutting themes evaluate the outcomes or processes of pillars. evaluates Indicates that Data can be used for provide feedback to Governance on the effectiveness of LHS. feedback To Governance Indicates that Data can be used for provide feedback to Leadership on the effectiveness of LHS. feedback To Leadership Indicates that Governance focus on consumer. focus On Consumer Indicates that Governance should focus on building strong LHS culture. focus On Culture Indicates that Governance gather LHS data. gather Data Indicates that governance guide organisations guide Organisation to indicates that pillars are guided by the principles or oversight of a cross-cutting theme. guides Indicates the benefits derived from a Learning Health System. has Benefit Indicate LHS Organisations has capabilities relationship with "LHS Organisational Capabilities" class. Indicates the cultural aspects present within a Learning Health System has Culture Indicates the relationship between LHS and its supporting pillars hasPillar Indicate the relationship between Learning Health System to its cutting through themes hasTheme An early analysis of these systems, including strengths, gaps and opportunities for development, is therefore often a prerequisite to developing the technical building blocks. Resources are always limited and this analysis can help to prioritise where they can have most impact. has Building Blocks Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Healthcare is a complex adaptive system.It has many interactingelements, the whole is greater than the sum of the parts and out-comes are often unpredictable. As well as a clear rationale, anunderstanding of the complexity associated with health and LearningHealth Systems is central to initiating, implementing, and sustaining Learning Health Systems in the real world. has Complexity Academic features that emphasize how an aLHS differs from an LHS, especially from Academic Medical Center's (AMC) point of view. has Research and Education Indicates that LHS has core competencies has Core Competencies Indicates that organisations and workforce implement LHS standards. implement Standard Indicates that Incentives are designed around policies. incentivise Policy Indicates that Governance includes Incentives as part of its structure include Incentives Indicates that a Learning Health System includes various organisations include Organisation indicates that data can be used to influence policy making. influence Policy Indicates that some LHS pillars influence Culture and Change Management influences Indicates that Leadership keep workforce informed all LHS related initiatives. inform Workforce Indicates that some LHS pillars informs "Community and Patient Engagement" informs Indicates the bidirectional integration of themes with pillars. integrates With Indicates that governance interact with workforce. interact Woith Workforce Indicates that Leadership and organisations make decisions to invest in LHS technology invest In Technology Indicates that some pillars of LHS make decisions to invest in LHS technology infrastructure and data invests In indicates that LHS invovles stakeholders who are patients, community, clinicians and more. invovles Stakeholders is About Indicates some pillar is accountable for organizations and strategies and mode of care is Accountable For Indicates that some LHS pillars are closely linked with other pillars is Closely Linked With is realised By Indicates the LHS Framework is implemented by LHS systems or organisations. is implemented By is Mapped To is governed By is operationalised By Indicates that Leadership can leverage data for LHS benefits or decision making. Leverage Data indicates that some LHS pillars leverage "Data and Tehcnology Infrastructure" leverages Indicates that standards should be developed to measure LHS benefits. measure Benefits Indicates that some LHS pillars measure 'Value Creation and Benefits" measures Indicates that LHS technology should meet standards. meet Standard Indicates that organisations offer LHS benefit via implementing LHS. offer Benefits operationalises Indicates that governance oversight LHS leadership oversight Leadership Indicates that consumer can participate in the culture such as advodating for continuous learning and inprovement, data driven decision making etc. participate In Culture Indicates that the LHS organisations participate in some processes participate In Indicates that consumer can partner with community based on their condition, geographic location, occupation and ethnic background. partner With Community Indicates that participants considered the involvement of consumers as important in a LHS as they could make valued contributionto prioritising data, commenting on measures to be collected and analysed, and deciding time frames for data collection. prioritise Data Indicates that Leadership promote LHS culture promote Culture Indicate that consumer promote patient centred care. promote Model of Care Indicates that some LHS pillars promote Care Delivery and Organisational Strategies promotes Indicate that Learning Health System Data provide evidence based support for consumer. provide Evidence Based Support For Consumer Indicate that Learning Health System provides evidence based support for LHS workforce. provide Evidence Based Support For Workforce Indicates that LHS policies should provide framework for LHS culture. provide Framework For Culture Indicates that LHS policies should provide framework for LHS governance. provide Framework For Governance realises Indicate relationship or attribute that connects benefits (such as health outcomes or advantages) to specific policies (such as guidelines or regulations) reflect In Policy Indicates that 'Value Creation and Benefits" is reflected in "Goverannce and Leadership" reflects In Indicates that LHS policies regulate data processes. regulate Data Indicates that LHS policies regulate how technology should be invested, employed and used. regulate Technology Indicates that some LHS pillars reinforces 'Culture and Change Management' reinforces Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. "Data to Knowledge" requires many statistical and deliberate methods. require Analytic Approaches for 'Data To Knowledg' Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Change approaches requrie Culture as a central to a Learning Health System. require Culture Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. "Practice To Data" building block reuqires Data require Data for 'Practice To Data" A Learning Health System requires a strategy andmust be part of the broader strategy of its host organisation(s).The strategy is a set of inter-related choices on how the organisa-tion will deliver value. 38 It can be an explicit document, outlininggoals, priorities and actions, or it can be “a pattern in a streamof decisions” - a strategic direction. require Strategies Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Much of the infrastructure required to build a Learning Health System is common across organisations. This can include IT, Electronic Record Systems, data transport, storage and processing,decision support and even governance arrangements and organisational structures. require Technology for Platform Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Developing a Learning Health System is a multidisciplinary endeavour. Patients, carers, clinicians, researchers, designers, strategists, analysts and other professionals have a role to play, depending on the particular implementation. require Workforce Continuous improvement within a Learning Health System requires change. The earlier report, workshops and the literature, suggest a number of areas in which strategic approaches to change are required require Change Approaches Indicates that required workforce skillsets should support culture. required For Culture Indicates that workforce skillsets should be required for LHS strategies. required For Strategy indicates that some LHS pillars require Workforce and Capacity Building requires Indicates that governance review standards. review Standard Indicate that Patient Centred Care benefits reward the culture of "continous learning and improvement" reward Culture Indicates that LHS governance should reward leadership when LHS benefits are achieved. reward Leadership Indicates that Leadership set directions for strategies. set Direction For Strategy Indicates that leadership set vision for strategies set Vision For Strategy Indicates that some LHS pillars shapes "Care Delivery and Organisational Strategies" shapes Indicates that consumer can share their data such as past treatment histoy and organisations share their data with other organisations. share Data specifies Process Indicates that LHS technology should support LHS culture. support Culture Indicates that organisations support model of care and patient centered care. support Model Of Care Indicates that LHS technology implementation should support strategies. support Strategy Indicates that LHS policy should support workforce. support Workforce LHS Frameworks are to support LHS planning and implementation. support LHS Implementation To define the relationship between the Learning Health System (LHS) and its themes supports Indicates that consumer and workforce should understand the benefits of LHS. understand Benefits Indicates that leadership uphold standards. uphold Standard Indicates that consumer use Model of Care. use Model Of Care Indicates that consumer use some technology such as patient portal platform. use Technology Indicates that a Learning Health System utilizes data for its operations and improvement processes utilises Data Indicates that a Learning Health System utilizes specific technologies. utilises Technology entity Entity Julius Caesar Verdi’s Requiem the Second World War your body mass index BFO 2 Reference: In all areas of empirical inquiry we encounter general terms of two sorts. First are general terms which refer to universals or types:animaltuberculosissurgical procedurediseaseSecond, are general terms used to refer to groups of entities which instantiate a given universal but do not correspond to the extension of any subuniversal of that universal because there is nothing intrinsic to the entities in question by virtue of which they – and only they – are counted as belonging to the given group. Examples are: animal purchased by the Emperortuberculosis diagnosed on a Wednesdaysurgical procedure performed on a patient from Stockholmperson identified as candidate for clinical trial #2056-555person who is signatory of Form 656-PPVpainting by Leonardo da VinciSuch terms, which represent what are called ‘specializations’ in [81 Entity doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. For example Werner Ceusters 'portions of reality' include 4 sorts, entities (as BFO construes them), universals, configurations, and relations. It is an open question as to whether entities as construed in BFO will at some point also include these other portions of reality. See, for example, 'How to track absolutely everything' at http://www.referent-tracking.com/_RTU/papers/CeustersICbookRevised.pdf An entity is anything that exists or has existed or will exist. (axiom label in BFO2 Reference: [001-001]) entity Entity doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. For example Werner Ceusters 'portions of reality' include 4 sorts, entities (as BFO construes them), universals, configurations, and relations. It is an open question as to whether entities as construed in BFO will at some point also include these other portions of reality. See, for example, 'How to track absolutely everything' at http://www.referent-tracking.com/_RTU/papers/CeustersICbookRevised.pdf per discussion with Barry Smith An entity is anything that exists or has existed or will exist. (axiom label in BFO2 Reference: [001-001]) continuant Continuant An entity that exists in full at any time in which it exists at all, persists through time while maintaining its identity and has no temporal parts. BFO 2 Reference: Continuant entities are entities which can be sliced to yield parts only along the spatial dimension, yielding for example the parts of your table which we call its legs, its top, its nails. ‘My desk stretches from the window to the door. It has spatial parts, and can be sliced (in space) in two. With respect to time, however, a thing is a continuant.’ [60, p. 240 Continuant doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. For example, in an expansion involving bringing in some of Ceuster's other portions of reality, questions are raised as to whether universals are continuants A continuant is an entity that persists, endures, or continues to exist through time while maintaining its identity. (axiom label in BFO2 Reference: [008-002]) if b is a continuant and if, for some t, c has_continuant_part b at t, then c is a continuant. (axiom label in BFO2 Reference: [126-001]) if b is a continuant and if, for some t, cis continuant_part of b at t, then c is a continuant. (axiom label in BFO2 Reference: [009-002]) if b is a material entity, then there is some temporal interval (referred to below as a one-dimensional temporal region) during which b exists. (axiom label in BFO2 Reference: [011-002]) (forall (x y) (if (and (Continuant x) (exists (t) (continuantPartOfAt y x t))) (Continuant y))) // axiom label in BFO2 CLIF: [009-002] (forall (x y) (if (and (Continuant x) (exists (t) (hasContinuantPartOfAt y x t))) (Continuant y))) // axiom label in BFO2 CLIF: [126-001] (forall (x) (if (Continuant x) (Entity x))) // axiom label in BFO2 CLIF: [008-002] (forall (x) (if (Material Entity x) (exists (t) (and (TemporalRegion t) (existsAt x t))))) // axiom label in BFO2 CLIF: [011-002] continuant (forall (x) (if (Continuant x) (Entity x))) // axiom label in BFO2 CLIF: [008-002] (forall (x) (if (Material Entity x) (exists (t) (and (TemporalRegion t) (existsAt x t))))) // axiom label in BFO2 CLIF: [011-002] Continuant doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. For example, in an expansion involving bringing in some of Ceuster's other portions of reality, questions are raised as to whether universals are continuants A continuant is an entity that persists, endures, or continues to exist through time while maintaining its identity. (axiom label in BFO2 Reference: [008-002]) if b is a continuant and if, for some t, c has_continuant_part b at t, then c is a continuant. (axiom label in BFO2 Reference: [126-001]) if b is a continuant and if, for some t, cis continuant_part of b at t, then c is a continuant. (axiom label in BFO2 Reference: [009-002]) if b is a material entity, then there is some temporal interval (referred to below as a one-dimensional temporal region) during which b exists. (axiom label in BFO2 Reference: [011-002]) (forall (x y) (if (and (Continuant x) (exists (t) (continuantPartOfAt y x t))) (Continuant y))) // axiom label in BFO2 CLIF: [009-002] (forall (x y) (if (and (Continuant x) (exists (t) (hasContinuantPartOfAt y x t))) (Continuant y))) // axiom label in BFO2 CLIF: [126-001] occurrent Occurrent BFO 2 Reference: every occurrent that is not a temporal or spatiotemporal region is s-dependent on some independent continuant that is not a spatial region BFO 2 Reference: s-dependence obtains between every process and its participants in the sense that, as a matter of necessity, this process could not have existed unless these or those participants existed also. A process may have a succession of participants at different phases of its unfolding. Thus there may be different players on the field at different times during the course of a football game; but the process which is the entire game s-depends_on all of these players nonetheless. Some temporal parts of this process will s-depend_on on only some of the players. Occurrent doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. An example would be the sum of a process and the process boundary of another process. Simons uses different terminology for relations of occurrents to regions: Denote the spatio-temporal location of a given occurrent e by 'spn[e]' and call this region its span. We may say an occurrent is at its span, in any larger region, and covers any smaller region. Now suppose we have fixed a frame of reference so that we can speak not merely of spatio-temporal but also of spatial regions (places) and temporal regions (times). The spread of an occurrent, (relative to a frame of reference) is the space it exactly occupies, and its spell is likewise the time it exactly occupies. We write 'spr[e]' and `spl[e]' respectively for the spread and spell of e, omitting mention of the frame. An occurrent is an entity that unfolds itself in time or it is the instantaneous boundary of such an entity (for example a beginning or an ending) or it is a temporal or spatiotemporal region which such an entity occupies_temporal_region or occupies_spatiotemporal_region. (axiom label in BFO2 Reference: [077-002]) Every occurrent occupies_spatiotemporal_region some spatiotemporal region. (axiom label in BFO2 Reference: [108-001]) b is an occurrent entity iff b is an entity that has temporal parts. (axiom label in BFO2 Reference: [079-001]) (forall (x) (if (Occurrent x) (exists (r) (and (SpatioTemporalRegion r) (occupiesSpatioTemporalRegion x r))))) // axiom label in BFO2 CLIF: [108-001] (forall (x) (iff (Occurrent x) (and (Entity x) (exists (y) (temporalPartOf y x))))) // axiom label in BFO2 CLIF: [079-001] occurrent Occurrent doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. An example would be the sum of a process and the process boundary of another process. per discussion with Barry Smith Simons uses different terminology for relations of occurrents to regions: Denote the spatio-temporal location of a given occurrent e by 'spn[e]' and call this region its span. We may say an occurrent is at its span, in any larger region, and covers any smaller region. Now suppose we have fixed a frame of reference so that we can speak not merely of spatio-temporal but also of spatial regions (places) and temporal regions (times). The spread of an occurrent, (relative to a frame of reference) is the space it exactly occupies, and its spell is likewise the time it exactly occupies. We write 'spr[e]' and `spl[e]' respectively for the spread and spell of e, omitting mention of the frame. An occurrent is an entity that unfolds itself in time or it is the instantaneous boundary of such an entity (for example a beginning or an ending) or it is a temporal or spatiotemporal region which such an entity occupies_temporal_region or occupies_spatiotemporal_region. (axiom label in BFO2 Reference: [077-002]) Every occurrent occupies_spatiotemporal_region some spatiotemporal region. (axiom label in BFO2 Reference: [108-001]) b is an occurrent entity iff b is an entity that has temporal parts. (axiom label in BFO2 Reference: [079-001]) (forall (x) (if (Occurrent x) (exists (r) (and (SpatioTemporalRegion r) (occupiesSpatioTemporalRegion x r))))) // axiom label in BFO2 CLIF: [108-001] (forall (x) (iff (Occurrent x) (and (Entity x) (exists (y) (temporalPartOf y x))))) // axiom label in BFO2 CLIF: [079-001] ic IndependentContinuant a chair a heart a leg a molecule a spatial region an atom an orchestra. an organism the bottom right portion of a human torso the interior of your mouth b is an independent continuant = Def. b is a continuant which is such that there is no c and no t such that b s-depends_on c at t. (axiom label in BFO2 Reference: [017-002]) For any independent continuant b and any time t there is some spatial region r such that b is located_in r at t. (axiom label in BFO2 Reference: [134-001]) For every independent continuant b and time t during the region of time spanned by its life, there are entities which s-depends_on b during t. (axiom label in BFO2 Reference: [018-002]) (forall (x t) (if (IndependentContinuant x) (exists (r) (and (SpatialRegion r) (locatedInAt x r t))))) // axiom label in BFO2 CLIF: [134-001] (forall (x t) (if (and (IndependentContinuant x) (existsAt x t)) (exists (y) (and (Entity y) (specificallyDependsOnAt y x t))))) // axiom label in BFO2 CLIF: [018-002] (iff (IndependentContinuant a) (and (Continuant a) (not (exists (b t) (specificallyDependsOnAt a b t))))) // axiom label in BFO2 CLIF: [017-002] independent continuant b is an independent continuant = Def. b is a continuant which is such that there is no c and no t such that b s-depends_on c at t. (axiom label in BFO2 Reference: [017-002]) For any independent continuant b and any time t there is some spatial region r such that b is located_in r at t. (axiom label in BFO2 Reference: [134-001]) For every independent continuant b and time t during the region of time spanned by its life, there are entities which s-depends_on b during t. (axiom label in BFO2 Reference: [018-002]) (forall (x t) (if (IndependentContinuant x) (exists (r) (and (SpatialRegion r) (locatedInAt x r t))))) // axiom label in BFO2 CLIF: [134-001] (forall (x t) (if (and (IndependentContinuant x) (existsAt x t)) (exists (y) (and (Entity y) (specificallyDependsOnAt y x t))))) // axiom label in BFO2 CLIF: [018-002] (iff (IndependentContinuant a) (and (Continuant a) (not (exists (b t) (specificallyDependsOnAt a b t))))) // axiom label in BFO2 CLIF: [017-002] s-region SpatialRegion BFO 2 Reference: Spatial regions do not participate in processes. Spatial region doesn't have a closure axiom because the subclasses don't exhaust all possibilites. An example would be the union of a spatial point and a spatial line that doesn't overlap the point, or two spatial lines that intersect at a single point. In both cases the resultant spatial region is neither 0-dimensional, 1-dimensional, 2-dimensional, or 3-dimensional. A spatial region is a continuant entity that is a continuant_part_of spaceR as defined relative to some frame R. (axiom label in BFO2 Reference: [035-001]) All continuant parts of spatial regions are spatial regions. (axiom label in BFO2 Reference: [036-001]) (forall (x y t) (if (and (SpatialRegion x) (continuantPartOfAt y x t)) (SpatialRegion y))) // axiom label in BFO2 CLIF: [036-001] (forall (x) (if (SpatialRegion x) (Continuant x))) // axiom label in BFO2 CLIF: [035-001] spatial region Spatial region doesn't have a closure axiom because the subclasses don't exhaust all possibilites. An example would be the union of a spatial point and a spatial line that doesn't overlap the point, or two spatial lines that intersect at a single point. In both cases the resultant spatial region is neither 0-dimensional, 1-dimensional, 2-dimensional, or 3-dimensional. per discussion with Barry Smith A spatial region is a continuant entity that is a continuant_part_of spaceR as defined relative to some frame R. (axiom label in BFO2 Reference: [035-001]) All continuant parts of spatial regions are spatial regions. (axiom label in BFO2 Reference: [036-001]) (forall (x y t) (if (and (SpatialRegion x) (continuantPartOfAt y x t)) (SpatialRegion y))) // axiom label in BFO2 CLIF: [036-001] (forall (x) (if (SpatialRegion x) (Continuant x))) // axiom label in BFO2 CLIF: [035-001] t-region TemporalRegion Temporal region doesn't have a closure axiom because the subclasses don't exhaust all possibilites. An example would be the mereological sum of a temporal instant and a temporal interval that doesn't overlap the instant. In this case the resultant temporal region is neither 0-dimensional nor 1-dimensional A temporal region is an occurrent entity that is part of time as defined relative to some reference frame. (axiom label in BFO2 Reference: [100-001]) All parts of temporal regions are temporal regions. (axiom label in BFO2 Reference: [101-001]) Every temporal region t is such that t occupies_temporal_region t. (axiom label in BFO2 Reference: [119-002]) (forall (r) (if (TemporalRegion r) (occupiesTemporalRegion r r))) // axiom label in BFO2 CLIF: [119-002] (forall (x y) (if (and (TemporalRegion x) (occurrentPartOf y x)) (TemporalRegion y))) // axiom label in BFO2 CLIF: [101-001] (forall (x) (if (TemporalRegion x) (Occurrent x))) // axiom label in BFO2 CLIF: [100-001] temporal region Temporal region doesn't have a closure axiom because the subclasses don't exhaust all possibilites. An example would be the mereological sum of a temporal instant and a temporal interval that doesn't overlap the instant. In this case the resultant temporal region is neither 0-dimensional nor 1-dimensional per discussion with Barry Smith A temporal region is an occurrent entity that is part of time as defined relative to some reference frame. (axiom label in BFO2 Reference: [100-001]) All parts of temporal regions are temporal regions. (axiom label in BFO2 Reference: [101-001]) Every temporal region t is such that t occupies_temporal_region t. (axiom label in BFO2 Reference: [119-002]) (forall (r) (if (TemporalRegion r) (occupiesTemporalRegion r r))) // axiom label in BFO2 CLIF: [119-002] (forall (x y) (if (and (TemporalRegion x) (occurrentPartOf y x)) (TemporalRegion y))) // axiom label in BFO2 CLIF: [101-001] (forall (x) (if (TemporalRegion x) (Occurrent x))) // axiom label in BFO2 CLIF: [100-001] 2d-s-region TwoDimensionalSpatialRegion an infinitely thin plane in space. the surface of a sphere-shaped part of space A two-dimensional spatial region is a spatial region that is of two dimensions. (axiom label in BFO2 Reference: [039-001]) (forall (x) (if (TwoDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [039-001] two-dimensional spatial region A two-dimensional spatial region is a spatial region that is of two dimensions. (axiom label in BFO2 Reference: [039-001]) (forall (x) (if (TwoDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [039-001] st-region SpatiotemporalRegion the spatiotemporal region occupied by a human life the spatiotemporal region occupied by a process of cellular meiosis. the spatiotemporal region occupied by the development of a cancer tumor A spatiotemporal region is an occurrent entity that is part of spacetime. (axiom label in BFO2 Reference: [095-001]) All parts of spatiotemporal regions are spatiotemporal regions. (axiom label in BFO2 Reference: [096-001]) Each spatiotemporal region at any time t projects_onto some spatial region at t. (axiom label in BFO2 Reference: [099-001]) Each spatiotemporal region projects_onto some temporal region. (axiom label in BFO2 Reference: [098-001]) Every spatiotemporal region occupies_spatiotemporal_region itself. Every spatiotemporal region s is such that s occupies_spatiotemporal_region s. (axiom label in BFO2 Reference: [107-002]) (forall (r) (if (SpatioTemporalRegion r) (occupiesSpatioTemporalRegion r r))) // axiom label in BFO2 CLIF: [107-002] (forall (x t) (if (SpatioTemporalRegion x) (exists (y) (and (SpatialRegion y) (spatiallyProjectsOntoAt x y t))))) // axiom label in BFO2 CLIF: [099-001] (forall (x y) (if (and (SpatioTemporalRegion x) (occurrentPartOf y x)) (SpatioTemporalRegion y))) // axiom label in BFO2 CLIF: [096-001] (forall (x) (if (SpatioTemporalRegion x) (Occurrent x))) // axiom label in BFO2 CLIF: [095-001] (forall (x) (if (SpatioTemporalRegion x) (exists (y) (and (TemporalRegion y) (temporallyProjectsOnto x y))))) // axiom label in BFO2 CLIF: [098-001] spatiotemporal region A spatiotemporal region is an occurrent entity that is part of spacetime. (axiom label in BFO2 Reference: [095-001]) All parts of spatiotemporal regions are spatiotemporal regions. (axiom label in BFO2 Reference: [096-001]) Each spatiotemporal region at any time t projects_onto some spatial region at t. (axiom label in BFO2 Reference: [099-001]) Each spatiotemporal region projects_onto some temporal region. (axiom label in BFO2 Reference: [098-001]) Every spatiotemporal region s is such that s occupies_spatiotemporal_region s. (axiom label in BFO2 Reference: [107-002]) (forall (r) (if (SpatioTemporalRegion r) (occupiesSpatioTemporalRegion r r))) // axiom label in BFO2 CLIF: [107-002] (forall (x t) (if (SpatioTemporalRegion x) (exists (y) (and (SpatialRegion y) (spatiallyProjectsOntoAt x y t))))) // axiom label in BFO2 CLIF: [099-001] (forall (x y) (if (and (SpatioTemporalRegion x) (occurrentPartOf y x)) (SpatioTemporalRegion y))) // axiom label in BFO2 CLIF: [096-001] (forall (x) (if (SpatioTemporalRegion x) (Occurrent x))) // axiom label in BFO2 CLIF: [095-001] (forall (x) (if (SpatioTemporalRegion x) (exists (y) (and (TemporalRegion y) (temporallyProjectsOnto x y))))) // axiom label in BFO2 CLIF: [098-001] process Process a process of cell-division, \ a beating of the heart a process of meiosis a process of sleeping the course of a disease the flight of a bird the life of an organism your process of aging. p is a process = Def. p is an occurrent that has temporal proper parts and for some time t, p s-depends_on some material entity at t. (axiom label in BFO2 Reference: [083-003]) BFO 2 Reference: The realm of occurrents is less pervasively marked by the presence of natural units than is the case in the realm of independent continuants. Thus there is here no counterpart of ‘object’. In BFO 1.0 ‘process’ served as such a counterpart. In BFO 2.0 ‘process’ is, rather, the occurrent counterpart of ‘material entity’. Those natural – as contrasted with engineered, which here means: deliberately executed – units which do exist in the realm of occurrents are typically either parasitic on the existence of natural units on the continuant side, or they are fiat in nature. Thus we can count lives; we can count football games; we can count chemical reactions performed in experiments or in chemical manufacturing. We cannot count the processes taking place, for instance, in an episode of insect mating behavior.Even where natural units are identifiable, for example cycles in a cyclical process such as the beating of a heart or an organism’s sleep/wake cycle, the processes in question form a sequence with no discontinuities (temporal gaps) of the sort that we find for instance where billiard balls or zebrafish or planets are separated by clear spatial gaps. Lives of organisms are process units, but they too unfold in a continuous series from other, prior processes such as fertilization, and they unfold in turn in continuous series of post-life processes such as post-mortem decay. Clear examples of boundaries of processes are almost always of the fiat sort (midnight, a time of death as declared in an operating theater or on a death certificate, the initiation of a state of war) (iff (Process a) (and (Occurrent a) (exists (b) (properTemporalPartOf b a)) (exists (c t) (and (MaterialEntity c) (specificallyDependsOnAt a c t))))) // axiom label in BFO2 CLIF: [083-003] process p is a process = Def. p is an occurrent that has temporal proper parts and for some time t, p s-depends_on some material entity at t. (axiom label in BFO2 Reference: [083-003]) (iff (Process a) (and (Occurrent a) (exists (b) (properTemporalPartOf b a)) (exists (c t) (and (MaterialEntity c) (specificallyDependsOnAt a c t))))) // axiom label in BFO2 CLIF: [083-003] disposition Disposition an atom of element X has the disposition to decay to an atom of element Y certain people have a predisposition to colon cancer children are innately disposed to categorize objects in certain ways. the cell wall is disposed to filter chemicals in endocytosis and exocytosis BFO 2 Reference: Dispositions exist along a strength continuum. Weaker forms of disposition are realized in only a fraction of triggering cases. These forms occur in a significant number of cases of a similar type. b is a disposition means: b is a realizable entity & b’s bearer is some material entity & b is such that if it ceases to exist, then its bearer is physically changed, & b’s realization occurs when and because this bearer is in some special physical circumstances, & this realization occurs in virtue of the bearer’s physical make-up. (axiom label in BFO2 Reference: [062-002]) If b is a realizable entity then for all t at which b exists, b s-depends_on some material entity at t. (axiom label in BFO2 Reference: [063-002]) (forall (x t) (if (and (RealizableEntity x) (existsAt x t)) (exists (y) (and (MaterialEntity y) (specificallyDepends x y t))))) // axiom label in BFO2 CLIF: [063-002] (forall (x) (if (Disposition x) (and (RealizableEntity x) (exists (y) (and (MaterialEntity y) (bearerOfAt x y t)))))) // axiom label in BFO2 CLIF: [062-002] disposition b is a disposition means: b is a realizable entity & b’s bearer is some material entity & b is such that if it ceases to exist, then its bearer is physically changed, & b’s realization occurs when and because this bearer is in some special physical circumstances, & this realization occurs in virtue of the bearer’s physical make-up. (axiom label in BFO2 Reference: [062-002]) If b is a realizable entity then for all t at which b exists, b s-depends_on some material entity at t. (axiom label in BFO2 Reference: [063-002]) (forall (x t) (if (and (RealizableEntity x) (existsAt x t)) (exists (y) (and (MaterialEntity y) (specificallyDepends x y t))))) // axiom label in BFO2 CLIF: [063-002] (forall (x) (if (Disposition x) (and (RealizableEntity x) (exists (y) (and (MaterialEntity y) (bearerOfAt x y t)))))) // axiom label in BFO2 CLIF: [062-002] realizable RealizableEntity the disposition of this piece of metal to conduct electricity. the disposition of your blood to coagulate the function of your reproductive organs the role of being a doctor the role of this boundary to delineate where Utah and Colorado meet To say that b is a realizable entity is to say that b is a specifically dependent continuant that inheres in some independent continuant which is not a spatial region and is of a type instances of which are realized in processes of a correlated type. (axiom label in BFO2 Reference: [058-002]) All realizable dependent continuants have independent continuants that are not spatial regions as their bearers. (axiom label in BFO2 Reference: [060-002]) (forall (x t) (if (RealizableEntity x) (exists (y) (and (IndependentContinuant y) (not (SpatialRegion y)) (bearerOfAt y x t))))) // axiom label in BFO2 CLIF: [060-002] (forall (x) (if (RealizableEntity x) (and (SpecificallyDependentContinuant x) (exists (y) (and (IndependentContinuant y) (not (SpatialRegion y)) (inheresIn x y)))))) // axiom label in BFO2 CLIF: [058-002] realizable entity To say that b is a realizable entity is to say that b is a specifically dependent continuant that inheres in some independent continuant which is not a spatial region and is of a type instances of which are realized in processes of a correlated type. (axiom label in BFO2 Reference: [058-002]) All realizable dependent continuants have independent continuants that are not spatial regions as their bearers. (axiom label in BFO2 Reference: [060-002]) (forall (x t) (if (RealizableEntity x) (exists (y) (and (IndependentContinuant y) (not (SpatialRegion y)) (bearerOfAt y x t))))) // axiom label in BFO2 CLIF: [060-002] (forall (x) (if (RealizableEntity x) (and (SpecificallyDependentContinuant x) (exists (y) (and (IndependentContinuant y) (not (SpatialRegion y)) (inheresIn x y)))))) // axiom label in BFO2 CLIF: [058-002] 0d-s-region ZeroDimensionalSpatialRegion A zero-dimensional spatial region is a point in space. (axiom label in BFO2 Reference: [037-001]) (forall (x) (if (ZeroDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [037-001] zero-dimensional spatial region A zero-dimensional spatial region is a point in space. (axiom label in BFO2 Reference: [037-001]) (forall (x) (if (ZeroDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [037-001] quality Quality the ambient temperature of this portion of air the color of a tomato the length of the circumference of your waist the mass of this piece of gold. the shape of your nose the shape of your nostril a quality is a specifically dependent continuant that, in contrast to roles and dispositions, does not require any further process in order to be realized. (axiom label in BFO2 Reference: [055-001]) If an entity is a quality at any time that it exists, then it is a quality at every time that it exists. (axiom label in BFO2 Reference: [105-001]) (forall (x) (if (Quality x) (SpecificallyDependentContinuant x))) // axiom label in BFO2 CLIF: [055-001] (forall (x) (if (exists (t) (and (existsAt x t) (Quality x))) (forall (t_1) (if (existsAt x t_1) (Quality x))))) // axiom label in BFO2 CLIF: [105-001] quality a quality is a specifically dependent continuant that, in contrast to roles and dispositions, does not require any further process in order to be realized. (axiom label in BFO2 Reference: [055-001]) If an entity is a quality at any time that it exists, then it is a quality at every time that it exists. (axiom label in BFO2 Reference: [105-001]) (forall (x) (if (Quality x) (SpecificallyDependentContinuant x))) // axiom label in BFO2 CLIF: [055-001] (forall (x) (if (exists (t) (and (existsAt x t) (Quality x))) (forall (t_1) (if (existsAt x t_1) (Quality x))))) // axiom label in BFO2 CLIF: [105-001] sdc SpecificallyDependentContinuant Reciprocal specifically dependent continuants: the function of this key to open this lock and the mutually dependent disposition of this lock: to be opened by this key of one-sided specifically dependent continuants: the mass of this tomato of relational dependent continuants (multiple bearers): John’s love for Mary, the ownership relation between John and this statue, the relation of authority between John and his subordinates. the disposition of this fish to decay the function of this heart: to pump blood the mutual dependence of proton donors and acceptors in chemical reactions [79 the mutual dependence of the role predator and the role prey as played by two organisms in a given interaction the pink color of a medium rare piece of grilled filet mignon at its center the role of being a doctor the shape of this hole. the smell of this portion of mozzarella b is a specifically dependent continuant = Def. b is a continuant & there is some independent continuant c which is not a spatial region and which is such that b s-depends_on c at every time t during the course of b’s existence. (axiom label in BFO2 Reference: [050-003]) Specifically dependent continuant doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. We're not sure what else will develop here, but for example there are questions such as what are promises, obligation, etc. (iff (SpecificallyDependentContinuant a) (and (Continuant a) (forall (t) (if (existsAt a t) (exists (b) (and (IndependentContinuant b) (not (SpatialRegion b)) (specificallyDependsOnAt a b t))))))) // axiom label in BFO2 CLIF: [050-003] specifically dependent continuant b is a specifically dependent continuant = Def. b is a continuant & there is some independent continuant c which is not a spatial region and which is such that b s-depends_on c at every time t during the course of b’s existence. (axiom label in BFO2 Reference: [050-003]) Specifically dependent continuant doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. We're not sure what else will develop here, but for example there are questions such as what are promises, obligation, etc. per discussion with Barry Smith (iff (SpecificallyDependentContinuant a) (and (Continuant a) (forall (t) (if (existsAt a t) (exists (b) (and (IndependentContinuant b) (not (SpatialRegion b)) (specificallyDependsOnAt a b t))))))) // axiom label in BFO2 CLIF: [050-003] role Role John’s role of husband to Mary is dependent on Mary’s role of wife to John, and both are dependent on the object aggregate comprising John and Mary as member parts joined together through the relational quality of being married. the priest role the role of a boundary to demarcate two neighboring administrative territories the role of a building in serving as a military target the role of a stone in marking a property boundary the role of subject in a clinical trial the student role BFO 2 Reference: One major family of examples of non-rigid universals involves roles, and ontologies developed for corresponding administrative purposes may consist entirely of representatives of entities of this sort. Thus ‘professor’, defined as follows,b instance_of professor at t =Def. there is some c, c instance_of professor role & c inheres_in b at t.denotes a non-rigid universal and so also do ‘nurse’, ‘student’, ‘colonel’, ‘taxpayer’, and so forth. (These terms are all, in the jargon of philosophy, phase sortals.) By using role terms in definitions, we can create a BFO conformant treatment of such entities drawing on the fact that, while an instance of professor may be simultaneously an instance of trade union member, no instance of the type professor role is also (at any time) an instance of the type trade union member role (any more than any instance of the type color is at any time an instance of the type length).If an ontology of employment positions should be defined in terms of roles following the above pattern, this enables the ontology to do justice to the fact that individuals instantiate the corresponding universals – professor, sergeant, nurse – only during certain phases in their lives. b is a role means: b is a realizable entity & b exists because there is some single bearer that is in some special physical, social, or institutional set of circumstances in which this bearer does not have to be& b is not such that, if it ceases to exist, then the physical make-up of the bearer is thereby changed. (axiom label in BFO2 Reference: [061-001]) (forall (x) (if (Role x) (RealizableEntity x))) // axiom label in BFO2 CLIF: [061-001] role b is a role means: b is a realizable entity & b exists because there is some single bearer that is in some special physical, social, or institutional set of circumstances in which this bearer does not have to be& b is not such that, if it ceases to exist, then the physical make-up of the bearer is thereby changed. (axiom label in BFO2 Reference: [061-001]) (forall (x) (if (Role x) (RealizableEntity x))) // axiom label in BFO2 CLIF: [061-001] fiat-object-part FiatObjectPart or with divisions drawn by cognitive subjects for practical reasons, such as the division of a cake (before slicing) into (what will become) slices (and thus member parts of an object aggregate). However, this does not mean that fiat object parts are dependent for their existence on divisions or delineations effected by cognitive subjects. If, for example, it is correct to conceive geological layers of the Earth as fiat object parts of the Earth, then even though these layers were first delineated in recent times, still existed long before such delineation and what holds of these layers (for example that the oldest layers are also the lowest layers) did not begin to hold because of our acts of delineation.Treatment of material entity in BFOExamples viewed by some as problematic cases for the trichotomy of fiat object part, object, and object aggregate include: a mussel on (and attached to) a rock, a slime mold, a pizza, a cloud, a galaxy, a railway train with engine and multiple carriages, a clonal stand of quaking aspen, a bacterial community (biofilm), a broken femur. Note that, as Aristotle already clearly recognized, such problematic cases – which lie at or near the penumbra of instances defined by the categories in question – need not invalidate these categories. The existence of grey objects does not prove that there are not objects which are black and objects which are white; the existence of mules does not prove that there are not objects which are donkeys and objects which are horses. It does, however, show that the examples in question need to be addressed carefully in order to show how they can be fitted into the proposed scheme, for example by recognizing additional subdivisions [29 the FMA:regional parts of an intact human body. the Western hemisphere of the Earth the division of the brain into regions the division of the planet into hemispheres the dorsal and ventral surfaces of the body the upper and lower lobes of the left lung BFO 2 Reference: Most examples of fiat object parts are associated with theoretically drawn divisions b is a fiat object part = Def. b is a material entity which is such that for all times t, if b exists at t then there is some object c such that b proper continuant_part of c at t and c is demarcated from the remainder of c by a two-dimensional continuant fiat boundary. (axiom label in BFO2 Reference: [027-004]) (forall (x) (if (FiatObjectPart x) (and (MaterialEntity x) (forall (t) (if (existsAt x t) (exists (y) (and (Object y) (properContinuantPartOfAt x y t)))))))) // axiom label in BFO2 CLIF: [027-004] fiat object part b is a fiat object part = Def. b is a material entity which is such that for all times t, if b exists at t then there is some object c such that b proper continuant_part of c at t and c is demarcated from the remainder of c by a two-dimensional continuant fiat boundary. (axiom label in BFO2 Reference: [027-004]) (forall (x) (if (FiatObjectPart x) (and (MaterialEntity x) (forall (t) (if (existsAt x t) (exists (y) (and (Object y) (properContinuantPartOfAt x y t)))))))) // axiom label in BFO2 CLIF: [027-004] 1d-s-region OneDimensionalSpatialRegion an edge of a cube-shaped portion of space. A one-dimensional spatial region is a line or aggregate of lines stretching from one point in space to another. (axiom label in BFO2 Reference: [038-001]) (forall (x) (if (OneDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [038-001] one-dimensional spatial region A one-dimensional spatial region is a line or aggregate of lines stretching from one point in space to another. (axiom label in BFO2 Reference: [038-001]) (forall (x) (if (OneDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [038-001] object-aggregate ObjectAggregate a collection of cells in a blood biobank. a swarm of bees is an aggregate of members who are linked together through natural bonds a symphony orchestra an organization is an aggregate whose member parts have roles of specific types (for example in a jazz band, a chess club, a football team) defined by fiat: the aggregate of members of an organization defined through physical attachment: the aggregate of atoms in a lump of granite defined through physical containment: the aggregate of molecules of carbon dioxide in a sealed container defined via attributive delimitations such as: the patients in this hospital the aggregate of bearings in a constant velocity axle joint the aggregate of blood cells in your body the nitrogen atoms in the atmosphere the restaurants in Palo Alto your collection of Meissen ceramic plates. An entity a is an object aggregate if and only if there is a mutually exhaustive and pairwise disjoint partition of a into objects BFO 2 Reference: object aggregates may gain and lose parts while remaining numerically identical (one and the same individual) over time. This holds both for aggregates whose membership is determined naturally (the aggregate of cells in your body) and aggregates determined by fiat (a baseball team, a congressional committee). ISBN:978-3-938793-98-5pp124-158#Thomas Bittner and Barry Smith, 'A Theory of Granular Partitions', in K. Munn and B. Smith (eds.), Applied Ontology: An Introduction, Frankfurt/Lancaster: ontos, 2008, 125-158. b is an object aggregate means: b is a material entity consisting exactly of a plurality of objects as member_parts at all times at which b exists. (axiom label in BFO2 Reference: [025-004]) (forall (x) (if (ObjectAggregate x) (and (MaterialEntity x) (forall (t) (if (existsAt x t) (exists (y z) (and (Object y) (Object z) (memberPartOfAt y x t) (memberPartOfAt z x t) (not (= y z)))))) (not (exists (w t_1) (and (memberPartOfAt w x t_1) (not (Object w)))))))) // axiom label in BFO2 CLIF: [025-004] object aggregate An entity a is an object aggregate if and only if there is a mutually exhaustive and pairwise disjoint partition of a into objects An entity a is an object aggregate if and only if there is a mutually exhaustive and pairwise disjoint partition of a into objects ISBN:978-3-938793-98-5pp124-158#Thomas Bittner and Barry Smith, 'A Theory of Granular Partitions', in K. Munn and B. Smith (eds.), Applied Ontology: An Introduction, Frankfurt/Lancaster: ontos, 2008, 125-158. b is an object aggregate means: b is a material entity consisting exactly of a plurality of objects as member_parts at all times at which b exists. (axiom label in BFO2 Reference: [025-004]) (forall (x) (if (ObjectAggregate x) (and (MaterialEntity x) (forall (t) (if (existsAt x t) (exists (y z) (and (Object y) (Object z) (memberPartOfAt y x t) (memberPartOfAt z x t) (not (= y z)))))) (not (exists (w t_1) (and (memberPartOfAt w x t_1) (not (Object w)))))))) // axiom label in BFO2 CLIF: [025-004] 3d-s-region ThreeDimensionalSpatialRegion a cube-shaped region of space a sphere-shaped region of space, A three-dimensional spatial region is a spatial region that is of three dimensions. (axiom label in BFO2 Reference: [040-001]) (forall (x) (if (ThreeDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [040-001] three-dimensional spatial region A three-dimensional spatial region is a spatial region that is of three dimensions. (axiom label in BFO2 Reference: [040-001]) (forall (x) (if (ThreeDimensionalSpatialRegion x) (SpatialRegion x))) // axiom label in BFO2 CLIF: [040-001] site Site Manhattan Canyon) a hole in the interior of a portion of cheese a rabbit hole an air traffic control region defined in the airspace above an airport the Grand Canyon the Piazza San Marco the cockpit of an aircraft the hold of a ship the interior of a kangaroo pouch the interior of the trunk of your car the interior of your bedroom the interior of your office the interior of your refrigerator the lumen of your gut your left nostril (a fiat part – the opening – of your left nasal cavity) b is a site means: b is a three-dimensional immaterial entity that is (partially or wholly) bounded by a material entity or it is a three-dimensional immaterial part thereof. (axiom label in BFO2 Reference: [034-002]) (forall (x) (if (Site x) (ImmaterialEntity x))) // axiom label in BFO2 CLIF: [034-002] site b is a site means: b is a three-dimensional immaterial entity that is (partially or wholly) bounded by a material entity or it is a three-dimensional immaterial part thereof. (axiom label in BFO2 Reference: [034-002]) (forall (x) (if (Site x) (ImmaterialEntity x))) // axiom label in BFO2 CLIF: [034-002] object Object atom cell cells and organisms engineered artifacts grain of sand molecule organelle organism planet solid portions of matter star BFO 2 Reference: BFO rests on the presupposition that at multiple micro-, meso- and macroscopic scales reality exhibits certain stable, spatially separated or separable material units, combined or combinable into aggregates of various sorts (for example organisms into what are called ‘populations’). Such units play a central role in almost all domains of natural science from particle physics to cosmology. Many scientific laws govern the units in question, employing general terms (such as ‘molecule’ or ‘planet’) referring to the types and subtypes of units, and also to the types and subtypes of the processes through which such units develop and interact. The division of reality into such natural units is at the heart of biological science, as also is the fact that these units may form higher-level units (as cells form multicellular organisms) and that they may also form aggregates of units, for example as cells form portions of tissue and organs form families, herds, breeds, species, and so on. At the same time, the division of certain portions of reality into engineered units (manufactured artifacts) is the basis of modern industrial technology, which rests on the distributed mass production of engineered parts through division of labor and on their assembly into larger, compound units such as cars and laptops. The division of portions of reality into units is one starting point for the phenomenon of counting. BFO 2 Reference: Each object is such that there are entities of which we can assert unproblematically that they lie in its interior, and other entities of which we can assert unproblematically that they lie in its exterior. This may not be so for entities lying at or near the boundary between the interior and exterior. This means that two objects – for example the two cells depicted in Figure 3 – may be such that there are material entities crossing their boundaries which belong determinately to neither cell. Something similar obtains in certain cases of conjoined twins (see below). BFO 2 Reference: To say that b is causally unified means: b is a material entity which is such that its material parts are tied together in such a way that, in environments typical for entities of the type in question,if c, a continuant part of b that is in the interior of b at t, is larger than a certain threshold size (which will be determined differently from case to case, depending on factors such as porosity of external cover) and is moved in space to be at t at a location on the exterior of the spatial region that had been occupied by b at t, then either b’s other parts will be moved in coordinated fashion or b will be damaged (be affected, for example, by breakage or tearing) in the interval between t and t.causal changes in one part of b can have consequences for other parts of b without the mediation of any entity that lies on the exterior of b. Material entities with no proper material parts would satisfy these conditions trivially. Candidate examples of types of causal unity for material entities of more complex sorts are as follows (this is not intended to be an exhaustive list):CU1: Causal unity via physical coveringHere the parts in the interior of the unified entity are combined together causally through a common membrane or other physical covering\. The latter points outwards toward and may serve a protective function in relation to what lies on the exterior of the entity [13, 47 BFO 2 Reference: an object is a maximal causally unified material entity BFO 2 Reference: ‘objects’ are sometimes referred to as ‘grains’ [74 b is an object means: b is a material entity which manifests causal unity of one or other of the types CUn listed above & is of a type (a material universal) instances of which are maximal relative to this criterion of causal unity. (axiom label in BFO2 Reference: [024-001]) object b is an object means: b is a material entity which manifests causal unity of one or other of the types CUn listed above & is of a type (a material universal) instances of which are maximal relative to this criterion of causal unity. (axiom label in BFO2 Reference: [024-001]) gdc GenericallyDependentContinuant The entries in your database are patterns instantiated as quality instances in your hard drive. The database itself is an aggregate of such patterns. When you create the database you create a particular instance of the generically dependent continuant type database. Each entry in the database is an instance of the generically dependent continuant type IAO: information content entity. the pdf file on your laptop, the pdf file that is a copy thereof on my laptop the sequence of this protein molecule; the sequence that is a copy thereof in that protein molecule. b is a generically dependent continuant = Def. b is a continuant that g-depends_on one or more other entities. (axiom label in BFO2 Reference: [074-001]) (iff (GenericallyDependentContinuant a) (and (Continuant a) (exists (b t) (genericallyDependsOnAt a b t)))) // axiom label in BFO2 CLIF: [074-001] generically dependent continuant b is a generically dependent continuant = Def. b is a continuant that g-depends_on one or more other entities. (axiom label in BFO2 Reference: [074-001]) (iff (GenericallyDependentContinuant a) (and (Continuant a) (exists (b t) (genericallyDependsOnAt a b t)))) // axiom label in BFO2 CLIF: [074-001] function Function the function of a hammer to drive in nails the function of a heart pacemaker to regulate the beating of a heart through electricity the function of amylase in saliva to break down starch into sugar BFO 2 Reference: In the past, we have distinguished two varieties of function, artifactual function and biological function. These are not asserted subtypes of BFO:function however, since the same function – for example: to pump, to transport – can exist both in artifacts and in biological entities. The asserted subtypes of function that would be needed in order to yield a separate monoheirarchy are not artifactual function, biological function, etc., but rather transporting function, pumping function, etc. A function is a disposition that exists in virtue of the bearer’s physical make-up and this physical make-up is something the bearer possesses because it came into being, either through evolution (in the case of natural biological entities) or through intentional design (in the case of artifacts), in order to realize processes of a certain sort. (axiom label in BFO2 Reference: [064-001]) (forall (x) (if (Function x) (Disposition x))) // axiom label in BFO2 CLIF: [064-001] function A function is a disposition that exists in virtue of the bearer’s physical make-up and this physical make-up is something the bearer possesses because it came into being, either through evolution (in the case of natural biological entities) or through intentional design (in the case of artifacts), in order to realize processes of a certain sort. (axiom label in BFO2 Reference: [064-001]) (forall (x) (if (Function x) (Disposition x))) // axiom label in BFO2 CLIF: [064-001] p-boundary ProcessBoundary the boundary between the 2nd and 3rd year of your life. p is a process boundary =Def. p is a temporal part of a process & p has no proper temporal parts. (axiom label in BFO2 Reference: [084-001]) Every process boundary occupies_temporal_region a zero-dimensional temporal region. (axiom label in BFO2 Reference: [085-002]) (forall (x) (if (ProcessBoundary x) (exists (y) (and (ZeroDimensionalTemporalRegion y) (occupiesTemporalRegion x y))))) // axiom label in BFO2 CLIF: [085-002] (iff (ProcessBoundary a) (exists (p) (and (Process p) (temporalPartOf a p) (not (exists (b) (properTemporalPartOf b a)))))) // axiom label in BFO2 CLIF: [084-001] process boundary p is a process boundary =Def. p is a temporal part of a process & p has no proper temporal parts. (axiom label in BFO2 Reference: [084-001]) Every process boundary occupies_temporal_region a zero-dimensional temporal region. (axiom label in BFO2 Reference: [085-002]) (forall (x) (if (ProcessBoundary x) (exists (y) (and (ZeroDimensionalTemporalRegion y) (occupiesTemporalRegion x y))))) // axiom label in BFO2 CLIF: [085-002] (iff (ProcessBoundary a) (exists (p) (and (Process p) (temporalPartOf a p) (not (exists (b) (properTemporalPartOf b a)))))) // axiom label in BFO2 CLIF: [084-001] 1d-t-region OneDimensionalTemporalRegion the temporal region during which a process occurs. BFO 2 Reference: A temporal interval is a special kind of one-dimensional temporal region, namely one that is self-connected (is without gaps or breaks). A one-dimensional temporal region is a temporal region that is extended. (axiom label in BFO2 Reference: [103-001]) (forall (x) (if (OneDimensionalTemporalRegion x) (TemporalRegion x))) // axiom label in BFO2 CLIF: [103-001] one-dimensional temporal region A one-dimensional temporal region is a temporal region that is extended. (axiom label in BFO2 Reference: [103-001]) (forall (x) (if (OneDimensionalTemporalRegion x) (TemporalRegion x))) // axiom label in BFO2 CLIF: [103-001] material MaterialEntity material entity a flame a forest fire a human being a hurricane a photon a puff of smoke a sea wave a tornado an aggregate of human beings. an energy wave an epidemic the undetached arm of a human being An independent continuant that is spatially extended whose identity is independent of that of other entities and can be maintained through time. BFO 2 Reference: Material entities (continuants) can preserve their identity even while gaining and losing material parts. Continuants are contrasted with occurrents, which unfold themselves in successive temporal parts or phases [60 BFO 2 Reference: Object, Fiat Object Part and Object Aggregate are not intended to be exhaustive of Material Entity. Users are invited to propose new subcategories of Material Entity. BFO 2 Reference: ‘Matter’ is intended to encompass both mass and energy (we will address the ontological treatment of portions of energy in a later version of BFO). A portion of matter is anything that includes elementary particles among its proper or improper parts: quarks and leptons, including electrons, as the smallest particles thus far discovered; baryons (including protons and neutrons) at a higher level of granularity; atoms and molecules at still higher levels, forming the cells, organs, organisms and other material entities studied by biologists, the portions of rock studied by geologists, the fossils studied by paleontologists, and so on.Material entities are three-dimensional entities (entities extended in three spatial dimensions), as contrasted with the processes in which they participate, which are four-dimensional entities (entities extended also along the dimension of time).According to the FMA, material entities may have immaterial entities as parts – including the entities identified below as sites; for example the interior (or ‘lumen’) of your small intestine is a part of your body. BFO 2.0 embodies a decision to follow the FMA here. A material entity is an independent continuant that has some portion of matter as proper or improper continuant part. (axiom label in BFO2 Reference: [019-002]) Every entity which has a material entity as continuant part is a material entity. (axiom label in BFO2 Reference: [020-002]) every entity of which a material entity is continuant part is also a material entity. (axiom label in BFO2 Reference: [021-002]) (forall (x) (if (MaterialEntity x) (IndependentContinuant x))) // axiom label in BFO2 CLIF: [019-002] (forall (x) (if (and (Entity x) (exists (y t) (and (MaterialEntity y) (continuantPartOfAt x y t)))) (MaterialEntity x))) // axiom label in BFO2 CLIF: [021-002] (forall (x) (if (and (Entity x) (exists (y t) (and (MaterialEntity y) (continuantPartOfAt y x t)))) (MaterialEntity x))) // axiom label in BFO2 CLIF: [020-002] material entity A material entity is an independent continuant that has some portion of matter as proper or improper continuant part. (axiom label in BFO2 Reference: [019-002]) Every entity which has a material entity as continuant part is a material entity. (axiom label in BFO2 Reference: [020-002]) every entity of which a material entity is continuant part is also a material entity. (axiom label in BFO2 Reference: [021-002]) (forall (x) (if (MaterialEntity x) (IndependentContinuant x))) // axiom label in BFO2 CLIF: [019-002] (forall (x) (if (and (Entity x) (exists (y t) (and (MaterialEntity y) (continuantPartOfAt x y t)))) (MaterialEntity x))) // axiom label in BFO2 CLIF: [021-002] (forall (x) (if (and (Entity x) (exists (y t) (and (MaterialEntity y) (continuantPartOfAt y x t)))) (MaterialEntity x))) // axiom label in BFO2 CLIF: [020-002] cf-boundary ContinuantFiatBoundary b is a continuant fiat boundary = Def. b is an immaterial entity that is of zero, one or two dimensions and does not include a spatial region as part. (axiom label in BFO2 Reference: [029-001]) BFO 2 Reference: In BFO 1.1 the assumption was made that the external surface of a material entity such as a cell could be treated as if it were a boundary in the mathematical sense. The new document propounds the view that when we talk about external surfaces of material objects in this way then we are talking about something fiat. To be dealt with in a future version: fiat boundaries at different levels of granularity.More generally, the focus in discussion of boundaries in BFO 2.0 is now on fiat boundaries, which means: boundaries for which there is no assumption that they coincide with physical discontinuities. The ontology of boundaries becomes more closely allied with the ontology of regions. BFO 2 Reference: a continuant fiat boundary is a boundary of some material entity (for example: the plane separating the Northern and Southern hemispheres; the North Pole), or it is a boundary of some immaterial entity (for example of some portion of airspace). Three basic kinds of continuant fiat boundary can be distinguished (together with various combination kinds [29 Continuant fiat boundary doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. An example would be the mereological sum of two-dimensional continuant fiat boundary and a one dimensional continuant fiat boundary that doesn't overlap it. The situation is analogous to temporal and spatial regions. Every continuant fiat boundary is located at some spatial region at every time at which it exists (iff (ContinuantFiatBoundary a) (and (ImmaterialEntity a) (exists (b) (and (or (ZeroDimensionalSpatialRegion b) (OneDimensionalSpatialRegion b) (TwoDimensionalSpatialRegion b)) (forall (t) (locatedInAt a b t)))) (not (exists (c t) (and (SpatialRegion c) (continuantPartOfAt c a t)))))) // axiom label in BFO2 CLIF: [029-001] continuant fiat boundary b is a continuant fiat boundary = Def. b is an immaterial entity that is of zero, one or two dimensions and does not include a spatial region as part. (axiom label in BFO2 Reference: [029-001]) Continuant fiat boundary doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. An example would be the mereological sum of two-dimensional continuant fiat boundary and a one dimensional continuant fiat boundary that doesn't overlap it. The situation is analogous to temporal and spatial regions. (iff (ContinuantFiatBoundary a) (and (ImmaterialEntity a) (exists (b) (and (or (ZeroDimensionalSpatialRegion b) (OneDimensionalSpatialRegion b) (TwoDimensionalSpatialRegion b)) (forall (t) (locatedInAt a b t)))) (not (exists (c t) (and (SpatialRegion c) (continuantPartOfAt c a t)))))) // axiom label in BFO2 CLIF: [029-001] immaterial ImmaterialEntity BFO 2 Reference: Immaterial entities are divided into two subgroups:boundaries and sites, which bound, or are demarcated in relation, to material entities, and which can thus change location, shape and size and as their material hosts move or change shape or size (for example: your nasal passage; the hold of a ship; the boundary of Wales (which moves with the rotation of the Earth) [38, 7, 10 immaterial entity 1d-cf-boundary OneDimensionalContinuantFiatBoundary The Equator all geopolitical boundaries all lines of latitude and longitude the line separating the outer surface of the mucosa of the lower lip from the outer surface of the skin of the chin. the median sulcus of your tongue a one-dimensional continuant fiat boundary is a continuous fiat line whose location is defined in relation to some material entity. (axiom label in BFO2 Reference: [032-001]) (iff (OneDimensionalContinuantFiatBoundary a) (and (ContinuantFiatBoundary a) (exists (b) (and (OneDimensionalSpatialRegion b) (forall (t) (locatedInAt a b t)))))) // axiom label in BFO2 CLIF: [032-001] one-dimensional continuant fiat boundary a one-dimensional continuant fiat boundary is a continuous fiat line whose location is defined in relation to some material entity. (axiom label in BFO2 Reference: [032-001]) (iff (OneDimensionalContinuantFiatBoundary a) (and (ContinuantFiatBoundary a) (exists (b) (and (OneDimensionalSpatialRegion b) (forall (t) (locatedInAt a b t)))))) // axiom label in BFO2 CLIF: [032-001] process-profile ProcessProfile On a somewhat higher level of complexity are what we shall call rate process profiles, which are the targets of selective abstraction focused not on determinate quality magnitudes plotted over time, but rather on certain ratios between these magnitudes and elapsed times. A speed process profile, for example, is represented by a graph plotting against time the ratio of distance covered per unit of time. Since rates may change, and since such changes, too, may have rates of change, we have to deal here with a hierarchy of process profile universals at successive levels One important sub-family of rate process profiles is illustrated by the beat or frequency profiles of cyclical processes, illustrated by the 60 beats per minute beating process of John’s heart, or the 120 beats per minute drumming process involved in one of John’s performances in a rock band, and so on. Each such process includes what we shall call a beat process profile instance as part, a subtype of rate process profile in which the salient ratio is not distance covered but rather number of beat cycles per unit of time. Each beat process profile instance instantiates the determinable universal beat process profile. But it also instantiates multiple more specialized universals at lower levels of generality, selected from rate process profilebeat process profileregular beat process profile3 bpm beat process profile4 bpm beat process profileirregular beat process profileincreasing beat process profileand so on.In the case of a regular beat process profile, a rate can be assigned in the simplest possible fashion by dividing the number of cycles by the length of the temporal region occupied by the beating process profile as a whole. Irregular process profiles of this sort, for example as identified in the clinic, or in the readings on an aircraft instrument panel, are often of diagnostic significance. The simplest type of process profiles are what we shall call ‘quality process profiles’, which are the process profiles which serve as the foci of the sort of selective abstraction that is involved when measurements are made of changes in single qualities, as illustrated, for example, by process profiles of mass, temperature, aortic pressure, and so on. b is a process_profile =Def. there is some process c such that b process_profile_of c (axiom label in BFO2 Reference: [093-002]) b process_profile_of c holds when b proper_occurrent_part_of c& there is some proper_occurrent_part d of c which has no parts in common with b & is mutually dependent on b& is such that b, c and d occupy the same temporal region (axiom label in BFO2 Reference: [094-005]) (forall (x y) (if (processProfileOf x y) (and (properContinuantPartOf x y) (exists (z t) (and (properOccurrentPartOf z y) (TemporalRegion t) (occupiesSpatioTemporalRegion x t) (occupiesSpatioTemporalRegion y t) (occupiesSpatioTemporalRegion z t) (not (exists (w) (and (occurrentPartOf w x) (occurrentPartOf w z))))))))) // axiom label in BFO2 CLIF: [094-005] (iff (ProcessProfile a) (exists (b) (and (Process b) (processProfileOf a b)))) // axiom label in BFO2 CLIF: [093-002] process profile b is a process_profile =Def. there is some process c such that b process_profile_of c (axiom label in BFO2 Reference: [093-002]) b process_profile_of c holds when b proper_occurrent_part_of c& there is some proper_occurrent_part d of c which has no parts in common with b & is mutually dependent on b& is such that b, c and d occupy the same temporal region (axiom label in BFO2 Reference: [094-005]) (forall (x y) (if (processProfileOf x y) (and (properContinuantPartOf x y) (exists (z t) (and (properOccurrentPartOf z y) (TemporalRegion t) (occupiesSpatioTemporalRegion x t) (occupiesSpatioTemporalRegion y t) (occupiesSpatioTemporalRegion z t) (not (exists (w) (and (occurrentPartOf w x) (occurrentPartOf w z))))))))) // axiom label in BFO2 CLIF: [094-005] (iff (ProcessProfile a) (exists (b) (and (Process b) (processProfileOf a b)))) // axiom label in BFO2 CLIF: [093-002] r-quality RelationalQuality John’s role of husband to Mary is dependent on Mary’s role of wife to John, and both are dependent on the object aggregate comprising John and Mary as member parts joined together through the relational quality of being married. a marriage bond, an instance of requited love, an obligation between one person and another. b is a relational quality = Def. for some independent continuants c, d and for some time t: b quality_of c at t & b quality_of d at t. (axiom label in BFO2 Reference: [057-001]) (iff (RelationalQuality a) (exists (b c t) (and (IndependentContinuant b) (IndependentContinuant c) (qualityOfAt a b t) (qualityOfAt a c t)))) // axiom label in BFO2 CLIF: [057-001] relational quality b is a relational quality = Def. for some independent continuants c, d and for some time t: b quality_of c at t & b quality_of d at t. (axiom label in BFO2 Reference: [057-001]) (iff (RelationalQuality a) (exists (b c t) (and (IndependentContinuant b) (IndependentContinuant c) (qualityOfAt a b t) (qualityOfAt a c t)))) // axiom label in BFO2 CLIF: [057-001] 2d-cf-boundary TwoDimensionalContinuantFiatBoundary a two-dimensional continuant fiat boundary (surface) is a self-connected fiat surface whose location is defined in relation to some material entity. (axiom label in BFO2 Reference: [033-001]) (iff (TwoDimensionalContinuantFiatBoundary a) (and (ContinuantFiatBoundary a) (exists (b) (and (TwoDimensionalSpatialRegion b) (forall (t) (locatedInAt a b t)))))) // axiom label in BFO2 CLIF: [033-001] two-dimensional continuant fiat boundary a two-dimensional continuant fiat boundary (surface) is a self-connected fiat surface whose location is defined in relation to some material entity. (axiom label in BFO2 Reference: [033-001]) (iff (TwoDimensionalContinuantFiatBoundary a) (and (ContinuantFiatBoundary a) (exists (b) (and (TwoDimensionalSpatialRegion b) (forall (t) (locatedInAt a b t)))))) // axiom label in BFO2 CLIF: [033-001] 0d-cf-boundary ZeroDimensionalContinuantFiatBoundary the geographic North Pole the point of origin of some spatial coordinate system. the quadripoint where the boundaries of Colorado, Utah, New Mexico, and Arizona meet zero dimension continuant fiat boundaries are not spatial points. Considering the example 'the quadripoint where the boundaries of Colorado, Utah, New Mexico, and Arizona meet' : There are many frames in which that point is zooming through many points in space. Whereas, no matter what the frame, the quadripoint is always in the same relation to the boundaries of Colorado, Utah, New Mexico, and Arizona. a zero-dimensional continuant fiat boundary is a fiat point whose location is defined in relation to some material entity. (axiom label in BFO2 Reference: [031-001]) (iff (ZeroDimensionalContinuantFiatBoundary a) (and (ContinuantFiatBoundary a) (exists (b) (and (ZeroDimensionalSpatialRegion b) (forall (t) (locatedInAt a b t)))))) // axiom label in BFO2 CLIF: [031-001] zero-dimensional continuant fiat boundary zero dimension continuant fiat boundaries are not spatial points. Considering the example 'the quadripoint where the boundaries of Colorado, Utah, New Mexico, and Arizona meet' : There are many frames in which that point is zooming through many points in space. Whereas, no matter what the frame, the quadripoint is always in the same relation to the boundaries of Colorado, Utah, New Mexico, and Arizona. requested by Melanie Courtot a zero-dimensional continuant fiat boundary is a fiat point whose location is defined in relation to some material entity. (axiom label in BFO2 Reference: [031-001]) (iff (ZeroDimensionalContinuantFiatBoundary a) (and (ContinuantFiatBoundary a) (exists (b) (and (ZeroDimensionalSpatialRegion b) (forall (t) (locatedInAt a b t)))))) // axiom label in BFO2 CLIF: [031-001] 0d-t-region ZeroDimensionalTemporalRegion a temporal region that is occupied by a process boundary right now the moment at which a child is born the moment at which a finger is detached in an industrial accident the moment of death. temporal instant. A zero-dimensional temporal region is a temporal region that is without extent. (axiom label in BFO2 Reference: [102-001]) (forall (x) (if (ZeroDimensionalTemporalRegion x) (TemporalRegion x))) // axiom label in BFO2 CLIF: [102-001] zero-dimensional temporal region A zero-dimensional temporal region is a temporal region that is without extent. (axiom label in BFO2 Reference: [102-001]) (forall (x) (if (ZeroDimensionalTemporalRegion x) (TemporalRegion x))) // axiom label in BFO2 CLIF: [102-001] history History A history is a process that is the sum of the totality of processes taking place in the spatiotemporal region occupied by a material entity or site, including processes on the surface of the entity or within the cavities to which it serves as host. (axiom label in BFO2 Reference: [138-001]) history A history is a process that is the sum of the totality of processes taking place in the spatiotemporal region occupied by a material entity or site, including processes on the surface of the entity or within the cavities to which it serves as host. (axiom label in BFO2 Reference: [138-001]) information content entity A generically dependent continuant that is about some thing. An information content entity whose concretizations indicate to their bearer how to realize them in a process. plan specification A directive information entity with action specifications and objective specifications as parts, and that may be concretized as a realizable entity that, if realized, is realized in a process in which the bearer tries to achieve the objectives by taking the actions specified. measurement datum (an information/ data item that is a measurement) document A collection of information content entities intended to be understood together as a whole software planned process organization An entity that can bear roles, has members, and has a set of organization rules. Members of organizations are either organizations themselves or individual people. Members can bear specific organization member roles that are determined in the organization rules. The organization rules also determine how decisions are made on behalf of the organization by the organization members. IAO:0000118 "organisation"@en-AU organization # health care facility # health care provider role A role that inheres in a human being who is the object of clinical assessment, diagnosis, treatment, or care within a healthcare system. # human patient role Patient Role # health care provider organization A process that creates, tests, and implements artificial intelligence models to enhance analytics, prediction, and adaptive system intelligence. AI Developer Workforce Process A process that establishes oversight, transparency, risk controls, and ethical use guidelines for artificial intelligence systems. Artificial Intelligence in Health Care A Report From the National Academy of Medicine Health care delivery systems should have a robust and matureinformation technology (IT)governance strategy prior toembarking on substantial AI deployment and integration. AI Governance Process A process that enforces technical, ethical, fairness, and safety standards for developing, validating, deploying, and monitoring artificial intelligence systems in healthcare. Matheny, M.E., D. Whicher, and S. Thadaney Israni, Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA : the journal of the American Medical Association, 2020. 323(6): p. 509-510. Directives outlining safe, ethical, and effective adoption of artificial intelligence solutions in clinical and operational workflows. AI Implementation Standards Process A process that applies recognised accreditation requirements to ensure that health organisations meet defined safety, quality, governance, and performance criteria. Formal benchmarks and processes by which healthcare organizations are assessed and recognized for quality, safety, and compliance. Accreditation Standards Process Brooks, D., et al. (2017). "Developing a framework for integrating health equity into the learning health system." Learning health systems 1(3): n/a. Once the data are gathered and analyzed, strategies and interventions can be developed to address specific barriers to health equity. It is essential to develop an action plan that is targeted, prioritized, and initiated strategically. Patients, community members, and private and public organizations should be included to address immediate concerns, develop impactful actions, and determine thresholds for goal achievement. Throughout execution, data should be continuously collected and evaluated to monitor successes and/or barriers to effectiveness. In turn, the strategy should be updated and adjusted accordingly to account for any challenges or gaps in services. Interested organizations will need to establish a sustainability plan to continue to work with their patient, community, private, and public partnerships; create specific leadership roles and resource allocations; and develop policies and procedures to modify strategies when necessary Act On The Data A process that creates and maintains a flexible network of actors—individuals, teams, and institutions—who self-organise, share knowledge, and collaboratively address healthcare improvement goals. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. A collaborative approach in which multiple stakeholders (‘actors’) iteratively share knowledge, resources, and responsibilities, accelerating learning and adaptation within healthcare systems. Actor Oriented Learning Network Process A process that develops leadership capacity to respond flexibly to change and promote innovation within complex healthcare environments. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Leaders must be adaptable and open to innovation. This involves being receptive to new ideas, technologies, and practices that can enhance the Learning Health System and improve patient care. Adapability And Innovation Process A planned process in which a health system continuously reflects, learns, and adjusts its strategies, structures, technologies, and practices over time in response to changing conditions, emerging evidence, and evolving needs. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. It is essential to the long-term success of asystem. All of the other six domains are subject to change Adapting Over Time A process that investigates and acts on social, economic, and environmental determinants of health in partnership with community organisations to improve population health and equity. Rosenthal, G. E., et al. (2023). "The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers." Academic medicine 98(9): 1002-1007. Addresses social determinants of health and creates community partnerships to mitigate disparities and improve health equity In addition to medical education and research, AMCs can play important roles in advancing health equity. This imperative is particularly germane, given that AMCs care for a disproportionately large number of disadvantaged patients. Additionally, the drive to value-based purchasing (and the resulting financial incentives to control health care Addressing Social Determinants And Community Partnerships Process A process that collects, cleans, and aggregates clinical data across systems, departments, or institutions to enable population-level analysis, benchmarking, and performance monitoring. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Refers to systematically integrating and sharing internal clinical data from various sources within the health system Aggregated Clinical Data Management Process A process that promotes cultural alignment by ensuring that incentives, motivations, and organisational priorities reinforce behaviours that create value for patients and the health system. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Financial, recognition, or policy-based motivators designed to promote high-quality, cost-effective care by rewarding measurable improvements in outcomes and efficiency. Aligning Incentives For Value A planning process that designs and implements financial and non-financial incentive structures to promote high-quality, equitable, and cost-effective care outcomes across the health system. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Designing and implementing financial or structural incentives (e.g., performance-based payments, rewards for quality) that encourage healthcare providers and organizations to prioritize safe, effective, and cost-efficient care. Aligning Incentives With Highvalue Care A process that ensures strategic objectives and operational policies are consistent with organisational governance frameworks, ethical standards, and system-level priorities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Developing policies and governance structures that support the principles of a Learning Health System. This includes establishing standards for data use, ensuring regulatory compliance, and fostering a culture of accountability and transparency. Aligning Policy And Governance A process that uses data analytics, modelling, and system metrics to guide change decisions, evaluate interventions, and provide evidence for adaptive learning. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Systematic methods employing data measurement, monitoring, and interpretation to identify opportunities, guide decision-making, and evaluate the impact of change initiatives Analytic Approaches A process that applies statistical, computational, and AI methods to interpret health data, generate insights, and inform learning and improvement activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The ability to analyze large datasets using advanced analytical tools and techniques, including machine learning and artificial intelligence, is crucial. These capabilities enable the extraction of meaningful insights, prediction of outcomes, and identification of best practices. Daa Analytics Process A process that uses data analytics, modelling, and system metrics to guide change decisions, evaluate interventions, and provide evidence for adaptive learning. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Guidance is available on appraising whether or not to build a Learning Health System, but, in practice, the decision can be reached by a multitude of formal and informal means. It might be driven by top-down directive or bottom-up interest. Pilots can be useful in testing hypothesised impacts, Processes for assessing the effectiveness, outcomes, and value of change efforts, using structured metrics or frameworks to inform continuous improvement Appraisal and Evaluation Process A process that designs, implements, and reinforces behavioural interventions to promote desired practices and reduce resistance to change among individuals and teams. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. A successful Learning Health System will require individuals and organisations to change what they are doing. This often requires purposeful intervention. There are many models that can be employed to guide this process.These often involve understanding what drives a given behaviour and then selecting techniques that can influence those drivers. Techniques and interventions designed to alter individual or group behaviors, typically involving motivation, reinforcement, or habit formation to achieve desired outcomes Behaviour Change Process These represent processes that generate specific categories of benefit. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. A growing body of evidence highlights the potential benefits of patient-centered care for clinical outcomes, health, satisfaction among health care workers, and providers’ financial performance. In other frameworks, the "Benefits" is also called "Rationale". Benefits A process that establishes and applies standards promoting equitable, culturally safe, and inclusive care, research, and data use across diverse populations. Guidelines ensuring fair, non-discriminatory treatment of all patients and staff, addressing cultural competence, accessible design, and equitable resource distribution Best Priactice For Inclusivity And Equity Process A process that identifies, appraises, and applies the best available scientific evidence to support shared clinical decision-making and optimise patient outcomes. Best scientific evidence refers to current, high-quality research evidence derived from systematic reviews, clinical practice guidelines, and well-conducted scientific studies. Within a Learning Health System, evidence is continuously generated, evaluated, and integrated into care to support evidence-based practice and shared decision-making alongside clinician expertise and patient preferences. Best Scientifc Evidence A process that detects and reduces systematic errors or distortions in data that may affect fairness, accuracy, or validity of learning activities. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Data bias refers to systematic and prejudiced errors in data collection, analysis, or interpretation that result in skewed or inaccurate outcomes, often leading to unfair or unrepresentative conclusions. WHS hinges on PRO measures empowering patients to reflect on their physical, emotional, and spiritual well-being to individuals to make informed treatment choices and communicate their preferences. These outcomes aid in assessing treatment effectiveness, enhancing quality of life, and driving research, while ensuring integrative health aligns with patient needs and values at a systems level. In fact, they contribute tremendously to healthcare system transformation and enhancing the value proposition for investing in the WHS Bias Identification and Mitigation Process A process that manages and analyses large-scale, high-volume, and high-velocity health data to identify trends, patterns, and predictive insights supporting decision-making and system improvement. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Refers to the deployment of computational technologies and informatics approaches that organize and leverage large electronic health data sets—commonly known as “big data.” These data sets are used for research and quality improvement at the point of care for each patient, incorporating new knowledge generated by research1. In essence, big data analysis enables healthcare systems to harness vast amounts of data to drive evidence-based decision-making and continuous improvement. Big Data Utilisation Process A governance-level leadership process in which organisational boards set long-term vision, ensure accountability, endorse strategic priorities, and oversee safety, quality, and value creation across the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The role of the board in setting the strategic vision and ensuring that the organization adheres to its mission of continuous learning and improvement. The board is responsible for governance and oversight, ensuring the necessary resources and policies are in place. Board Leadership Process Reduces the impact of psychological stressors and external risks through supportive interventions. Cultivating protective factors helps mitigate mental health challenges3. Buffer Against Mental Health Risks A process that develops future Learning Health System leaders, researchers, and practitioners through structured training, mentoring, and professional development pathways. Rosenthal, G. E., et al. (2023). "The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers." Academic medicine 98(9): 1002-1007. A high-functioning LHS requires individuals with the ability to develop and rigorously evaluate health care innovations in the complex environments of real-world clinical systems. Thus, an aLHS has an important role to play in training LHS experts. Realizing the full potential impact of the LHS to improve health care delivery equires LHS investigators as well as many clinicians and staff who are fluent in core LHS principles and practices and who can serve as “connectors” to investigators. This is particularly true with regard to the utilization of electronic health record (EHR) data to identify opportunities for quality improvement (including an understanding of the many potential limitations and methodological biases associated with the application of EHR data25) and the development and operationalization of successful strategies for improving care that are built around the basic tenets of implementation science.4 Thus, an important role for the aLHS is to develop focused curricula and training programs for health care practitioners and staff in such areas Building LHS Expertise Pipeline Process A research and evaluation process that designs studies, analyses data, and converts tacit or local learnings into generalisable evidence (e.g., guidelines, standards, publications). Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The ability of an organization to produce or support research and data analysis, translating findings into robust evidence for continuous healthcare improvement. Building New Knowledge Into Evidence A process that establishes enabling legislation, regulations, and organisational policies that facilitate data sharing, research translation, and innovation within the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Developing regulations, guidelines, and policies at local, state, or organizational levels that facilitate continuous improvement, data sharing, and collaborative practices within a Learning Health System Buildinga Supportive Policy Environment A process that identifies and resolves limitations in storage, fidelity, availability, and processing capacity that constrain data-driven learning. Capacity challenges relate to the system’s ability to store, process, and manage data. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Data capacity refers to the availability and suitability of data, as well as the maximum amount of data that a storage system, database, or technology infrastructure can effectively handle, store, or process within a specified timeframe or resource constraint Data Capacity Change Management Process A process that ensures continuity and coordination of healthcare services across all stages of care — from prevention and diagnosis to treatment, rehabilitation, and palliative care — enabling seamless patient journeys. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A concept describing the seamless and continuous management of a patient’s healthcare needs across various providers and settings, ensuring coordinated care transitions, consistent communication among providers, comprehensive care planning, and ongoing monitoring and follow-ups. Care Continuum A planned process that designs, implements and reviews models of care and organisational strategies that embed learning into routine service delivery. Combine Model of Care, Organisations and Strategies into the top calss as one of seven supporting pillars. Care Delivery And Organisational Strategies Process A process that defines and operationalises how healthcare services are delivered to patients and communities through integrated, evidence-informed, and patient-centred models of care. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Emphasizes how healthcare services are organized and carried out. Care Delivery Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A process that applies clinical and operational standards defining expected levels of care quality, safety, and performance. Established protocols defining the minimum acceptable quality and approach for patient treatment, including evidence-based practices. Care Standards Process A process that enforces standards ensuring safe, coordinated, and consistent transitions of care across providers, sectors, and settings. Established protocols defining the minimum acceptable quality and approach for patient treatment, including evidence-based practices. Care Transition Standards Process A process in which formal or informal caregivers (excluding patients themselves) participate in care planning, shared decision-making, feedback activities, and system learning initiatives. Caregiver Engagement Process A process of using data insights to inform and modify healthcare practices, policies, and interventions. McDonald, P. L., et al. (2024). "Data to knowledge to improvement: creating the learning health system." BMJ 384: e076175-e076175. Paige McDonald and colleagues detail key domains, tools, and actions required to enact learning health systems for continuous intelligent improvement in healthcare Refer to the process of using data insights to inform and modify healthcare practices, policies, and interventions. Purpose: LHSs continuously analyze data to identify areas for improvement, adapt strategies, and implement evidence-based changes in real time. Change Decisions and Actions A structured process that applies evidence-based frameworks and methodologies (e.g., Kotter’s model, ADKAR, PDSA) to plan, execute, and evaluate organisational change within healthcare contexts. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Continuous improvement within a Learning Health System requires change. The earlier report, workshops and the literature, suggest a number of areas in which strategic approaches to change are required Change Approaches A process that builds capabilities in planning, communicating, and embedding change across individuals and teams. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Competence in managing and leading change within healthcare organizations. This includes understanding the principles of organizational behavior, motivating staff, and fostering a culture that embraces innovation and continuous learning. Change Management Skill Development Process A specification that describes how a Learning Health System applies continuous learning cycles to manage chronic conditions such as diabetes, heart failure, or COPD through longitudinal data, monitoring, feedback, and personalised interventions. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Learning health systems focused on the continuous improvement of chronic disease management through regular monitoring, data collection, and the implementation of best practices. Chronic Disease Management Use-Case Clinical communication tools are digital or technology-enabled systems that support timely, structured, and reliable communication between healthcare professionals, patients, carers, and care teams. Within a Learning Health System, these tools facilitate coordination of care, information sharing, patient engagement, and feedback loops that support continuous learning and improvement. Clinical Communication Tools A process that collects, validates, structures, and maintains patient clinical information—including diagnoses, medications, procedures, vital signs, and care notes—to support clinical care, research, and analytics. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good identifies the transformational prospects for large interoperable linical and administrative data sets to allow real-time discovery on issues ranging from disease etiology to personalized diagnosis and treatment. It also explores the key priorities for data stewardship if clinical data are to be a carefully nurtured resource for continuous learning and better care (IOM, 2011a). Clinical Data Process A process that prepares, curates, and maintains the data elements required by clinical decision support tools, algorithms, and rules. Decision support tools and knowledge management systems should be routine features of health care delivery to ensure that decisions made by clinicians and patients are informed by current best evidence. Accelerate integration of the best clinical knowledgeinto care decisions.Decision support tools and knowledge m outine features of health care delivery to ensure that decisions made by clinicians and patients are informed by current best evidence. Clinical Decision Support Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press CDSS are tools that provide healthcare providers with knowledge and patient-specific information to enhance decision-making at the point of care. They integrate clinical guidelines, research evidence, and patient data to offer recommendations, alerts, and reminders to clinicians. Clinical Decision Support Systems A process that develops, disseminates, and implements guidelines based on high-quality evidence to support standardised and effective healthcare practice. Recommendations derived from systematic reviews of scientific research, informing clinical decision-making to optimize patient outcomes. Clinical Evidence Based Guidelines Process A process that ensures the quality, safety, and continuous improvement of clinical care through structured oversight, accountability, and monitoring. Focused on maintaining and improving clinical quality and safety Clinical Governance Process A process that trains healthcare professionals to integrate informatics principles into clinical care, analytics, and decision support. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Kalra, A., et al. (2017). "Cultivating Skills for Success in Learning Health Systems: Learning to Lead." Journal of the American College of Cardiology 70(19): 2450-2454. Competencies Demonstrate the ability to use data derived from electronic health records and other clinical information sources for research and quality improvement. Demonstrate knowledge about additional data sources that can be linked to health system clinical data in order to augment exposure and outcome ascertainment. Demonstrate the ability to assess data quality and apply data quality assurance processes, including error prevention, data cleaning, data monitoring, documentation, and relevant data standards. Demonstrate knowledge of population health informatics, including disease surveillance, monitoring of community health, assessment of social and behavioral determinants of health, and geographic information systems. Demonstrate knowledge of clinical information systems, including electronic health records, clinical documentation, computerized physician order entry (CPOE), clinical decision support systems, electronic prescribing, medical imaging, and clinical/population dashboards. Core Competencies for Learning Health System (LHS) Investigators Curriculum Basics of clinical databases: storing and retrieving data, types of data, data mining, data security The design or architecture of health information systems The usability of an electronic health record Clinical decision support: improving quality and safety Global eHealth trends and technologies: definitions, standards, impact, implications for research Patient portals How can we achieve an LHS? A view from the Office of the National Coordinator—the intersection of health information technology and policy Essential requirements of electronic health records and responsibilities of clinicians who use them Essential requirements of a comprehensive health information technology evaluation and monitoring function at the local or national level Exchanging clinical data electronically: progress and remaining challenges Electronic health record adoption and effective use: what can we learn from other countries and industries Integrating new data sources to improve population health Using informatics to study and improve chronic disease Building the App Store for health Familiarity with health information technologies, including electronic health records (EHRs), health information exchanges (HIEs), and clinical decision support systems (CDSS). This also involves understanding interoperability standards and ensuring data quality and integrity. Clinical Informatics Skillsets Development Process A process in which clinicians lead improvement, advocate for evidence-informed care, influence culture change, and guide clinical governance and safety within the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Clinical leaders, including physicians, nurses, and other healthcare professionals, are essential for driving quality improvement initiatives. They are often at the forefront of implementing evidence-based practices and engaging in direct patient care. Clinical leaders advocate for patient-centered approaches and contribute to the development of clinical guidelines and protocols. Clinical Leadership Process A specification that outlines how a Learning Health System supports clinical trials through integrated data capture, adaptive trial design, interim analysis, patient recruitment support, and feedback of trial findings into practice. P.Friedman, C. (2010). Achieving a Nationwide Learning System. Science Translational Medicine. 2. An institution that is planninga clinical trial for a new drug to be tested in a specific class of patients wishes to know whether a sufficient number of such patients exists to support the trial as designed. This institution places a query to the learning system: “How many patients who meet these specific eligibility criteria does your institution have?” All members of the learning system would receive the query, and many would reply with an answer expressed as a numerator (the number of patients who fit the criteria) and possibly a denominator (the total number of patients evaluated) as well. This allows the institution that is planning the study to determine whether the proposed sample size is feasible and to develop an appropriately designed strategy for patient recruitment. The integration of clinical trials into routine care to continuously generate evidence about the effectiveness and safety of treatments. This approach enables real-time learning and application of new knowledge. Clinical Trial Use-Case A process that involves healthcare professionals delivering direct care while contributing clinical insights to improvement and research cycles. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press This includes physicians, nurses, and other healthcare providers who deliver direct patient care and play a critical role in implementing evidence-based practices and engaging in quality improvement activities. Clinician Workforce Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Cloud-based solutions offer scalable and flexible infrastructure for storing and processing health data. They facilitate collaboration, data sharing, and access to computational resources necessary for advanced analytics and machine learning. Cloud Computing A participatory process that engages patients, staff, and other stakeholders in jointly designing solutions, workflows, or systems to ensure relevance, usability, and ownership. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. The complexity of a Learning Health System can only be understood by involving the stakeholders in its ongoing development.Design principles have been developed, based on ISO 9241-210, the international standard in human-centred design for interactive systems. Libraries of open-source co-design tools are available.The choice of tools can be influenced by, the task, the type of knowledge sought and by the participants,but there is limited evidence on how to choose the most appropriate tools. It can be helpful to employ an overarching co-design framework Co-design Process A process that designs and conducts prospective or retrospective cohort studies to understand outcomes, risk factors, and treatment effects over time. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Cohort trials are a type of observational study that follows a group of participants over time, examining how specific factors (such as exposure to risk factors) affect their health outcomes. Cohort Trials Process A process that designs policies promoting shared work across organisations, sectors, and communities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Promoting collaboration among different stakeholders, including healthcare providers, researchers, policymakers, and patients. Policies should encourage partnerships that facilitate knowledge sharing and coordinated care efforts. Collaboration And Partnership A planned process that cultivates cooperative working relationships, shared goals, interprofessional collaboration, and collective problem-solving to enhance learning and performance in the organisation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Emphasizing cooperative efforts among various stakeholders, including clinicians, researchers, patients, and policymakers, to achieve shared goals. Promoting interdisciplinary collaboration and teamwork is essential. Leaders need to create a culture where different healthcare professionals work together seamlessly to provide integrated and patient-centered care. Collaboration And Teamwork Process A strategic coordination process that fosters partnerships and multidisciplinary collaboration across healthcare, academia, government, and community sectors to achieve shared learning goals. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Promoting collaboration and teamwork among healthcare providers, researchers, and other stakeholders. This strategy involves creating multidisciplinary teams that work together to solve complex healthcare problems. Collaborative Practices A process where teams jointly interpret data, insights, and experiences to understand problems and identify opportunities for improvement. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Collective Sense making A process that ensures leaders prioritise patient safety, quality improvement, and evidence-informed decision-making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Leaders need to prioritize quality and safety in healthcare delivery. This involves fostering a culture where continuous improvement is integral to daily operations and encouraging staff to engage in quality improvement initiatives. Commitment To Quality and Safety Process A curated set of information artefacts describing widely cited LHS frameworks used as reference models in SMARTLHS. Common Reference LHS Frameworks A process that builds the capability to communicate effectively with patients, consumers, clinicians, researchers, managers, and other stakeholders to support learning, engagement, evidence translation, and implementation within a Learning Health System. https://www.ahrq.gov/learning-health-systems/building-workforce.html Understanding Stakeholder Values and Mechanisms: LHS professionals need to demonstrate knowledge of the values and communication mechanisms used by various stakeholder groups involved in health system research1. This involves understanding the perspectives, needs, and priorities of different stakeholders, such as patients, clinicians, administrators, and policymakers. Translating and Disseminating Research Value: LHS workforce members should be able to translate, disseminate, and communicate the value proposition and business case for research to diverse health system stakeholders. Effective communication helps bridge the gap between research findings and practical implementation, ensuring that evidence informs decision-making and practice improvement. Communication Skills Development Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The typical definition of a community is a group located in a particular geographic area. However, communities that promote continuous learn-ing and improvement in health care go beyond geographic boundaries to include groups linked through culture, occupation, conditions based on a common workplace, prognosis, stage in the care process, intensity of care needed, and more. Community A planned process in which consumers and community partners are engaged to co-design, inform and evaluate LHS priorities, services and policies. Bruin, J., et al. (2023). "Conceptualizing learning health systems: A mapping review." Learning health systems 7(1): e10311-n/a. A planned process in which a health organisation engages consumers and the wider community to inform, co-design, or evaluate services and policies. Consumer and Community Engagement Process A process that delivers health interventions in community settings that reflect local needs, culture, and population characteristics. Initiatives or programs implemented within community settings to address specific health needs, often involving collaboration among local organizations, healthcare professionals, and patients to ensure culturally and contextually appropriate care. Community Based Interventions A process where multidisciplinary teams work within or alongside community structures to deliver personalised, culturally appropriate care. Multidisciplinary groups of healthcare providers, community workers, and sometimes patient representatives who collaborate to deliver care and services within local communities, prioritizing patient preferences and cultural sensitivity. Community Based Teams Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. Refers to a group of individuals who share cultural backgrounds, beliefs, practices, and traditions. In LHS, understanding cultural diversity is essential for providing culturally competent care and addressing health disparities1. Community By Culture Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. This community focuses on individuals affected by a specific health condition or disease. LHS aims to integrate data and evidence to improve care for specific patient populations, such as those with diabetes, cancer, or cardiovascular diseases2. Community By Disease Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. Geographically defined communities involve people living in a specific area, neighborhood, or region. LHS considers local health needs, environmental factors, and social determinants of health to tailor interventions3. Community By Geography Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. Occupational communities consist of individuals working in similar professions or industries. LHS recognizes occupational health risks, workplace wellness, and occupational health services4. Community By Occupation A process that forms formal partnerships between healthcare organisations and community clinical providers to extend coordinated care beyond the health system. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Formal or informal alliances with community-based clinics or clinical services to enhance patient access, coordinate care, and align treatment protocols for improved health outcomes Community Clinical Partnership Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Population-level programs or projects aimed at improving health outcomes and addressing specific health needs within a defined community or region. Community Health Initiatives A process that connects healthcare organisations with community resources, public health agencies, and social services to address broader determinants of health and support population wellbeing. Care Delivery and community-based organisations and agencies should partner with each other to develop cooperative strategies for the design, implementation and accountability of services. Establishing and maintaining connections between healthcare providers and community resources to support the health and well-being of patients. Community Links A process that links healthcare providers with community agencies (e.g., housing, education, welfare) to address broader determinants of health. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Cooperative initiatives and relationships established between healthcare providers and local community organizations (e.g., non-profits, volunteer groups) to address patient needs, social determinants of health, and holistic care Community Organisation Collaboration A process in which a defined community participates in shaping healthcare planning, service delivery, research activities, and system learning cycles. Communities may be defined by geography, culture, disease, occupation, or social identity. Community Engagement Process A culturally tailored process that engages individuals who share cultural, linguistic, or heritage backgrounds to contribute to co-design, communication strategies, health planning, and learning initiatives. Community_Engagement_by_Culture_Process A process that engages individuals connected by a shared disease or health condition in research prioritisation, co-design, peer support structures, and care improvement activities. Community_Engagement_by_Disease_Process A process that engages individuals who share a common workplace or profession in co-designing services, developing improvement initiatives, or participating in learning activities relevant to their occupational context. Community_Engagement_by_Geography_Process A process that engages individuals who share a common workplace or profession in co-designing services, developing improvement initiatives, or participating in learning activities relevant to their occupational context. Community Engagement by Occupation Process A process that compares performance, outcomes, or capabilities across departments, organisations, or systems to identify strengths, weaknesses, and opportunities for improvement. Internal Benchmarking: Comparisons within the organization to monitor progress over time. External Benchmarking: Comparisons with peer organizations or national standards to identify gaps and best practices. Comparative Benchmarking Process A reflective and adaptive process that recognises the non-linear, interdependent, and emergent nature of health systems, using systems-thinking and complexity science to guide strategic and operational change. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. This process supports the Learning Health System’s ability to evolve under uncertainty by enhancing organisational capacity for sense-making, resilience, interdependence awareness, and adaptive decision-making. It integrates Foley & Vale’s domains: adapting over time, the adopter system, the condition, the organisation, the technology, the value proposition, and the wider context. Complexity Management Process A process that regularly reviews organisational activities, data use, and research practices to ensure adherence to legal, regulatory, and ethical standards within the Learning Health System. Regulatory Checkpoints: Data tracking for compliance with laws and standards. Evidence of adherence to external audits and certifications. Ethics Reporting: Annual reports on ethical compliance and oversight activities. Outcomes of ethics-related reviews and investigations. Compliance Monitoring Process A process that develops a coordinated, multidisciplinary plan addressing a patient’s clinical, social, and behavioural needs across the entire continuum of care. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A collaborative process that identifies a patient’s medical, psychosocial, and functional needs, sets short- and long-term care goals, and develops an integrated strategy to meet those needs. Typically, this process involves the patient, their family or caregivers, and multiple healthcare professionals working together. Comprehensive Care Planning A process that ensures systematic capture of relevant clinical, operational, and contextual data from multiple sources to enable a holistic understanding of care delivery and outcomes. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Data encompasses a wide range of information, including clinical data (e.g., electronic health records, laboratory results), administrative data (e.g., billing information), patient-reported outcomes, and data from wearable devices and health applications. Comprehensive Collection Process A process that identifies, discloses, and mitigates conflicts of interest to ensure that decisions related to data, research, and system improvements remain impartial and trustworthy. Policies and procedures that identify, disclose, and mitigate potential conflicts among stakeholders to preserve objectivity and trust. Conflict Of Interest Management Proccess A process that manages, documents, and oversees patient consent preferences for the use, sharing, and secondary use of health data and learning activities. Consent Data: Consent forms and records of patient/family authorization for data use. Preferences for data sharing and withdrawal options. Dynamic Consent: Tracking systems for updated or revised consent in longitudinal studies. Consent Management Process A process that ensures timely, accurate, and structured communication among clinicians and care teams to support continuity and safety across settings. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A collaborative process that identifies a patient’s medical, psychosocial, and functional needs, sets short- and long-term care goals, and develops an integrated strategy to meet those needs. Typically, this process involves the patient, their family or caregivers, and multiple healthcare professionals working together. Consisitent Communications Among Healthcare Providers A process where subject matter experts or external advisors provide specialised guidance on strategic, clinical, or digital transformation initiatives. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Professionals who provide expert advice and guidance on various aspects of healthcare delivery, including process improvement, technology implementation, and strategic planning. Consultant Workforce Process Enticott, J., et al. (2020). "Leaders’ perspectives on learning health systems: a qualitative study." BMC Health Services Research 20(1). Patients is one pillar in SLHS. It refers to Patients, family, other care givers and the public. Consumer Role Further, participants considered the involvement of consumers as important in a LHS as they could make valued contributionto prioritising data, commenting on measures to be collected and analysed, and deciding time frames for data collection. A planned process in which real-time or routinely collected data are continuously captured, analysed, interpreted, and fed back into care delivery and decision-making to support rapid, ongoing learning within a Learning Health System. Bruin, J., et al. (2023). "Conceptualizing learning health systems: A mapping review." Learning health systems 7(1): e10311-n/a. Learning cycles can occur at various speeds and levels of scale but invariably consist of three core processes, namely converting data to knowledge (D2K), applying knowledge to influence performance (K2P), and3 documenting hanges in performance to generate new data (P2D).” Represents the “rapid-cycle learning loop” of an LHS, where high-frequency data streams (e.g., EHR data, operational data, quality metrics) drive incremental improvement and adaptive responses at the point of care. Continuous Data-Driven Learning Cycle A process that establishes and maintains mechanisms for timely, transparent feedback across teams and levels of the organisation to support learning, understanding, and performance improvement. Real-time or regularly collected input from patients, staff, and other stakeholders, used to make rapid adjustments and improvements in care delivery or organizational processes. Continuous Feedback A process that embeds ongoing, iterative improvement methods—such as Lean, Six Sigma, or PDSA—into routine organisational practice. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Continuous Improvement Processes Are Used A process that plans and supports ongoing generation, synthesis, and application of new knowledge through research, evaluation, and practice-based learning. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Developing systems that enable continuous learning from patient care experiences. This involves capturing and analyzing data from routine clinical practice to generate actionable knowledge and apply it in real-time to improve care. Continuous Knowledge Developement and Application A process that fosters ongoing reflection, experimentation, evaluation, and iterative adjustment of practices to strengthen individual and organisational learning capabilities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Continuous learning and improvement—The process of ongoing measurement and analysis to inform changes in the delivery of care. Continuous learning occurs both intra- and interinstitutionally and relies on the realtime capture and use of data on patient experience, outcomes, and process measures. Encouraging a constant pursuit of excellence, where feedback is sought, and processes are regularly evaluated and refined. Continuous Learning And Improvement A process that engages temporary or project-based professionals who contribute to defined learning, technical, or improvement tasks. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Individuals or organizations hired to perform specific tasks or projects, such as IT services, construction of healthcare facilities, or specialized clinical services. Contractor Workforce Process A process that develops integrated care plans involving multiple providers to ensure seamless, patient-centred care across settings. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Well-structured, collaborative plans developed across multiple providers or settings to align patient goals, treatments, and follow-up steps. Coordinated Care Plans A process that involves patients in coordinating their own care by connecting healthcare providers with community supports. The process of aligning and managing healthcare services and community-based resources to ensure patients receive continuous, tailored support that addresses both medical and social needs. Coordinating Care Through Health Care and Communities A process that manages the safe and effective transfer of patients between providers, teams, or care settings. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The structured management of patient transfers from one healthcare setting or level of care to another (e.g., from hospital to home or to a rehabilitation facility) to maintain continuity of treatment plans, medication safety, and overall patient well-being during periods of change. Coordination Of Care Transition A plan specification that declares system-wide processes that apply across all pillars of an LHS. The top class have Ethics and Oversight, Evaluation and Methodology, and Value Creation and Benefits which are overarching concepts cutting across multiple areas of LHS Framework. Cross Cutting Themes A process that identifies, cultivates, and reinforces the key attributes of a positive organisational culture—such as trust, collaboration, accountability, inclusiveness, and transparency—that support continuous learning and patient-centred improvement. Represents the “what” of organisational culture, describing the characteristics that foster learning and adaptability. Cultural Attributes A continuous organisational process that shapes shared values, beliefs, norms, and behaviours to promote learning, collaboration, transparency, and patient-centred improvement. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Culture refers to the collective attitudes, values, goals, and practices that characterize an organization or system. It's about how individuals within the system — including healthcare providers, researchers, administrators, and patients — think, behave, and interact with each other. Culture in an LHS is particularly important because it influences how readily the system can adapt, learn, and improve over time. Culture Process A planned process that shapes organisational culture and manages change to enable trust, transparency, psychological safety and continuous improvement. Combine Change Approaches, Complexity, Culture and Technical Building Blocks into one of seven supporting pillars for LHS framework. Culture And Change Management Process A process that evaluates cultural dimensions—such as readiness for change, psychological safety, leadership behaviour, and learning orientation—through surveys, interviews, and observational methods to inform improvement strategies. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. There are quantitative and narrative-based tools for assessingculture, but none is applicable in all situation Culture Assessment And Measurement A structured process that designs and implements targeted interventions (e.g., leadership programs, recognition systems, communication campaigns) to shift cultural norms and behaviours toward a learning and improvement mindset. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Key targets and tools have beenidentified, with a focus on: vision, values, goals, compassion, learning innovation, teamwork and collective leadership. Culture Change Interventions A process that defines and describes the distinctive cultural patterns, values, and dynamics within a healthcare organisation or system, serving as a baseline for learning and change initiatives. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Methods or frameworks used to describe, document, and interpret an organization’s cultural attributes, providing insights into prevailing mindsets, shared assumptions, and typical behaviors. Culture Characterisation A process that builds analytical literacy and technical proficiency for interpreting data to support learning and decision-making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Proficiency in analyzing and interpreting complex healthdata is essential. This includes understanding statistical methods, using data visualization tools, and applying advanced analytics techniques such as machine learning to derive actionable insights from healthcare data. Data Analytics Skillsets Development Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press The use of advanced data analytics and big data techniques is emphasized for extracting actionable insights from large volumes of health data. These technologies help identify patterns, predict outcomes, and inform evidence-based practices. Data Analytics And BigData A planned process that provisions, integrates, governs and operates data assets and technical platforms required to support learning cycles. A planned process that provisions, operates, and improves the organisation’s data assets, platforms, and technical infrastructure to support learning health system functions. Data and Technology Infrastructure Management Process A process that organises, interprets, and contextualises raw data into meaningful information that can support learning, analysis, and decision-making. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Data Are Translated Into Information A process that defines, monitors, and manages key data properties—such as quality, completeness, timeliness, privacy, and interoperability—to ensure that data is fit for purpose within the Learning Health System. Data Attributes Process A process that manages and governs the collection, use, and maintenance of different categories of data used within a Learning Health System. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Data Challenges Process Bailey-Davis, L.D., et al., Operationalizing the Learning Health Care System in an Integrated Delivery System. EGEMS (Washington, DC), 2015. 3(1): p. 1122-1122. T he deliverables component refers to the product or outcome of learning activities across levels of the system and draws attention to the need to embed learning across the system. Data Deliverables A process that promotes the use of data, evidence, and analytical insights as the basis for organisational decisions, reinforcing transparency and accountability. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Cultivating a mindset that prioritizes evidence and data over anecdotal experiences or traditional practices when making decisions. Data Driven Decision Making A process where leaders use data, analytics, and evidence to guide strategic and operational decisions. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Leaders in a Learning Health System leverage data to inform decision-making. This involves using health information technology to collect and analyze data, and applying insights from this data to drive improvements in care processes and patient outcomes. Data Driven Dicision Making Process A process that defines policies, responsibilities, and standards for managing, accessing, sharing, and protecting data assets. Ensures that data are managed securely and ethically, facilitating high-quality data usage for continuous improvement. Data Governance Process A process that governs data from creation and acquisition through storage, usage, sharing, archiving, and disposal, ensuring data quality, compliance, and reusability. Lessard, L., et al., Architectural frameworks: defining the structures for implementing learning health systems. Implementation science : IS, 2017. 12(1): p. 78-78. Another element within the technical dimension is data lifecycle management. The walk through a chain ofevidence from data collection to data transformation and consumption can be a challenging task, given the diversity of stakeholders involved in each step [44]. The ways in which data objects and the end-to-end data lifecycle are handled will likewise vary according to the selected technical architecture. Seminal LHS literature promotes a distributed approach to data management to ensure the infrastructure’s resilience [7]. In this perspective, data management can be distributed at different points during its lifecycle. In a federated system, for example, data repositories remain under the control and at the location of the institution producing the data, although data queries can originate from any participating institution, which then handles its own analysis [40, 45]. Alternatively, data repositories may be distributed but the results of data queries centralized (e.g., PaTH A fully centralized approach to data management may be preferred to better handle real-time requests (e.g., Peds-NET [38]) or advanced analytics (e.g., Optum Labsm[46]). Data Life Cycle Management Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A planned process that collects, organises, maintains, governs, and enables the effective use of clinical, operational, and research data to support learning cycles, decision-making, and improvement within a Learning Health System. Data within a Learning Health System refers to the comprehensive collection of health-related information enerated through routine clinical practice, patient interactions, and health research. This data forms the backbone of a continuously learning healthcare environment where real-time insights can be derived to improve care quality, efficiency, and patient outcomes. Data Management Process Olsen, L., D. Aisner, and J.M. McGinnis, The learning healthcare system workshop summary. 2007, Washington, DC: National Academies Press. These case examples illustrate the application of such designs to the quality and outcomes of care and demonstrate how these studies influence state and national health policies, including the Medicare drug benefit. A process that assesses and enforces safeguards protecting health data confidentiality, integrity, and responsible use throughout the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Policies must ensure the protection of patient data, maintaining privacy and security while allowing for the effective use and sharing of data for learning purposes. Privacy Indicators: De-identified and anonymized data statuses. Pseudonymization methods applied to datasets. Access Control Logs: Records of data access (who accessed, when, and why). Role-based permissions and authorizations. Breach Management: Logs of security incidents or data breaches. Response plans and resolutions to privacy violations Data Privacy And Security Process A process that defines, governs, and enforces the rules, metrics, and expectations required to ensure the accuracy, completeness, consistency, timeliness, and fitness-for-purpose of data within a Learning Health System. Includes standards for data validation, quality dimensions, quality indicators, and continuous data quality monitoring. Standards and criteria ensuring that healthcare data is accurate, complete, timely, consistent, and reliable for effective clinical and operational use.” Data Quality Standard Process A process that specifies the technical, semantic, legal, and ethical rules required to enable safe, interoperable, and authorised data exchange across systems, organisations, and users. Guidelines and protocols that enable the secure, authorized exchange of health information among stakeholders, promoting interoperability and coordinated care delivery Includes API standards, interoperability specifications (e.g., HL7 FHIR), and data-sharing agreements. Data Sharing Standard Process A process that identifies, selects, and gathers data from clinical, operational, research, and community sources to support evaluation, analytics, and learning activities. Data Inputs: Electronic health records (EHRs), patient registries, and administrative data. Patient-reported data and surveys. Real-time operational data and quality dashboards. Data Quality Assurance: Protocols for ensuring data accuracy, completeness, and timeliness. Methods for minimizing biases and addressing missing data. Data Sources And Collection Process A process that defines and applies data formats, terminologies, coding systems, semantic rules, and interoperability specifications to support accurate and consistent data exchange. Specifications and formats (e.g., HL7, FHIR) guaranteeing consistent, accurate data capture, exchange, and integration across systems Data Standards Process A process that establishes requirements for storing, maintaining, organising, and retaining data so that it remains accurate, secure, accessible, and usable over time. Enticott, J., et al., Leaders’ perspectives on learning health systems: a qualitative study. BMC Health Services Research, 2020. 20(1). Includes standards for data retention, archival formats, storage security, back-up policies, and metadata management. Practices and infrastructures for securely retaining health data, addressing accessibility, scalability, and durability requirements over a defined retention period. Data Storage Standard Process A process that transforms raw data into meaningful insights through analytics, interpretation, and synthesis, enabling evidence generation and knowledge creation. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Data is of little value unless it can be turned into knowledge. Knowledge is the insight produced by analysing data and interpreting the product of that analysis. This could include a guideline, predictive model, calculator, etc. Organisations often collect huge amounts of data but fail to turn it into generalisable knowledge.28 Within a Learning Health System, knowledge can be generated by many statistical and deliberative methods. Data to Knowledge A process that manages and governs the collection, use, and maintenance of different categories of data used within a Learning Health System. Data Type Process A process that reduces harm and preventable incidents through safety systems, monitoring, and quality improvement. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. refers to the reduction in preventable adverse events within healthcare systems Decreased Adverse Events A process that reduces resource waste and inefficiencies through improved workflows, decision-making, and care coordination. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Engaging patients in their care can lead to more efficient use of healthcare resources. For example, better management of chronic conditions can reduce the need for emergency care and hospital readmissions, thereby lowering overall healthcare costs . Decreased Operational Costs A process that identifies and interprets the implicit, taken-for-granted beliefs, values, and assumptions that underpin how staff think, act, and make decisions within the organisation. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. These assumptions exist below conscious awareness and strongly shape behaviour, learning capacity, and openness to change. Unconscious underpinnings of dailypractice. Deeper Shared Assumptions A process that applies human-centred design and systems thinking to optimise workflows, interfaces, and user experiences in healthcare transformation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Professionals involved in the design of healthcare facilities, processes, and systems. This includes architects, process engineers, and user experience designers who focus on creating environments that support efficient and effective care delivery. Designer Workforce Process A process that communicates research findings, insights, and lessons learned to researchers, clinicians, and stakeholders through publications, presentations, networks, and digital platforms. McDonald, P. L., et al. (2024). "Data to knowledge to improvement: creating the learning health system." BMJ 384: e076175-e076175. Paige McDonald and colleagues detail key domains, tools, and actions required to enact learning health systems for continuous intelligent improvement in healthcare Rosenthal, G. E., et al. (2023). "The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers." Academic medicine 98(9): 1002-1007. Disseminates knowledge broadly to the research community to advance the evidence for clinical practice and health system science methods A core attribute of the LHS is the generation of knowledge to improve health care delivery locally.2 The aLHS extends this attribute to produce generalizable knowledge to improve care more broadly and to advance the methodological disciplines (e.g.,mplementation science, clinical informatics) that underlie health care improvement. The primary approach is through peer-reviewed publications and presentations to scientific communities. An important element in this aLHS function is aligning the professional goals of investigators with the improvement goals of health systems. One strategy is the creation of embedded researchers who build sustained relationships with clinical teams and solve health system problems.Ideally, external funding agencies and internal health systems support this work. The duality of funding enables aLHS investigators to maintain dual professional identities— first, as teammates connected to the priorities, constraints, and incentives of the health system, and second, as researchers connected to broader scientific communities that are focused on producing generalizable knowledge about improving health care delivery and advancing research methods. Developing knowledge that transcends the immediate needs of individual health systems— and that can then apply to other health systems—represents a key distinguishing feature of the aLHS. Knowledge dissemination involves sharing research findings, evidence, and best practices with relevant stakeholders. Purpose: LHSs disseminate knowledge to clinicians, administrators, policymakers, and patients, ensuring that evidence informs decision-making and drives positive outcomes. Disseminating Knowledge To Research Community Process A process that identifies risk factors or early signs of disease through screening, monitoring, or predictive analytics. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Strategies and initiatives aimed at identifying health issues in their initial stages—before symptoms become severe—to enable timely interventions and improve patient outcomes Early Detection A process that evaluates the costs, cost-effectiveness, value, and economic impact of interventions, models of care, or system changes. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Economic analysis applies economic theories and methods to understand how society makes decisions related to health behaviors, healthcare use, and health resource allocation. Economic Analysis Process A process that defines requirements and expectations for ongoing workforce education and capability development. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Supporting ongoing education and training for healthcare professionals to ensure they are equipped with the skills and knowledge needed to participate in a Learning Health System. This includes training in data analytics, quality improvement methods, and patient-centered care practices. Education And Training Process An organisation whose primary function is to provide education, training, and academic support to students, clinicians, and researchers participating in Learning Health System activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Universities and medical schools that provide training and education for healthcare professionals, fostering a culture of continuous learning and research in healthcare practices. Educational Institution Avoiding adverse events minimizes unnecessary costs and resource utilization. Efficient Resource Utilisation Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press EHRs are crucial for capturing detailed patient data, facilitating the storage, retrieval, and analysis of health information. They provide the foundation for integrating clinical data across various healthcare settings, enabling better coordination of care and supporting clinical decision-making. Electronic Health Records Routine, method-based improvement processes (e.g., PDSA, Lean, Six Sigma, clinical audit) integrated into day-to-day operations to reduce variation, improve outcomes, and standardise best practice. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The systematic integration of quality improvement (QI) methods, tools, and principles into the day-to-day operations of an organization. This ensures that QI is not an occasional project but an ongoing, organization-wide practice aiming for continuous enhancement of care. Embedded Quality Improvement Processes A process where new ideas, practices, or insights arise organically through adaptive experimentation and collaboration. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Emergent Learning refers to the process of learning that arises naturally from ongoing activities and interactions within the healthcare system. Emergent Learning A process that mandates adoption of interoperability, data standards, and digital architecture requirements across the organisation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Establishing and enforcing standards that enable different health information systems to communicate and share data seamlessly. This is crucial for integrating various data sources and facilitating comprehensive data analysis. Enforcing Interoperability Standards Process Brooks, D., et al. (2017). "Developing a framework for integrating health equity into the learning health system." Learning health systems 1(3): n/a. It is impossible to address and eliminate the health inequities of populations without engaging the very people affected: the community. Overcoming historical distrust of institutions requires buy‐in from and ownership of initiatives by the community.Deliberate and strategic actions are necessary to engage patients, families, community organizations, and health professionals across the system.Community members should be included in decision making, and governance capacities and their feedback sought to inform all initiatives. Community leaders (elected and lay) should be involved early in the process to understand the concerns of their constituents and to facilitate gaining the trust of the community. Additionally, structured mechanisms for incorporating their feedback should be in place. Strong partnerships with public officials and private organizations are essential, including service organizations that address SDH. Public partnerships may include public safety and/or transportation officials, Medicaid programs, school board members, and city council members. Private and/or service organizations may include faith‐based entities, local convenience stores and bodegas, schools, food pantries, and grocery stores. Leveraging these relationships will also contribute to the visibility and, in turn, the sustainability of the initiative. Innovative technology designed to support and engage underresourced communities is important for gathering data and me Engage The Community A process that cultivates skills in collaborative leadership, influencing, and motivating others toward continuous improvement. https://www.ahrq.gov/learning-health-systems/building-workforce.html Competencies Demonstrate the ability to build and lead research teams with diverse health system stakeholder representation. Demonstrate knowledge of the values and communication mechanisms used by stakeholder groups involved in research in health systems. Demonstrate the ability to translate, disseminate, and communicate the value proposition and business case for research to diverse health system stakeholders. Demonstrate the ability to conduct effective team-based project management, employing skills in leadership, communication, negotiation, consensus building, and problem-solving. Demonstrate the ability to develop protocols consistent with health systems needs and timelines, employing patient and clinician engagement, and using a mix of conventional and alternative funding sources. Demonstrate the ability to implement protocols aligned with health systems operations and integrated into clinical settings, including engaging clinicians in the research process. Demonstrate knowledge of participatory research approaches that foster participation and engagement of vulnerable populations. Core Competencies for Learning Health System (LHS) Investigators To engage stakeholders in all aspects of the research process and effectively lead and manage LHS research teams and projects. Engagement And Leadership Development Process A planning and design process that embeds the perspectives of patients, families, and caregivers into strategy formation, service design, and evaluation activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Strategies aimed at actively involving patients and their families in care decisions, system design, and improvement initiatives, ensuring that care is person-centered and values-driven. Engagement Of Patients and Families A process that conducts and connects all phases of translational research—from basic discovery to clinical implementation and population-level evaluation—within the Learning Health System. Rosenthal, G. E., et al. (2023). "The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers." Academic medicine 98(9): 1002-1007. Engages the full spectrum of translational investigation from mechanistic basic sciences to population health; The spectrum of translational investigation extends from basic and preclinical research (T0) to early-phase human trials and proof of concept studies (T1), clinical trials (T2), and beyond (T3–T4).19 The LHS model focuses on T3 (effectiveness of treatments and delivery innovations in real-world practice settings) and T4 (impacts of treatments and innovations on communities and populations).19 A distinguishing feature of the aLHS is its engagement of investigative expertise across the T0–T4 spectrum of translational investigation. Engaging The Full Spectrum Of Translational Investigation Process A process where leaders actively empower teams, encourage participation, and support shared ownership of improvement efforts. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Effective leaders engage and empower both healthcare professionals and patients. They create an environment where team members feel valued and are encouraged to contribute to the organization's learning and improvement processes. Leaders also engage patients as active participants in their care. Ensure that every patient can participate in the learning system Consider health and digital literacy as well as patient preference in all health and healthcare actions and decisions Engagement And Empowerment Process A process that increases the system’s ability to adjust rapidly to new information, challenges, or patient needs. Menear, M., et al., A framework for value-creating learning health systems. Health research policy and systems, 2019. 17(1): p. 79-79. The LHS will be designed to enable iterative, rapid adaptation and incremental evolution to meet the current and future needs of stakeholders Enhanced Adaptability Reduction in harm contributes to better overall care quality. Enhanced Quality of Care A process that improves staff skills, knowledge, performance, and confidence through continuous learning, feedback, and development opportunities within the Learning Health System. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Developing and reinforcing staff competencies through targeted training programs and performance support, ultimately improving care delivery and fostering professional growth. Enhancing Staff Education And Performance A process that investigates disease patterns, determinants, and distributions within populations to inform learning cycles and improvement actions. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Epidemiology studies the distribution and determinants of health-related events in specified populations. It explores causes, patterns, and impacts of diseases. Epideminology Based Research Process A process that develops awareness and strategies for equitable access, inclusion, and fairness within system learning. https://www.ahrq.gov/learning-health-systems/building-workforce.html Competencies Assess how existing health inequities affect population-level health, individual health, and healthcare delivery systems with methods grounded in an awareness of the multilevel factors contributing to such inequities. Apply methods to optimally engage diverse participants (including clinicians, staff, patients, community members, and caregivers) in LHS initiatives throughout each initiative, from priority-setting to design to implementation through evaluation. Demonstrate awareness of the value and limitations of sub-group analyses in LHS research and improvement activities to understand heterogeneity of health system effectiveness. Apply LHS knowledge and methods to translate, implement, and scale research innovations to advance equity and justice in health and healthcare delivery. Demonstrate awareness of measurement tools related to health equity at the population, community, healthcare practitioner, and individual patient and community member levels. To know how to assess health equity and apply LHS science methods to advance equity and justice in healthcare delivery systems and health. Equity And Justice Development Process A process that strengthens fairness, justice, and ethical behaviour across the organisation, ensuring decisions and actions are inclusive, respectful, and aligned with ethical standards. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Upholding high ethical standards and striving for equity in healthcare delivery and research, ensuring that all patients receive fair and respectful treatment. Ethical And Equitable Practices A process that creates policies promoting fair, ethical, and lawful practice within the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Establishing ethical and legal frameworks that guide the conduct of research, data use, and patient care within a Learning Health System. This ensures that all activities are conducted with integrity and respect for patient rights. Ethical And Legal Framework Process A process that ensures evaluation activities uphold ethical principles, protect participants, and minimise harm, bias, or unintended consequences. nformed Consent for Evaluation Studies: Ensuring that patients and stakeholders are aware of how data will be used for evaluation. Equity and Justice: Evaluating whether interventions disproportionately benefit or harm specific groups. Ethical Considerations In Evaluation Process A process that establishes ethical principles, policies, and decision-making mechanisms guiding responsible data use, research, and learning activities. Policies and Guidelines: Ethical frameworks governing data use, sharing, and management. Standards for consent and privacy protection (e.g., GDPR, HIPAA compliance). Conflict of interest policies for stakeholders. Decision-Making Records: Documentation of ethical approvals and rejections (e.g., IRB or HREC decisions). Meeting minutes of ethics committees and oversight boards. Ethical Governance Process A process that promotes ethical behaviours, fairness, and integrity in leadership practices and decision-making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Upholding ethical standards and ensuring that patient privacy and confidentiality are protected is a fundamental responsibility of leaders in a Learning Health System. Ethical Leadership Process A process that reviews proposed research, data use, and interventions to ensure ethical compliance and protect participants. Menear, M., et al., A framework for value-creating learning health systems. Health research policy and systems, 2019. 17(1): p. 79-79. Ethical Review Boards and Committees play a crucial role in ensuring the ethical conduct of research involving human participants. Ethical Review Boards (ERBs): Also known as Institutional Review Boards (IRBs), ERBs are formally constituted committees responsible for reviewing and approving research protocols. Their primary focus is to protect the rights, welfare, and dignity of research participants. ERBs assess the scientific validity, ethical considerations, and risk-benefit balance of proposed research studies. They ensure compliance with ethical guidelines, national regulations, and institutional policies Purpose of Ethical Review Boards and Committees: Scientific Soundness: ERBs evaluate the scientific rigor and validity of research proposals. Participant Protection: They ensure that research respects participants’ autonomy, privacy, and safety. Ethical Principles: ERBs uphold ethical principles such as beneficence, justice, and informed consent. Oversight and Monitoring: ERBs provide ongoing oversight during the research process. Risk Assessment: They assess potential risks and benefits to participants and the broader community. Ethical Review Board And Commitees Process A process that identifies, evaluates, and mitigates ethical risks associated with data use, digital tools, machine learning, and learning interventions. Risk Documentation: Identification of ethical risks (e.g., data misuse, bias in AI algorithms). Mitigation plans for identified risks. Bias and Fairness Metrics: Assessment of bias in datasets, particularly for underrepresented populations. Impact analyses for ensuring equitable outcomes. Ethical Risk Assessment Process A planned process that assures ethical conduct, fairness, transparency and accountable oversight for LHS data use, learning activities and interventions. When considering Ethics and Oversight as a cross-cutting theme in a Learning Health System (LHS), the key data elements can be grouped under categories that ensure ethical integrity, transparency, accountability, and compliance. These data elements enable robust governance of ethical practices and oversight mechanisms within the system Ethics And Oversight Process A process that educates workforce members in ethical decision-making related to research, data use, and change implementation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press https://www.ahrq.gov/learning-health-systems/building-workforce.html Awareness of ethical and legal considerations related data use, patient privacy, and healthcare delivery. This involves understanding regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and ensuring compliance with ethical standards in research and practice. Competencies Demonstrate the ability to apply ethical principles in the engagement of health systems, including issues of business ethics and the importance of publishing both positive and negative findings in the public domain. Demonstrate knowledge of what activities constitute research as opposed to quality improvement activities, and seek appropriate oversight for each. Demonstrate knowledge of specific Health Insurance Portability and Accountability Act (HIPAA) requirements associated with varied data sources used in health systems research activities and seek appropriate approvals. Demonstrate the ability to identify and minimize potential conflicts of interest in the design, conduct, and reporting of research conducted in health systems. Demonstrate knowledge of ethical and legal considerations when engaging in multi-system studies for compliant collaboration and study conduct. Core Competencies for Learning Health System (LHS) Investigators Ethics In Research And Implementation Training Process A planned process that designs and runs methodological and measurement activities to assess the effectiveness, safety and equity of LHS initiatives. Bailey-Davis, L.D., et al., Operationalizing the Learning Health Care System in an Integrated Delivery System. EGEMS (Washington, DC), 2015. 3(1): p. 1122-1122. Activities and methodological approaches needed to identify, implement, measure and disseminate learning initiatives. The Evaluation and Methodology component of a Learning Health System (LHS) framework ensures that the system's processes, interventions, and outcomes are systematically assessed to support continuous improvement and innovation. Evaluation And Methodology Process A process that designs structured frameworks for evaluating programmes, interventions, or system-level learning activities. Goals and Objectives: Clearly defined goals for what the evaluation seeks to measure (e.g., efficiency, effectiveness, equity). Alignment with the system’s broader objectives and stakeholder priorities. Evaluation Scope: Determining the scope, including the populations, interventions, and timeframes being evaluated. Evaluation Framework Development Process A process that measures the economic, social, clinical, and operational value generated by learning cycles, interventions, or system improvements. Cost-Effectiveness Analysis: Assessing the economic impact of interventions relative to their benefits. Return on Investment (ROI): Measuring financial and non-financial returns for stakeholders. Evaluation of Value Creation Process A process that applies qualitative, quantitative, and mixed-methods tools—such as surveys, modelling, audits, and qualitative inquiry—to assess system performance and improvement. Evaluation Tools: Logic models and theory of change frameworks to map causal pathways. Balanced scorecards for tracking multiple dimensions of performance. Visualization Techniques: Dashboards and heatmaps for real-time insight into evaluation metrics. Evaluation Tools And Techniques Process A senior leadership process that aligns organisational strategy, resources, culture, and operations to support continuous learning, digital transformation, and system-wide improvement. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Executives are tasked with operationalizing the vision set by the board. They are responsible for leading the organization’s strategic initiatives, fostering a culture of learning, and ensuring that systems and processes support continuous improvement. This includes senior leaders such as the CEO, CFO, and other top executives. Executive Leadership Process A process that operationalises FAIR (Findable, Accessible, Interoperable, Reusable) principles to enhance data discoverability, usability, and collaboration across systems. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. FAIR Data Management Process A role borne by a person who is related to a patient through familial or household relationships and participates in supporting care, providing input, making shared decisions, or contributing to organisational learning activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Relatives of patients who may be involved in the care process, providing support and making decisions alongside the patient. Family Role A planned process that involves family members in supporting patient care, participating in decisions, contributing feedback, and engaging in improvement and learning activities at individual or organisational levels. Includes family advisory councils, bedside rounding participation, and shared-care models. Family Engagement Process A process that shortens recovery time by applying evidence-based interventions and personalised care. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. refers to the accelerated healing and recuperation experienced by patients. When LHS principles are applied effectively, patients experience quicker recovery times after illness, surgery, or medical interventions. Factors contributing to faster recovery include evidence-based practices, streamlined care pathways, and personalized treatment plans. LHS facilitates continuous learning and adaptation, leading to improved patient outcomes and reduced hospital stays. Faster Recovery A process that operationalises federally mandated regulations that govern safety, data use, privacy, research conduct, technology deployment, and organisational compliance. Mandates issued by national authorities that define permissible behaviors, obligations, and oversight in healthcare delivery and management Federal Regulations Process A process that designs and maintains mechanisms for continuous data feedback from outcomes and operations into learning, planning, and practice improvement activities. McDonald, P. L., et al. (2024). "Data to knowledge to improvement: creating the learning health system." BMJ 384: e076175-e076175. Paige McDonald and colleagues detail key domains, tools, and actions required to enact learning health systems for continuous intelligent improvement in healthcare Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Data should facilitate continuous feedback loops where insights derived from data analysis are used to inform and improve clinical practice, patient care processes, and health outcomes. This cyclical process is a core component of a learning health system. Feedback Loops Development Process A process that designs, monitors, and refines feedback mechanisms that connect data → knowledge → practice to enable continuous learning and system adaptation. Real-Time Evaluation: Systems for ongoing monitoring and real-time feedback to stakeholders. Early warning systems for detecting issues or opportunities for improvement. Iterative Learning: Processes for integrating findings into practice for rapid-cycle improvement. Mechanisms for sharing lessons learned across teams and organizations. Feedback Loops And Learning Cycles Process Reduces behavioural dysregulation by addressing psychosocial and clinical contributors. Improved emotional health leads to fewer behavioral problems Fewer Externalizing Behaviors An organisational unit responsible for planning, allocating, and monitoring financial resources to support healthcare delivery, digital infrastructure, research, and learning activities within the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Responsible for managing the financial resources of healthcare organizations, ensuring that funding is allocated effectively to support continuous improvement and learning initiatives. Finance Department These describe how incentives support value and learning. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Structure payment to reward continuous learning andm improvement in the provision of best care at lower cost. Payers should structure payment models, contracting policies, and benefit designs to reward care that is effective and efficient and continuously learns and improves. Incentives Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Policies incentivize learning Policies incentivize research Policies incentivize translation Policies incentivize patient engagement A process that communicates research findings broadly through publications, presentations, digital tools, or stakeholder engagement to enable evidence uptake and reuse. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Findings From The Research Are Shared Or Disseminated A process that assesses, manages, and mitigates risks associated with data sharing and release by applying the Five Safes framework — Safe Projects, Safe People, Safe Data, Safe Settings, and Safe Outputs — to ensure that data use within a Learning Health System remains ethical, secure, and trustworthy. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. The Five Safes framework is an internationally recognized approach used in the context of Learning Health Systems (LHS) to assess and manage risks associated with data sharing and release. It considers strategic, privacy, security, ethical, and operational risks as part of a holistic assessment. Five Safes Process Foley LHS Complexity Foley LHS Building Blockes Emphasizing the importance of culture, the framework integrates a learning culture as a core element, recognizing that long-term transformation requires not only technological and procedural changes but also a shift in mindset and organizational culture. Foley LHS Cultural Integration Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Tom Foley's 2023 Learning Health System (LHS) framework presents a structured approach to enhancing healthcare systems by integrating learning directly into their operations. The main structure of this framework can be outlined as follows: Building Blocks: This includes essential components such as transforming practice to data, converting data to knowledge, and applying this knowledge back into practice. Each component is designed to ensure that the system continuously learns and improves. Complexity Management: The framework addresses the complexities inherent in healthcare systems, offering strategies to manage and navigate these challenges effectively. Strategic Approaches: It outlines strategic methods to implement changes within the health system, ensuring that the system is adaptive and responsive to the needs of patients and the evolving healthcare environment. Cultural Integration: Emphasizing the importance of culture, the framework integrates a learning culture as a core element, recognizing that long-term transformation requires not only technological and procedural changes but also a shift in mindset and organizational culture. Foley LHS Framework Foley LHS Rationale Foley LHS Strategic Approaches A process that produces new, transferable, and evidence-based knowledge that can be applied beyond the local setting to improve care and system performance. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Producing new insights and evidence that can be widely applied or adapted across different healthcare settings, thus accelerating improvements in population health Generating Generalisable Knowledge A performance-oriented process that sets explicit, measurable targets and uses data feedback loops to prioritise actions, monitor progress, and iterate interventions toward those goals. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. A structured method of organizational learning in which activities are aligned with clearly defined, measurable objectives (e.g., improving patient outcomes or workflow efficiencies). This enables targeted improvement efforts and progress tracking over time. Goal Driven Learning A process that establishes and operationalises foundational governance principles and types of governance. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Governance in the LHS framework refers to the overarching system of rules, practices, and processes by which an organization is directed and controlled. It encompasses the mechanisms through which an organization’s objectives are set, achieved, monitored, and maintained. Governance ensures that the right people are making the right decisions in the right way. It typically involves high-level strategic guidance and oversight, and can include various stakeholders like healthcare providers, researchers, and patient representatives. hospital boards play an important role in guiding the organization toward continuous learning and improvement.Under federal regulations and accreditation standards, hospital boards areaccountable for the quality of care provided by their organization (Belmont et al., 2011). They also are responsible not only for ensuring the organization’s financial health and reputation, but also for overseeing its executives and shaping the organization’s mission (Conway, 2008). Governance A planned process that sets direction, allocates accountability and ensures compliant, safe and effective operation of the LHS. A planned process that establishes direction, oversight, accountability, and leadership capability for safe, effective, and continuously improving care. Governance And Leadership Process A process that establishes and operationalises foundational governance principles—such as transparency, accountability, risk management, and ethical conduct—to guide system decision-making and oversight. Fundamental mechanisms and values guiding how authority is exercised, decisions are made, and accountability is maintained within an organization or system Governance Principles Process A process that implements governance models tailored to different system domains to ensure safe, ethical, and effective oversight. Governance Types Process A process involving organisational boards that provide strategic direction, executive oversight, and organisational accountability. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. These are responsible for setting strategic direction, ensuring organizational accountability, and overseeing the implementation of policies and practices that support continuous learning and improvement. Governing Boards Process An organisation that provides clinical services, patient care, and operational support, and participates in generating and applying evidence through LHS learning cycles. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. These include hospitals, clinics, and other entities that provide direct patient care. They implement evidence-based practices and foster environments that support continuous learning and quality improvement. Health Care Delivery Organisation Brooks, D., et al. (2017). "Developing a framework for integrating health equity into the learning health system." Learning health systems 1(3): n/a. Health Equity PETAL Framework Brooks, D., et al. (2017). "Developing a framework for integrating health equity into the learning health system." Learning health systems 1(3): n/a. Core LHS Culture and Values are clearnly aligned with health equity Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press HIEs allow for the sharing of health information across different healthcare organizations. They support interoperability and ensure that patient data is available where and when it is needed, improving care continuity and reducing redundancies. Health Information Exchanges A process that defines the legal, ethical, and organisational rules to protect individuals’ identifiable and sensitive health information throughout its lifecycle. Includes consent rules, de-identification standards, permitted uses, and regulatory compliance requirements. Policies and frameworks designed to protect patients’ personal health information from unauthorized access or disclosure, maintaining confidentiality and respecting individual consent Health Information Privacy Standard Process A process that defines the technical and organisational safeguards required to protect health information from unauthorised access, alteration, disclosure, or loss. Includes encryption standards, authentication requirements, security controls, incident response, and risk management. echnical and administrative safeguards that defend health data against breaches, cyber threats, and unauthorized use, ensuring integrity and availability of information. Health Information Security Standard Process A strategic process that plans, invests in, and governs digital health infrastructure and technologies to support interoperability, data analytics, and evidence-driven decision making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Utilizing health information technology to support the collection, analysis, and dissemination of data. This includes the implementation of electronic health records (EHRs) and other digital tools that facilitate the integration of clinical data and support decision-making. HealthInformation Technology An external or partner organisation that develops, supplies, or maintains digital health technologies, data systems, AI tools, or platforms required for Learning Health System operations. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Companies that develop and supply health IT solutions, such as electronic health records (EHRs) and clinical decision support systems, which are essential for data collection, analysis, and sharing in an LHS. Health Information Technology Vendor Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Initiatives or campaigns designed to encourage healthy behaviors, reduce risk factors, and promote overall well-being among individuals or populations. Health Promotion Activities A process that coordinates oversight across multiple organisations, networks, or regions to ensure system-level integration, safety, and performance. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. This involves broader health system oversight, including the integration of various healthcare organizations and services to ensure alignment with learning health system principles. This can include regional health authorities or integrated delivery systems. Health Systems Governance Process A process that trains staff in quality improvement methods (PDSA, Lean, Six Sigma) and performance measurement. Kalra, A., et al. (2017). "Cultivating Skills for Success in Learning Health Systems: Learning to Lead." Journal of the American College of Cardiology 70(19): 2450-2454. Curriculum: Frontline process improvement Total quality improvement planning Developing a hospital-based program to improve quality Hospital quality and performance measurement Population health management Standardized care assessment and management Ambulatory care coordination for high-risk patients Health care consumer quality: the patient perspective Creating and evaluating effective clinical decision support tools Health Care Quality Improvement Skills Development Process A process that establishes organisational policies ensuring AI systems are safe, fair, transparent, equitable, and inclusive. Matheny, M.E., D. Whicher, and S. Thadaney Israni, Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA : the journal of the American Medical Association, 2020. 323(6): p. 509-510. Policies guiding the fair, responsible, and bias-minimized development and deployment of AI technologies in healthcare. Healthcare AI Ethical Equitable Inclusive Policy Process A process that applies usability, accessibility, and design standards to ensure digital health tools are safe, efficient, intuitive, and supportive of clinicians and patients. Design principles and usability criteria ensuring that healthcare software and devices are intuitive, safe, and effective for end-users.” Human Computer Interaction Standards Process A process that administers vaccines and monitors uptake to prevent infectious diseases at population and individual levels. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The administration of vaccines or immunological agents to individuals or populations, providing protection against infectious diseases and reducing transmission risk. Immunisations A process that evaluates the feasibility, fidelity, acceptability, and scalability of new interventions or programmes within different organisational contexts. Feasibility Studies: Assessing the practicality of scaling interventions system-wide. Sustainability Metrics: Measures to determine long-term viability and integration of improvements. Implementation And Scalability Assessment Process A process that sets policy expectations for the structured, evidence-based implementation of new practices and technologies. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Defined steps and workflows that ensure smooth execution of policies, including planning, stakeholder engagement, and outcome tracking Implementation Processes Policy A process that applies standards and frameworks from implementation science to guide effective translation of evidence into practice. Systematic methods for introducing and evaluating evidence-based interventions in real-world healthcare settings Implementation Science Guidelines Process A process that applies systems thinking, workflow analysis, and design optimisation methods to improve organisational efficiency, safety, and adaptability. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Applying principles from other industries, such as system engineering, to healthcare. This involves streamlining processes, reducing inefficiencies, and improving the reliability and quality of care delivery. Implementing System Engineering Principles A process that enhances patient mental wellbeing through supportive care, communication, psychological safety, and behavioural health interventions. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. refers to enhancing individuals’ emotional well-being and resilience within healthcare systems. Improved Emotional Health A process that increases patient understanding of their health, treatment options, and system navigation through education and communication. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Patient-centered approaches improve health literacy by providing patients with the information and support they need to understand their health conditions and treatment options. This empowers patients to make informed decisions about their care . Improved Health Literacy Fewer preventable adverse events lead to safer care for patients. Improved Patient Safety A process that empowers patients to better manage their own health behaviours, risks, and early warning signs. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Patients who are actively involved in their care decisions are more likely to adhere to prescribed treatments. This can lead to better health outcomes and fewer complications, further reducing the burden on healthcare systems . Improved Personal Health Maintenance A process that enhances trust, communication, and collaboration between providers and patients. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Patient-centered care fosters stronger relationships between healthcare providers and patients. Trust and communication are enhanced, leading to more effective care and increased patient loyalty . Improved Provider Patient Relationships A process that develops expertise in applying implementation frameworks, evaluation, and evidence translation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press https://www.ahrq.gov/learning-health-systems/building-workforce.html Competencies Demonstrate the ability to employ specific quality improvement methods to reduce avoidable variation in clinical processes and outcomes in routine practice. Demonstrate the ability to employ specific implementation science or quality improvement methods to study and promote systematic uptake of research findings and other effective clinical interventions into routine practice. Demonstrate knowledge regarding when to mount larger efforts to scale up, spread, and sustain successful interventions based on strength of clinical evidence and organizational and provider readiness to change and adopt interventions. Core Competencies for Learning Health System (LHS) Investigators Knowledge of quality improvement methodologies, such as Lean, Six Sigma, and Plan-Do-Study-Act (PDSA) cycles. These methods help healthcare professionals identify inefficiencies, implement changes, and measure the impact of those changes to improve care processes and outcomes. Improvement And Implementation Science Capability Process A process that enhances patient health outcomes, safety, and satisfaction through evidence-informed care, feedback loops, and patient-centred improvements. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Enhancing clinical results (e.g., reduced morbidity or mortality) and patient satisfaction by employing evidence-based practices, continuous feedback, and patient engagement strategies. Improving Patient Outcomes And Experience A process that ensures diverse perspectives are valued and actively incorporated into decision-making, fostering a culture where all voices contribute to learning and improvement. Menear, M., et al., A framework for value-creating learning health systems. Health research policy and systems, 2019. 17(1): p. 79-79. Every individual and organisation committed to the goals of the LHS is invited and encouraged to participate Inclusiveness A process that addresses inconsistent, missing, or poorly structured data categories that hinder analysis, interoperability, and interpretation. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Incomplete and variable categorization of data on health factors and social determinants of health can hinder care outcomes, especially when many practices have now transitioned to digital health, such as telehealth, mobile health, and wearable technologies Incomplete And Variable Categorisation Of Data A process that resolves errors and variability in how data is recorded, entered, or collected across systems or workflows. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. The inconsistent and inaccurate data capture by different interdisciplinary clinicians across clinical data, social determinants, and patient-reported outcomes, a common challenge in many healthcare systems16,17—can be a fundamental data capacity bottleneck that limits the ability to address patients' whole person needs. Inonsistent And Inaccurate Data Capature A process that integrates Learning Health System principles—such as data literacy, improvement science, and patient partnership—into formal training programs and clinical placements. Rosenthal, G. E., et al. (2023). "The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers." Academic medicine 98(9): 1002-1007. Thus, an opportunity exists to apply LHS principles to the evaluation of educational programs for physicians and other health care professionals. Such approaches could capitalize on informatics infrastructures and EHR data to examine the impact of educational programs on patient outcomes, employing the same pragmatic designs used to evaluate health care delivery strategies. Furthermore, the emergence of the National Patient- Centered Clinical Research Network and other EHR research-based networks offers the opportunity to conduct multi-institutional evaluations of education programs.5,37,38 Although such approaches do not address the limited amount of external funding for medical education research, nearly two-thirds of such research is internally funded.39 Application of aLHS resources could dramatically improve the quality of this research Incorporating LHS Principles In Curricula And Clinical Rotations Process A process that reduces turnover by improving working conditions, culture, and professional growth pathways. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Refers to an organization’s ability to keep its staff and prevent turnover—meaning the number of employees who leave their jobs either voluntarily or involuntarily within a certain period1. Increased Employee Retention A process that improves the likelihood that patients follow recommended treatments through communication, support, and personalised care. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Patients who are actively involved in their care decisions are more likely to adhere to prescribed treatments. This can lead to better health outcomes and fewer complications, further reducing the burden on healthcare systems . Increased Treatment Adherence A specification describing how a Learning Health System collects, integrates, and analyses infectious disease data for early detection, outbreak response, real-time situational awareness, and public health decision-making. P.Friedman, C. (2010). Achieving a Nationwide Learning System. Science Translational Medicine. 2. An outbreak of an infectious disease occurs in a specific part of the country, and the disease begins to spread. Once it is apparent that an outbreak has occurred, the learning system is mobilized to track the disease’s spread. As new cases are diagnosed, these data are stored in the EHRs at health care practice sites. In response to a daily or more frequent query, electronic case reports are moved from each practice site to aggregation points in the local, state, and national public health system, making possible real-time nationwide surveillance of the spread of the disease. Infectious Disease Surveillance Use-Case A process that gathers, analyses, and interprets data to generate insights supporting evidence-based decision-making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Specialists who analyze health data to generate insights that inform clinical decision-making, policy development, and quality improvement initiatives. They play a key role in leveraging data to drive continuous learning. Information Analysts Workforce Process A process that delivers coordinated, multidisciplinary care across organisational boundaries to provide patient-centred, efficient, and holistic healthcare services. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Integrated care involves coordinating primary, specialty, and behavioral health services to provide comprehensive and continuous care to patients. Integrated Care A process that engages patients in identifying, accessing, and integrating healthcare and community services into their care journey. A structured effort to combine clinical services with wider community or social services to meet the holistic needs of patients, thus bridging gaps in care delivery. Integrating Health And Community Resources A process that addresses difficulties in harmonising data across systems, applying consistent standards, and ensuring interoperability. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Data Integration is combining data from various sources to create a unified dataset. At the same time, data standardization involves establishing uniform formats, definitions, and structures for consistent and compatible data handling and exchange. This is often challenging because of the variation in the training and certification requirements for providers delivering a wide range of services in WHS. The absence of clear guidance for outcome measure selection is insufficient consideration of the clinical significance of PRO results Integration And Standardisation Management Process A process that strengthens teamwork and shared learning across clinical, research, and administrative boundaries. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Ability to work collaboratively across different disciplines and healthcare settings. This involves teamwork, coordination, and integration of care among various healthcare providers to ensure comprehensive and cohesive patient care. Interdisiplinary Collaboration Developement Process A structured process in which periodic or episodic clinical research activities—such as trials, registries, cohort studies, or evaluations—are designed, conducted, analysed, and translated back into practice to generate new evidence and inform system-wide learning. Bruin, J., et al. (2023). "Conceptualizing learning health systems: A mapping review." Learning health systems 7(1): e10311-n/a. Captures the “formal research cycle” of a Learning Health System, where evidence is generated through planned scientific studies rather than continuous workflow data. Supports evidence-based practice updates, guideline improvements, and system redesign. Learning as (intermittent) information exchange between the clinical domain and the research domain Intermittent Clinical–Research Learning Cycle A process that combines organisational insights with external evidence, benchmarks, guidelines, or best practices to generate comprehensive, actionable knowledge. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Internal Knowledge And External Knowledge Are Integrated A process that uses internal rewards or recognition mechanisms to motivate teams to adopt learning and improvement behaviours. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Health care delivery organizations should reward continuous learning and improvement through the use of internal practice incentives. Internal Practice Incentives A process that increases openness within the organisation by sharing information, performance data, rationales, and decisions to build trust and support informed action. Leveraging open communication of performance metrics, error rates, and lessons learned among teams and departments to build trust, encourage accountability, and accelerate improvement efforts. Internal Transparency As A Tool A process that ensures different digital systems, devices, and applications can access, exchange, and use health data coherently across organisational boundaries. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Ensuring that data from various sources and systems can be integrated and communicated seamlessly is critical. Standards such as Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR) play a significant role in achieving interoperability. Data Interoperability Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Standards such as Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR) are critical for ensuring that different health information systems can communicate and exchange data seamlessly. These standards support the integration of various technologies within a Learning Health System. Interoperability Standards A process that applies generated knowledge to clinical and operational settings through decision support, guidelines, training, and workflow integration. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. There are many challenges to mobilising knowledge within a Learning Health System • Size of the task: The body of medical knowledge is huge. • Standards: Are required to make medical knowledge interoperable • Ambiguity: Unlike traditional guidelines, computable guidelines require all background knowledge and common-sense assumptions to be made explicit. • Inference: knowledge must be combined with data about the specific situation or patient to produce actionable outputs. This becomes even more challenging when there are conflicting knowledge reources, multiple valid interventions, multi-morbidity, polypharmacy,etc. • Outcomes: Must be tracked to understand their magnitude and to who they accrue. • Safety: When such systems malfunction, large numbers of staff and patients can be affected.108 Decision support systems may be considered medical devices under some regulatory environments, • Professionalism: Including clinical problem solving, craft skills and compassion, cannot be entirely replaced by computable knowledge. To improve health and care, it is not enough to generate knowledge. It must be applied. Traditionally, knowledge has been represented in books, journal articles, guidelines and protocols. It has been passed on through publications, education, training and word of mouth, before being unevenly applied. Knowledge To Practice A process that facilitates the sharing of information, expertise, and learning across teams and departments to ensure consistent understanding and accelerate system-wide improvement. Mechanisms and practices (e.g., internal wikis, communities of practice, debrief sessions) that ensure new insights and best practices are disseminated widely and consistently to all relevant stakeholders. Knowledge Transfer Across The Organisation A process that synthesises research and local evidence, adapts it to context, and embeds it into clinical and operational workflows via change management, education, and measurement. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The systematic process of incorporating research findings, clinical evidence, and best practices into everyday care delivery. This ensures that newly generated or discovered knowledge leads to tangible improvements in patient outcomes and organizational performance. Knolwedge Translation Into Practice Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. At INESSS, the raison d’être of learning cycles and LHSs is seen as the improvement of value for people. A critical task for communities of interest is thus to achieve an accepted definition of ‘value’ and track its improvement. LHS Outcomes Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. LHS pillars refer to the infrastructure, systems and resources that provide the foundational supports for LHSs, whereas accelerators represent specific supports that accelerate learning within these systems (i.e. create ‘rapid’ LHSs) We identified six main pillars – scientific, social, technological, policy, legal and ethical pillars, though some LHS supports could align with multiple pillars at the same time. LHS Pillars And Accelerators Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. P2D, D2K and K2P LHS Processes A process where researchers and data scientists design, evaluate, and advance methodologies that enable LHS feedback cycles. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Researchers and scientists dedicated to studying and developing the principles and practices of Learning Health Systems. They conduct research on health outcomes, system performance, and the implementation of evidence-based interventions. LHS Scientist Workfoce Process A process that aligns local change initiatives with the overarching Learning Health System strategy, ensuring coherence between local actions and system-wide learning objectives. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. A Learning Health System requires a strategy andmust be part of the broader strategy of its host organisation(s).The strategy is a set of inter-related choices on how the organisa-tion will deliver value. 38 It can be an explicit document, outlininggoals, priorities and actions, or it can be “a pattern in a streamof decisions” - a strategic direction. The stakeholders must co-design the strategy. Tactics and frameworks specifically tailored to Learning Health Systems, emphasizing data-driven decision-making, iterative improvement, and stakeholder engagement LHS Strategies Process A process that instils leadership behaviours, values, and capabilities and types of leadership required to guide continuous learning and improvement in the health system. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Leadership in the context of a Learning Health System involves guiding and inspiring healthcare organizations to continuously learn and improve. Effective leadership is crucial for creating a culture that prioritizes quality, safety, patient-centered care, and the continuous application of new knowledge to improve health outcomes. Leadership Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. A process that instils leadership behaviours, values, and capabilities required to guide continuous learning and improvement in the health system. Leadership Principles Process A process that represents specific forms of leadership practice enacted at different levels of the organisation to guide, influence, and support Learning Health System activities. Leadership Types Process Brooks, D., et al. (2017). "Developing a framework for integrating health equity into the learning health system." Learning health systems 1(3): n/a. The final component of the PETAL framework is to learn from the challenges and successes of the interventions and to improve health disparities. This is the culmination of all the preceding components and also the starting point in the feedback loop of continuous improvement. At this point, all processes and outcomes should be analyzed to understand the factors that contributed to success and to identify the barriers that will require adjustment moving forward. Dissemination of findings should be broad and transparent, including to the community. Sustainability is critical to the execution of this component and will determine whether and how the findings are embedded in the long term. learn and Improve A planned process executed by a Learning Health Organisation to generate and apply knowledge, characterised by the cadence and actors involved in the learning cycle. Bruin, J., et al. (2023). "Conceptualizing learning health systems: A mapping review." Learning health systems 7(1): e10311-n/a. Learning Approach Process A process that establishes and maintains a network of practitioners, researchers, and stakeholders who share data, experience, and insights to accelerate collective learning. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. A learning community is a group of stakeholders (which may include patients, clinicians, managers, administrators, researchers, technical staff and others) who come together to achieve collective improvement. They are the centre of a Learning Health System. The learning community should drive the development and operation of a Learning Health System. They should implement a process for deciding what is to be studied and will also generate knowledge through reflection and shared experience. Such human communities are well placed to make sense of the complexity associated with a Learning Health System. The learning community may develop from an existing forum and there are helpful guides to operationalising a learning community,but they should be tailored to each Learning Health System. Learning Community A reflective process that identifies, analyses, and disseminates lessons learned from past events—both successes and failures—to guide future decisions and behaviour. Analyzing successes and failures, including near-misses and past projects, to gather lessons learned and apply insights for future improvements Learning From Past Experience A social and institutional entity that coordinates people, resources, and infrastructure to deliver healthcare, generate knowledge, and implement continuous learning and improvement as part of a Learning Health System. McGinnis, J.M., B. Powers, and C. Grossmann, Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. 1 ed. Learning health system series. 2012, Washington, D.C: National Academies Press. A health care organisation in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the care process, whereby providers and patients work together in a coordinated effort Learning Health Organisations (LHOs) include various organisational types such as hospitals, specialty services, research institutes, and regulatory bodies that collectively contribute to the continuous learning and improvement cycle of care delivery and health system performance. Learning Health Organisations An information artefact that specifies a bounded problem or scenario in which a Learning Health Organisation applies learning approaches to achieve defined outcomes. Learning Use-Case Specification A health system where science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the care process. Patients and families are active participants in all elements, and new knowledge is captured as an integral by-product of the care experience. McGinnis, J.M., B. Powers, and C. Grossmann, Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. 1 ed. Learning health system series. 2012, Washington, D.C: National Academies Press. Learning Health Systems A process that mobilises academic knowledge across disciplines (e.g., informatics, epidemiology, systems science, implementation science) to support the design, evaluation, and improvement of LHS activities. Rosenthal, G. E., et al. (2023). "The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers." Academic medicine 98(9): 1002-1007. AMCs and their affiliated medical schools and schools of public health typically possess expertise in methodological disciplines (e.g., informatics, artificial intelligence, implementation science, organizational behavior, biostatistics) and resources (e.g., over 60 AMCs have National Institutes of Health [NIH] Clinical and Translational Science Awards)17 that are salient to improving health care delivery. These capacities provide AMCs the ability to develop novel delivery strategies that are theoretically based, rigorously test strategies, and spread innovations in a purposeful manner. Moreover, expertise in public health sciences (over 40 AMCs have Public Health/Preventive Health Departments with NIH funding18) provides an opportunity to develop new approaches to population health and management of conditions. In so doing, the aLHS can narrow the chasm in the United States between medicine and public health. Leveraging Academic Expertise In Health System Sciences Process A strategy-driven process that maximises the use of health data, digital platforms, and analytical tools to inform decisions, measure performance, and accelerate learning cycles. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Harnessing the power of advanced computational tools and big data analytics to generate insights from vast amounts of healthcare data. This strategy involves using technology to identify best practices, predict outcomes, and inform clinical decisions. Leveraging Technology and Data A process that delivers education on healthy behaviours such as nutrition, exercise, smoking cessation, and mental wellbeing. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Providing patients with knowledge, skills, and resources related to healthy behaviors—such as nutrition, exercise, and stress management—to support long-term health and prevent illness. Lifestyle Education A process that identifies and remediates challenges related to insufficient data storage infrastructure, fragmented data repositories, or inconsistent data accessibility. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Limited physical storage capacity and inconsistent data availability for WHS clinicians present considerable barriers to the integration of Whole Health initiatives within traditional clinical practice. Limited Physical Storage Capacity And Inconsistent Data Availability A process in which designated individuals within teams drive local improvement initiatives, model desired behaviours, and act as connectors between frontline staff and organisational leadership. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. local champions play a vital role in driving change and promoting system-wide improvements. Here are some key attributes of local champions: Influence: Local champions have the ability to inspire and mobilize others toward a common goal. Ownership: They take responsibility for driving positive change within their local context. Physical Presence at the Point of Change: Being actively involved where change occurs ensures effective leadership. Persuasiveness: Champions can articulate a compelling vision and gain buy-in from stakeholders. Grit: They persistently pursue goals despite challenges or setbacks. Participative Leadership Style: Inclusive and collaborative leadership fosters engagement and innovation. Local Champion Leadership Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press AI and machine learning algorithms are applied to analyze complex health data, predict patient outcomes, optimizetreatment plans, and identify new insights. These technologies drive innovation in personalized medicine and continuous improvement in healthcare practices. Machine Learning And Artificial Intelligence A process that communicates the achievements, innovations, and value of the Learning Health System to stakeholders, funders, and the community to build trust and engagement. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Efforts that raise awareness of a healthcare organization’s services or brand, potentially attracting more patients, staff, and community partnerships while promoting positive perception Marketing And Visibility A process that evaluates the organisation’s readiness, capability, and progress toward Learning Health System maturity across domains such as data, leadership, culture, and technology. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. it is important to understand the degree to which processes within the Learning Health System are able to achieve those outcomes in a predictable way.There are widely used maturity models for considering technical infrastructure and there are process maturity assessment tools aimed at the complex, constantly changing sociotechnical aspects of a Learning Health System. Maturity Assessment Process A process that ensures digital tools, AI systems, and data infrastructures are used effectively to improve efficiency, safety, and decision-making. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Optimizing the use and impact of existing health IT systems and infrastructure, driving more efficient workflows, better data insights, and higher-quality care Maximising Technology Benefits A process that develops and applies machine learning models using clinical and operational data to generate predictive insights, automate processes, and enhance learning capacity. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Machine learning is a subset of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed. Application in Healthcare: ML algorithms analyze large datasets, discover patterns, and predict outcomes. They assist in personalized treatment recommendations, disease detection, and optimizing healthcare processes. Machine Learning Enablement Process A process that defines, tracks, and uses metrics to encourage performance improvement and accountability. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Health economists, health service researchers, professional spe-cialty societies, and measure development organizations should partner with public and private payers to develop and evaluate metrics, payment models, contracting policies, and benefit designs that reward high-value care that improves health outcomes. Measurement Metrics A process that enables consumers and community members to formally submit complaints, concerns, or grievances regarding healthcare services, policies, or experiences. Complaint mechanisms provide structured channels for consumers to report concerns, contributing to transparency, organisational accountability, quality improvement, and patient safety within a Learning Health System. Mechanism of Submitting Complaints Menear, M., et al. (2019). "A framework for value-creating learning health systems." Health research policy and systems 17(1): 79-79. The framework was developed by an interdisciplinary team at the Institut national d’excellence en santé et en services sociaux (INESSS). It was informed by a scoping review of the scientific and grey literature on LHSs, regular team discussions over a 14-month period, and consultations with Canadian and international experts. Menear Value Creating LHS Framework A process that selects and designs methodological approaches for evaluation, including study design, sampling, analytic strategies, and validity considerations. Study Designs: Comparative effectiveness studies (e.g., randomized controlled trials, observational studies). Implementation science methodologies (e.g., Plan-Do-Study-Act [PDSA] cycles, mixed-methods approaches). Adaptive trials or pragmatic trials tailored to real-world settings. Analytical Approaches: Advanced statistical methods (e.g., regression modeling, causal inference). AI/ML techniques for predictive analytics and pattern recognition. Methodological Design Process A process that defines, develops, validates, and tracks measurable indicators to assess performance, quality, outcomes, and system learning. Key Performance Indicators (KPIs): Clinical outcomes (e.g., mortality, readmission rates, quality-adjusted life years [QALYs]). Operational outcomes (e.g., workflow efficiency, resource utilization). Patient-centered outcomes (e.g., satisfaction, patient-reported outcome measures [PROMs]). Equity Metrics: Measures to assess and address health disparities across different populations. Metrics And Indicators Development Process A process that implements small-scale, rapid interventions designed to produce incremental improvement and learning within complex healthcare environments. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Local, small-scale interventions that can be rapidly implemented and tested, allowing quick learning cycles and incremental progress toward larger change goals Microstrategies Process A process in which managers translate strategic priorities into operational actions, coordinate teams, remove barriers, and support the implementation of learning and improvement initiatives. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Mid-level managers play a crucial role in implementing strategies at the operational level. They bridge the gap between executive leadership and frontline staff, ensuring that policies and practices are executed effectively. They also play a key role in motivating and managing teams to adhere to continuous learning principles. Mid Management Leadership Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press mHealth applications include mobile apps and wearable devices that track health metrics, provide health education, and enable patient-provider communication.These tools empower patients to manage their health and participate actively in their care. Mobile Devices And Sensors Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Monash Quadrant 5 And 6 Data Derived Evidence Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Monash Quadrant 7 And 8 Implementation Derived Evidence Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Monash Quadrant 3 And 4 Research Derived Evidence Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Ensure that the system's goals and priorities are aligned with the needs of all stakeholders, including patients, clinicians, policymakers, and researchers. Monash Quadrant 1 And 2 Stakeholder Derived Evidence Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Monash Melder LHS Framework A process that tracks disease patterns, population trends, and emerging risks to support proactive system responses and resource allocation. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Detecting and analyzing patterns or trends in disease incidence and prevalence, enabling proactive interventions and informed public health decisions Monitoring Epidemiological Phenomena A process that brings together professionals from multiple disciplines to collaborate in planning, delivering, and reviewing patient care. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Groups of healthcare professionals from diverse specialties (e.g., primary care, behavioral health, pharmacy) who collaborate to deliver comprehensive and holistic patient care Multidisciplinary Care Teams Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press NLP technologies are used to extract meaningful information from unstructured data, such as clinical notes and medical literature. They enhance the ability to analyze and utilize diverse data sources for research and clinical decision-making. Natural Language Processing A specification that describes how real-world data and feedback loops are used to monitor the safety, side effects, and effectiveness of new therapeutics after approval and during early adoption. P.Friedman, C. (2010). Achieving a Nationwide Learning System. Science Translational Medicine. 2. Systems for post-market surveillance of new drugs to monitor their safety and effectiveness in real-world settings. This involves collecting and analyzing data on adverse events and outcomes. New Drug Safety Monitoring Use-Case A process that regularly reviews patient status, treatment adherence, symptoms, and outcomes to ensure continuity and prevent deterioration. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The continuous assessment of a patient’s health status over time, including regular check-ins, data collection (e.g., vitals, lab results), and timely interventions. This ensures that care remains aligned with the patient’s evolving needs, supports preventive measures, and enables early detection of issues. Ongoing Monitoring And Follow-ups A process that encourages curiosity, experimentation, acceptance of new ideas, and willingness to challenge existing assumptions to support innovation and growth. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Fostering an environment where continuous learning is valued and new ideas or approaches are welcomed and tested. Openness to Learning And Innovation A process that manages daily operations, aligning logistics, resources, and coordination for efficient service delivery. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Personnel responsible for the day-to-day functioning of healthcare facilities and systems. This includes administrative staff, support staff, and managers who ensure that healthcare operations run smoothly and efficiently. Operation Staff Workfoce Process A process that engages staff in improving workflows, reducing inefficiencies, and enhancing operational practices to strengthen performance and learning culture. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A learning health care system constantly refines complex care operations and processes through ongoing team training and skill building, systems analysis and information development, and creation of the feedback loops for continuous learning and system improvement. Operational Improvement A process that systematically applies generated knowledge to clinical practice, operations, and policy to create measurable improvements. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Ensuring that relevant, current, and evidence-based information is effectively integrated into clinical and operational decisions, leading to improved safety and performance.” Optimising Knowledge Application A continuous process that captures insights from practice, experiments with new ideas or technologies, evaluates outcomes, and diffuses successful innovations across the organisation. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The process by which an organization acquires, creates, and applies new knowledge to adapt, evolve, or innovate healthcare services and processes. It encompasses both learning from internal experiences and external evidence, fostering a culture of experimentation and continuous improvement. Organisational Learning and Innovation A process that includes patients or carers in organisational governance, service design, and quality improvement activities. Strategies and policies at the hospital or healthcare organization level that involve patients in service design, quality improvement, and governance Organisational Level Engagement A process that creates policies that reward, encourage, or mandate participation in research, improvement, data sharing, or learning activities. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Organisational Policies Incentivise LHS Activities A process that structures, governs, and coordinates healthcare organisations and teams to support efficient service delivery, clear accountability, and alignment with learning health system principles. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Organizations that support Learning Health Systems (LHS) are defined broadly to include a variety of entities that contribute to the continuous improvement and learning processes within healthcare. Organisational Processes A process that designs and adapts organisational arrangements, governance lines, and team configurations to enable effective change and learning. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. People and resources must be organised to deliver the strategy. This is sometimes called a target operating model (TOM) or an organisational architecture and has been explic-itly addressed in various Learning Health Systems.Care is requiredto ensure that this approach does not become overly mechanistic,making unrealistic assumptions about the future. The hierarchical arrangements, roles, and reporting relationships within an organization, influencing communication flow, accountability, and decision-making Organisational Structure Process A role that inheres in a person who provides care or support to a patient without being a family member, including friends, guardians, paid carers, disability or aged-care staff, and other support personnel. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. This includes not only family members but also friends, neighbors, or any other individuals who provide care and support to the patient. Other Caregiver Role A process that enables consumers and community members to provide feedback through formal or informal mechanisms beyond complaints, including suggestions, compliments, surveys, focus groups, and community consultations. Feedback opportunities encourage ongoing dialogue between consumers and healthcare organisations, supporting co-design, service improvement, and continuous organisational learning. Other Feedback Opportunities A process that provides administrative, logistical, and operational support enabling delivery and learning activities across the organisation. Support Staff Workface Process A process that links financial reimbursement to patient outcomes, quality, and value generation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Public and private payers should reward continuouslearning and improvement through outcome- and value-oriented payment models, contracting policies, and benefit designs. Payment models should adequately incentivize and support high-quality team-based care focused on the needs and goals of patients and families. Outcome And Value Oriented Payment A process that involves frontline staff, patients, and other stakeholders in governance and decision-making to promote ownership and inclusiveness. The leadership of the LHS will be a multi-stakeholder collaboration that empowers stakeholders to participate meaningfully in LHS decisions and activities Participatory Leadership Approach Process A process that ensures the values, needs, and preferences of patients are consistently integrated into organisational decisions, service models, and learning activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Engaged, empowered patients—A learning health care system is anchored on patient needs and perspectives and promotes the inclusion of patients, families, and other caregivers as vital members of the continuously learning care team. Ensuring that the needs and perspectives of patients are central to the system’s learning and improvement efforts. An organisation that represents patient and community interests, promotes patient-centered care, and contributes to governance, design, and evaluation of Learning Health System activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Organizations that represent the interests of patients and their families, advocating for patient-centered care, better access to healthcare, and policies that support the needs of patients. Patient Advocacy Group A process that guides how patients and clinicians are involved in co-design, governance, and decision-making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Encouraging active participation of both patients and healthcare providers in the learning process. Policies should promote shared decision-making, patient-centered care, and provider involvement in quality improvement activities. Patient And Provider Engagement Policy Process A process that ensures patients, families, communities, and stakeholders are meaningfully involved in decisions regarding data governance, research, and system learning. Feedback Records: Input from patients, families, and communities about ethical concerns. Stakeholder participation logs in oversight processes. Communication Logs: Documentation of how ethical guidelines are communicated to stakeholders. Notifications regarding changes in ethical practices or data use. Patient And Stakeholder Engagement Process A process that identifies and considers a patient's biological and clinical characteristics, including diagnosis, genetics, comorbidities, physiological status, and treatment risks, to support shared care decisions. The individual genetic, physiological, or medical traits of a patient that may affect diagnostic considerations, treatment efficacy, or prognosis. Patient Biological Characteristics A process that actively involves patients and their families in decision-making, tailoring healthcare services to individual preferences, needs, and values. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Patient-centered care focuses on providing care that is respectful of and responsive to individual patient preferences, needs, and values. It ensures that patient values guide all clinical decisions. Patient Centered Care Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Refer to the advantages and positive outcomes that arise from implementing patient-centered care practices within a healthcare system. Patient Centered Care Benefits Process Group A process that builds empathy, shared decision-making, and patient engagement competencies across care teams. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Skills to engage patients and their families as active participants in the care process. This includes effective communication, shared decision-making, and understanding patient preferences and values to tailor care plans accordingly. Patient Centered Care Skills Development Process A process that defines and applies validated patient-centred outcome measures to evaluate health experiences, functional improvements, and quality of life. Indicators and metrics capturing patient-reported experiences and results, central to value-based assessments and improvement initiatives. Patient Centered Outcome Measures Process A process that identifies and connects patients to community-based resources that support health, wellbeing, and self-management. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Efforts and programs designed to connect patients with available community resources (e.g., food banks, transportation services, educational workshops), ensuring that patients can easily access and benefit from local support systems. Patient Community Resource Support A planned process in which patients, families, and caregivers actively participate in their own care, decision-making, self-management, and system-level improvement activities, supported by healthcare teams, digital tools, and community resources. The active involvement of patients in their own healthcare decisions, goal-setting, and self-management activities. This includes shared decision-making, education, and collaboration between patients and healthcare professionals to improve outcomes and satisfaction. Patient Engagement A process that systematically collects patient-reported experiences of healthcare services to evaluate the quality, accessibility, responsiveness, and person-centredness of care. Patient experience surveys provide structured evidence about patients' interactions with healthcare services. The findings inform quality improvement initiatives, service redesign, and organisational learning by identifying areas where patient-centred care can be strengthened. Patient Experience Survey A process that identifies and considers a patient's social, economic, cultural, environmental, and personal circumstances that may influence healthcare decisions and treatment implementation. The environmental, social, financial, or cultural context in which a patient lives, shaping their ability to adhere to treatment plans, access resources, and engage in shared care decisions. Patient Life Circumstances A patient portal is a secure technological interface that allows patients to access various kinds of personal medical information. These portals typically provide access to laboratory results, imaging interpretations, diagnoses, and follow-up care. Patients can use patient portals to view their health records, communicate with healthcare providers, schedule appointments, and manage their healthcare information. Patient portals enhance patient engagement, facilitate communication, and empower individuals to actively participate in their own care. Patient Portal A process that identifies and incorporates a patient's expressed preferences regarding healthcare options, treatments, and care delivery into shared decision-making. Specific desires or inclinations patients express regarding their care, including treatment modalities, care settings, or lifestyle considerations, which guide shared decision-making processes. Patient Preferences A process that enhances shared understanding, communication, empathy, and trust between patients and clinicians. The direct communication and relationship-building activities between patients and healthcare professionals, encompassing consultations, education, emotional support, and mutual feedback. Patient Provider Interactions A process that collects, verifies, and uses patient-reported experience (PREMs) and patient-reported outcomes (PROMs) to inform quality improvement and value-based care. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Patient-Reported Experience Measures (PREMs) and Patient-Reported Outcome Measures (PROMs) play essential roles in assessing healthcare quality from the patient’s perspective: Patient Reported Experience and Outcome Measures Process A process that identifies and incorporates a patient's personal values, goals, beliefs, and priorities to guide shared healthcare decisions. The foundational beliefs, moral principles, or personal convictions held by a patient, influencing how they prioritize treatment options and make healthcare decisions Patient Values A process in which patients actively contribute to their own care, provide feedback, participate in shared decision-making, engage in co-design activities, and contribute to organisational learning and improvement. Includes PROMs/PREMs input, advisory roles, co-design sprints, and personalised care planning. Patient Engagement Process A process that trains staff to identify risks, prevent harm, and implement safety improvement measures. Kalra, A., et al. (2017). "Cultivating Skills for Success in Learning Health Systems: Learning to Lead." Journal of the American College of Cardiology 70(19): 2450-2454. Currculum: Developing a hospital program in patient safety Adverse event reporting Reviewing errors: root cause analyses Hospital-acquired conditions and serious reportable events Just culture Safety culture Disclosure and apology A trainee curriculum in safety Patient engagement Free from harm Diagnostic errors: what they are and how they can be better understood and prevented The hazardous intersection of electronic health records and patient safety The Food and Drug Administration Sentinel Network Patient Safety Skills Development Process A formal group or committee composed of patients, family members, and healthcare organization representatives that collaborates to shape policies, care practices, and strategic decisions, ensuring patient and family perspectives are integrated at the organizational level Patients And Family Council A process that tracks, reviews, and reports organisational performance against defined indicators to ensure accountability and continuous improvement. Ongoing review of organizational metrics and outcomes to ensure accountability, quality, and alignment with strategic objectives Performance Monitoring Process A process that establishes mechanisms for measuring, reporting, and communicating performance outcomes to promote accountability, trust, and continuous improvement. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Performance Transparency Increase transparency on health care system performance. Health care delivery organizations, clinicians, and payers should increase the availability of information on the quality, prices and cost, and outcomes of care to help inform care decisions and guide improvement efforts. Performance Transparency A process that applies PDSA cycles to test, measure, refine, and scale changes within operational or clinical workflows. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Plan-Do-Study-Act Cycles Are Used A process that develops and maintains technical or data-sharing platforms that underpin LHS infrastructure. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Definition: These are entities or individuals responsible for providing and maintaining the technological platforms that support data integration, interoperability, and communication within a Learning Health System. This includes providers of electronic health records (EHRs), health information exchanges (HIEs), and other digital health solutions. Role: They ensure that the infrastructure needed for data collection, storage, and analysis is robust, secure, and user-friendly, facilitating seamless data flow across different systems and stakeholders. Platform Providers Workfoce Process A process that designs and manages interoperable digital ecosystems — including platforms, APIs, and data standards — that enable scalable learning, integration, and innovation. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Much of the infrastructure required to build a Learning Health System is common across organisations. This can include IT, Electronic Record Systems, data transport, storage and processing, decision support and even governance arrangements and organisational structures. The development of platforms that provide this infrastructure as a service, is making it feasible and affordable for even small organisations to develop Learning Health Systems Platform Thinking A process that develops and applies principles guiding the creation, implementation, and monitoring of organisational policies. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Policies are formal guidelines or principles that direct decision-making and activities within the LHS. They serve as a blueprint for actions and help in establishing boundaries and expectations. Policies can be about operational processes (like data access, research methodologies), organizational behaviors (staff conduct, conflict-of-interest policies), or strategic directions (focus areas, resource allocation). Policy refers to the formalized rules, regulations, and guidelines that shape the operation and governance of a Learning Health System. These policies are designed to ensure that healthcare practices are aligned with the goals of continuous learning and improvement, support the integration of evidence-based practices, and foster an environment conducive to innovation and quality care. Policies Alignment: Policies establish and enforce standards for care quality, safety, and data management. These standards ensure consistency and reliability across the healthcare system, facilitating the adoption of best practices and continuous improvement. Example: Policies might require adherence to specific clinical guidelines or interoperability standards for health information technology. Integration: Policy provides the framework within which governance structures operate. Governance bodies, such as hospital boards and regulatory agencies, use policies to guide decision-making, ensure accountability, and oversee the implementation of LHS principles. Example: Policies may dictate the roles and responsibilities of governance bodies in promoting transparency and accountability in healthcare organizations. Regulation and Utilization: Policies regulate the use of health information technology and data analytics to support the LHS. They ensure data privacy, security, and ethical use, while promoting the integration of technology into clinical workflows. Example: Policies might specify requirements for electronic health record (EHR) systems to facilitate data sharing and interoperability. Regulation and Utilization: Policies regulate the use of health information technology and data analytics to support the LHS. They ensure data privacy, security, and ethical use, while promoting the integration of technology into clinical workflows. Example: Policies might specify requirements for electronic health record (EHR) systems to facilitate data sharing and interoperability. Support and Development: Policies influence workforce training, development, and management. They ensure that healthcare professionals have the necessary skills and resources to engage in continuous learning and improvement activities. Example: Policies may mandate ongoing education and training programs for clinicians to stay updated with the latest evidence-based practices. A process that develops and applies principles guiding the creation, implementation, and monitoring of organisational policies. Policy Principles Process A specification outlining how a Learning Health System stratifies populations, identifies risk groups, designs targeted interventions, and evaluates outcomes to improve population health and equity. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Approaches that use data to identify and address the health needs of specific populations. This involves stratifying risk, targeting interventions, and monitoring outcomes to improve overall health. anaging the health of a defined population by using data and analytics to identify health trends and address health needs at a population level. Population Health Management Use-Case A process that manages data representing population-level demographics, epidemiology, and health determinants to support surveillance, planning, and equity analysis. Matheny, M.E., D. Whicher, and S. Thadaney Israni, Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA : the journal of the American Medical Association, 2020. 323(6): p. 509-510. Promoting population-representative data with accessibility, standardization, and quality is imperative. Health care AI should be trained and validated on population-representative data to ensure accuracy for all populations and to achieve performance levels necessary for scalable success. Population Representative Data Process Promotes optimism, satisfaction, and trust through patient-centred communication and care. Individuals develop more positive attitudes about themselves and others Positive Attitudes A process that identifies and learns from individuals or teams who achieve exceptional outcomes despite similar constraints as their peers. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Positive Deviance A process that captures and standardises information from real-world practice, translating experiences and outcomes into structured data for ongoing learning and improvement. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Once the learning community has decided what to study, the next step is to collect data that represents what is going on within the system. This requires several technical building blocks, This includes Data capture, Data Quality and Provenance, Data Storage and Access, Information Governance and Interoperability. All those elements are captured under top class Data. Practice to Data A proactive process that identifies risks, promotes healthy behaviours, and implements interventions to prevent disease and reduce long-term healthcare burdens. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Emphasizing the prevention of diseases and health conditions through proactive measures and early interventions. Preventive Care A governance-guided learning process in which explicit organisational principles (e.g., safety, equity, value) are codified and used to steer decision-making, reflection, and improvement cycles across services. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. An approach to continuous learning guided by foundational principles—such as evidence-based practice, transparency, data-driven decision-making, and collaboration—that ensures consistent application of ethical, scientific, and operational standards across the organization. Principle Based Learning A process that enables leaders to systematically prioritise initiatives based on evidence, feasibility, risk, and strategic alignment. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. A systematic approach to setting clear, evidence-based priorities, ensuring resources and efforts align with the most critical objectives. Prioritisation And Ranking Process A process that directs how LHS initiatives are prioritised, resourced, and sequenced. Bailey-Davis, L.D., et al., Operationalizing the Learning Health Care System in an Integrated Delivery System. EGEMS (Washington, DC), 2015. 3(1): p. 1122-1122. Definition T he prioritization component refers to the process in which learning activities and opportunities are aligned with strategic goals across different levels of the organization. Prioritisation Of LHS Activities Policy Process Brooks, D., et al. (2017). "Developing a framework for integrating health equity into the learning health system." Learning health systems 1(3): n/a. Prioritise Health Equity The first component of the PETAL framework is the prioritization of health equity. Health equity cannot simply exist as a concept; it must be embedded into the core mission and supported through purposeful, tangible, and measureable actions. To accomplish this, health equity must be a strategic priority based on community need with recognition of SDH, supported with technology, and directed by a governance structure dedicated to equity that includes community members and leaders. Commitment to health equity as a strategic priority should be reflected and integrated throughout all initiatives and reinforced through allocation of resources.31 Through community engagement and preliminary data analysis, a health equity agenda can be developed to prioritize and strategize on how interventions should be deployedl A process that reinforces a culture of safeguarding sensitive information, ensuring data privacy, security, and compliance in all organisational activities. Menear, M., et al., A framework for value-creating learning health systems. Health research policy and systems, 2019. 17(1): p. 79-79. The LHS will protect the privacy, confidentiality and security of all data to enable responsible sharing of data, information and knowledge Privacy And Security Focused A process that introduces and embeds tools such as checklists, workflows, or decision aids that support consistent, reliable practices and reinforce a culture of structured learning. Adopting specific improvement methodologies or toolkits (e.g., Lean, Six Sigma, BPMN) to streamline operations, reduce variability, and foster a culture of standardized best practices. Process Tool Implementation A process by which professional regulatory authorities oversee credentialing, standards, ethics, and practice within specific disciplines. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. These bodies, such as medical boards and professional societies, set standards for professional practice, oversee certification and licensure, and ensure adherence to ethical and professional guidelines. Professional Governing Bodies Process A professional organisation that sets clinical standards, develops guidelines, supports workforce education, and promotes research and best practice relevant to its specialty. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Organizations representing specific medical specialties that develop clinical guidelines, provide continuing education, and support research and advocacy efforts to improve care within their fields. Professional Specialty Society A strategic process that cultivates values, leadership behaviours, and practices encouraging curiosity, reflection, and evidence-based experimentation within healthcare teams. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Fostering a culture within healthcare organizations that prioritizes continuous learning and improvement. This includes leadership commitment to quality improvement, transparency, and collaboration among healthcare professionals and patients. Promoting a Culture Of Learning A process that enhances value by improving quality, outcomes, and patient experience relative to cost. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Balancing quality and cost to deliver high-value care that meets patients’ needs while efficiently using resources, thus achieving sustainable health outcomes Providing Better Value Healthcare A role that inheres in a person by virtue of belonging to a defined community—such as a cultural, geographic, occupational, or disease-related group—whose perspectives are relevant to health system learning, engagement, and planning. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The broader community who are potential or actual recipients of healthcare services and who have a vested interest in the quality, safety, and efficiency of the healthcare system. Community Member Role A process focused on population-level data, epidemiology, and preventive interventions that contribute to system-wide learning. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Definition: Public health specialists are professionals who focus on the health and well-being of populations. They use data to identify health trends, assess community health needs, and develop policies and programs to improve public health outcomes. Role: In an LHS, they analyze population health data, contribute to public health surveillance, and design interventions to prevent disease and promote health at the community level. They also play a critical role in integrating public health insights into clinical care practices. Public Health Specialist Workforce Process A planned process in which members of the general public are engaged to contribute perspectives, preferences, and insights to inform organisational decisions, priority-setting, policy development, and learning activities within the health system. Includes public consultations, town-hall forums, surveys, deliberative panels, and co-design workshops. Public Engagement Process A process that applies qualitative methods, quantitative methods, or mixed-methods to explore experiences, measure outcomes, assess implementation, or evaluate system performance. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Qualitative research focuses on understanding concepts, thoughts, or experiences using words. Quantitative research deals with numbers and statistics. It tests or confirms theories, often establishing generalizable facts about a topic. Qualitative And Quantitative Research Process A process that promotes high standards of clinical quality, safety culture, error prevention, and continuous monitoring to protect patients and improve outcomes. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Prioritizing high standards of care and a commitment to reducing errors and harm in healthcare delivery. Quality And Safety Focus A process that defines expectations for continuous quality improvement practice across the organisation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Policies should support and mandate continuous quality improvement initiatives, including the use of evidence-based practices and regular performance assessments to enhance care quality. Quality Improvement Initiatives Policy Process A specification that describes how continuous quality improvement cycles—such as PDSA, Lean, or Six Sigma—are applied to clinical workflows or operational processes within the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Programs aimed at continuously improving the quality of care through data-driven approaches. These include identifying gaps in care, testing interventions, and scaling successful strategies. Quality Improvement Initiative Use-Case A process that validates and monitors data accuracy, completeness, consistency, and reliability to maintain confidence in analytics and decision-making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Maintaining high data quality and integrity is essential. This involves ensuring the accuracy, completeness, consistency, and reliability of data collected from different sources. Data Quality And Integrity Process A process that uses short learning cycles, real-time data, and quick experimentation to accelerate improvement. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Rapid Learning Processes Are Used A process that quickly develops, tests, and refines new ideas, workflows, or digital tools through iterative feedback and short learning cycles. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. A cycle of quick, iterative development and testing of new ideas, processes, or tools, enabling fast feedback and refinement before widespread deployment Rapid Prototyping Process A process that ensures appropriate and timely access to data for stakeholders, promoting openness, accountability, and equitable participation in learning. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Data should be accessible in real-time to support timely decision-making. This includes the ability to retrieve and analyze data as needed to inform clinical decisions, patient management, and research activities. Data Accessibility and Transparency Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A learning health care system continuously and reliably captures, curates, and delivers the best available evidence to guide, support, tailor, and improve clinical decision making and care safety and quality. Realtime Access To Knowledge A process that identifies and mitigates inaccuracies arising when individuals do not accurately remember past events, leading to distorted or incomplete data. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. (inaccurate recollection of information) and response bias (influenced patient responses).22,23 Recall bias may lead patients to inaccurately remember and report their previous treatments or therapies, potentially omitting essential information. Such omissions can hinder healthcare providers' ability to develop comprehensive care plans tailored to patients' needs. Furthermore Recall Bias A process that enables continuous feedback between research activities and real-world practice so each informs and improves the other. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Reciprocal Relationship Between Research and Practice A collaborative process in which patients, communities, clinicians, managers, researchers, and other stakeholders share experiences, insights, preferences, and feedback to co-produce knowledge, inform decision-making, and shape learning priorities within the Learning Health System. Bruin, J., et al. (2023). "Conceptualizing learning health systems: A mapping review." Learning health systems 7(1): e10311-n/a. Learning as recurrent interaction between stakeholders to reveal/discuss underlying values, to evaluate processes, and to identify opportunities for change, and share bestpractices. Human interaction and the exchange of experiences and ideas are central. Stakeholder Interaction Learning Cycle Alleviates fear, anxiety, and distress through supportive and empathetic care. Enhancing coping skills and resilience can decrease emotional distress. Reduced Emotional Distress A process that reduces hospital spending per patient through efficiency, appropriate care utilisation, and waste reduction. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Value-Based Healthcare: LHSs aim to improve value in healthcare by providing better care at lower cost. The goal is to achieve optimal health outcomes while minimizing expenses1. Hospital Cost Per Patient Decrease: LHSs leverage data, evidence, and continuous learning to optimize resource utilization. By identifying efficient practices, reducing waste, and enhancing care delivery, LHSs contribute to lowering hospital costs per patient. refers to the reduction in healthcare costs associated with individual patients Reduced Hospital Cost Per Patient A process that lowers litigation risk through better communication, transparency, safety, and trust. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Refers to a reduction in medical malpractice claims within healthcare systems. Reduced Malpractice Claims A process that enhances patient comfort through timely, evidence-based pain management interventions. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Pain Reduction: LHS aims to enhance patient care by implementing evidence-based practices. By optimizing pain management, LHS can reduce discomfort during illness, surgery, or recovery. Improved Quality of Life: Less pain leads to better patient experiences and faster healing. Patients can resume daily activities sooner, contributing to overall quality of life. Efficient Resource Use: Effective pain management reduces unnecessary hospital stays and resource utilization. LHS ensures that pain relief strategies align with best practices. efers to minimizing patients’ physical suffering and improving their overall well-being. Reduced Pain And Discomfort A process that optimises use of clinical, staffing, and technological resources through evidence-based streamlining. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Avoiding adverse events minimizes unnecessary costs and resource utilization. Reduced Resource Utilisation A process that decreases inconsistent, unsafe, or ineffective clinical practices through standardisation, evidence-based care, and benchmarking. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Minimizing inconsistent or unnecessary differences in clinical practice or resource use, ensuring more equitable, efficient, and evidence-based care Reducing Unwarranted Variation A process that carries out periodic tests (e.g., blood pressure, mammograms, diabetes screening) to detect disease early. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Routine, scheduled evaluations or tests (e.g., blood pressure checks, mammograms, colonoscopies) used to detect potential health issues early and facilitate timely preventive measures or treatments. Regular Health Screening An organisation that establishes and enforces regulatory requirements, safety standards, accreditation criteria, and compliance expectations for Learning Health System participants. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. These organizations set standards for healthcare quality and safety, conduct evaluations, and provide accreditation to healthcare institutions. Examples include The Joint Commission and other regulatory agencies. Regularotry And Accreditation Body A process that ensures organisational policies align with external regulatory and accreditation frameworks. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Aligning regulatory and accreditation requirements with the goals of a Learning Health System. This includes creating policies that facilitate innovation and remove barriers to the adoption of new practices and technologies. Regulartory And Accreditation Requirements Policy Process A process that ensures organisational operations, data use, and research activities comply with statutory, regulatory, and accreditation requirements. Ensuring that organizational policies, procedures, and actions align with external regulations, laws, and accreditation requirements. Regulatory Compliance Process A planned process that conducts research, translates evidence and builds capability through education to strengthen LHS practice. Rosenthal, G. E., et al. (2023). "The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers." Academic medicine 98(9): 1002-1007. Academic features that emphasize how an aLHS differs from an LHS, especially from Academic Medical Center's (AMC) point of view. Research And Education Process A process that designs and conducts research studies specifically addressing organisational priorities, operational needs, or quality concerns. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Research Answers Questions Directly Related To The Organisation A process that selects and applies appropriate research designs to answer clinical, operational, and system-level questions within the Learning Health System. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Refers to the systematic planning and structure of studies or investigations conducted within the health system. Research Design Process Group An organisation that allocates financial resources to support research, innovation, data infrastructure, workforce development, and LHS implementation activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Research Funding Agencies and Organisation A process that incorporates research findings into clinical workflows, policies, and decision support systems. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Research Is Translated Into Practice A process that equips staff with methodological and analytical competencies to design, conduct, and translate research. https://www.ahrq.gov/learning-health-systems/building-workforce.html Competencies Demonstrate the ability to use theory and conceptual models in the design and interpretation of LHS research. Demonstrate the ability to develop an appropriate observational, quasi-experimental, or experimental study design while mitigating threats to internal and external validity for research that is minimally disruptive to operations in real world health systems and practices. Demonstrate knowledge of mixed methods and how they can be used to improve LHS research studies. Demonstrate knowledge of how to assess multilevel determinants of health and health care disparities when designing studies. Demonstrate the ability to select and interpret appropriate clinical, financial, and patient-centered outcomes of interest based on the concepts they measure and their measurement properties. Demonstrate the ability to apply the principles of hypothesis testing and statistical inference to data collected routinely through the course of care as well as supplemental data from patients, providers, and health systems. Core Competencies for Learning Health System (LHS) Investigators Research methods within the context of complex health systems involve using appropriate study designs and analytic techniques to assess outcomes relevant to health system stakeholders12. These methods are essential for generating novel insights and evidence that can be rapidly implemented to improve individual and population outcomes as well as health system performance. Research Skills and Evidence Translation Process A process that teaches systematic identification, prioritisation, and framing of research or improvement questions. https://www.ahrq.gov/learning-health-systems/building-workforce.html Competencies Demonstrate the ability to compose feasible and timely research questions and hypotheses, incorporating stakeholder priorities, to generate evidence that informs meaningful clinical and policy decisions. Demonstrate the ability to engage with all relevant stakeholders (patients, families, clinicians, and system leaders) in the elicitation and prioritization of research questions that address current and future stakeholder needs. Demonstrate the ability to critically analyze and assess available scientific evidence from peer-reviewed articles, systematic reviews, meta-analyses, and gray literature to identify novel LHS questions and to judge the applicability of the evidence to a local care setting. Core Competencies for Learning Health System (LHS) Investigators This competency involves asking meaningful research questions and evaluating the usefulness of scientific evidence and insights. LHS researchers need to formulate relevant questions that address health system challenges and assess evidence rigorously to inform decision-making and practice improvement12. Research Questions Formulation Process A process that collects, manages, and preserves data used for scientific inquiry, ensuring integrity, reproducibility, and compliance with research governance requirements. Research Data Process A process that undertakes inquiry, analysis, and knowledge translation aligned with LHS improvement objectives. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Definition: Researchers in an LHS are scientists and academics who conduct studies to generate new knowledge and evidence about health, healthcare delivery, and system performance. Role: They design and implement research studies, analyze data, and translate findings into practice. Their work informs clinical guidelines, health policies, and quality improvement initiatives, ensuring that healthcare practices are based on the latest evidence. To help achieve a learning health care system, health researchers need to play the following roles: • Actively engage with care communities to advance understanding of clinical research and clinical trials and thereby enhance balanced consideration of and enrollment in clinical trials. • Develop and implement new methods for conducting clinical research that overcome the limitations of the traditional research enterprise. • Partner with patients to build trust in the clinical research enterprise. • Optimize, through formal and informal structures, the linkages among basic research, clinical research, public health, and care delivery through such means as technology, communities of learning, and cross-industry collaboration. • Engage in efforts to advance publication and learning as a result of quality improvement efforts. • Advance the science of dissemination and implementation, with a focus on practical strategies for expanding the diffusion of clinical research. Researchers Workforce Process A process that governs how financial, workforce, and technical resources are allocated to support learning and improvement. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Allocating sufficient resources to support the infrastructure, technology, and workforce necessary for a Learning Health System. This includes funding for health information technology, training, and research. Resource Allocation Policy Process A planned organisational process that allocates and stewards resources (people, budget, time, data/tech infrastructure) to establish, operate, and scale Learning Health System activities. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The allocation of organizational assets—such as funding, infrastructure, technology, and dedicated personnel—to support the planning, execution, and sustainment of learning health system activities. This includes underwriting research, data analytics, training, and quality improvement initiatives. Resource Investment In LHS A process that identifies and mitigates systematic errors caused by differences in how individuals respond to surveys, interviews, or questionnaires, resulting in skewed or incomplete data. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Response bias can influence how patients describe their experiences with integrative treatments, potentially leading to an overly positive or socially desirable portrayal.Mitigating these biases is crucial to ensuring patient care remains truly holistic, evidence-based, and focused on optimizing health well-being. It is vital to implement standardized assessment tools, provide cultural sensitivity, and offer diverse data collection methods while ensuring healthcare providers receive training in unbiased data interpretation. Additionally, inclusive accessibility and patient engagementmcoupled with a LHS feedback loop,can help mitigate biases and improve the overall quality and relevance of PRO data. Response Bias A process that translates analytical outputs into actionable insights and communicates findings to stakeholders for informed decision-making and policy development. McDonald, P. L., et al. (2024). "Data to knowledge to improvement: creating the learning health system." BMJ 384: e076175-e076175. Paige McDonald and colleagues detail key domains, tools, and actions required to enact learning health systems for continuous intelligent improvement in healthcare Interpretation of results involves analyzing and understanding the findings from studies or data analyses. Purpose: It allows researchers, clinicians, and policymakers to draw meaningful conclusions, make informed decisions, and improve healthcare practices based on evidence. Results Interpretation and Reporting Process A process that identifies, monitors, and manages modifiable risk factors (e.g., hypertension, obesity, smoking) to prevent disease progression. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A systematic approach to identifying, monitoring, and controlling modifiable factors (e.g., smoking, hypertension, obesity) that increase the likelihood of disease or poor health outcomes Risk Factor Management A process that identifies, assesses, prioritises, and mitigates risks related to safety, data use, technology, and organisational operations within the Learning Health System. Proactively identifying, evaluating, and mitigating threats to safety, finances, or reputation within a healthcare setting. Risk Management Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. A systematic approach to categorizing patients or populations by their likelihood of developing health conditions or complications, enabling targeted resource allocation and prevention strategies. Risk Stratification A process that systematically collects patient feedback to guide improvement and measure experience. The ongoing collection and evaluation of patient feedback, perceptions, and satisfaction data to drive continuous improvements in care processes and outcomes Routine Assessemen Of Patient Experience A process that routinely collects, monitors, and analyses consumer engagement measures to evaluate participation, experience, and the effectiveness of engagement activities for continuous improvement Routine measurement enables organisations to systematically monitor consumer engagement using feedback mechanisms and performance indicators. Regular measurement supports organisational learning by identifying strengths, opportunities for improvement, and changes in consumer experience over time. Routine Measurement A plan specification that organises the processes, roles and governance needed to operate a Learning Health System, including its supporting pillars and cross-cutting themes. SMARTLHS Framework A process that ensures the data itself are appropriately de-identified, minimised, curated, and managed so that the risk of re-identification or misuse is reduced to an acceptable level. https://www.abs.gov.au/about/data-services/data-confidentiality-guide/five-safes-framework Focuses on data privacy, confidentiality, and compliance with supplier requirements. Mitigates risks related to data handling and sharing. Includes techniques such as data minimisation, aggregation, perturbation, suppression, metadata quality checks, and sensitivity review. Safe Data Process A process that reviews and vets analytical results, reports, visualisations, and other outputs to ensure that they do not inadvertently disclose identifiable or sensitive information. https://www.abs.gov.au/about/data-services/data-confidentiality-guide/five-safes-framework Ensures that research outputs (such as publications or reports) protect privacy and confidentiality. Balances transparency with data sensitivity12. Includes disclosure control, small-cell suppression, context checking, and risk review before publication or release. Safe Outputs Process A process that verifies the trustworthiness, training, capability, and authorisation of individuals accessing data, ensuring they understand and adhere to ethical, privacy, and security obligations. https://www.abs.gov.au/about/data-services/data-confidentiality-guide/five-safes-framework Ensures that researchers accessing and using the data are appropriately authorized. Addresses legal, ethical, and public benefit considerations. Includes mandatory training, accreditation, role verification, agreements, and competency assessment. Safe People Process A process that assesses whether the proposed data use is appropriate, ethical, justified, and aligned with public benefit, organisational policy, and Learning Health System goals. https://www.abs.gov.au/about/data-services/data-confidentiality-guide/five-safes-framework Evaluates whether the proposed project aligns with legal and ethical requirements. Assesses the expected public benefits relative to the associated risks. Includes reviewing purpose, necessity, ethical approval, benefits vs. risks, feasibility, and alignment with organisational values. Safe Projects Process A process that ensures data are accessed, stored, or analysed within secure environments that mitigate risks of unauthorised access, misuse, or data leakage. https://www.abs.gov.au/about/data-services/data-confidentiality-guide/five-safes-framework Considers the security infrastructure and control where data access occurs. Includes secure remote access or managed physical locations. Includes IT infrastructure security, physical controls, VPN/secure labs, access restrictions, audit logs, and monitoring. Safe Settings Process A process that designs and tests small-scale interventions in a controlled environment where failure is anticipated, monitored, and used as a source of learning. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Structured pilot tests or small experiments designed to explore potential improvements with minimal risk, allowing learning from failures without severe impact on patient care or operations. Safe-to-fail Experimentation Process A process that establishes criteria for assessing the safety, efficacy, reliability, and real-world performance of interventions, technologies, and learning activities. Benchmarks to ensure clinical interventions, devices, and pharmaceuticals meet acceptable risk-benefit ratios for patient well-being. Safety And Effectiveness Criteria Process A process that cultivates honesty, rigor, transparency, and methodological soundness in all learning, evaluation, and research activities. Menear, M., et al., A framework for value-creating learning health systems. Health research policy and systems, 2019. 17(1): p. 79-79. The LHS and its participants will share a commitment to the most rigorous application of science to ensure the validity and credibility of findings Scientific Integrity A process that eliminates fragmentation in healthcare delivery by ensuring smooth, coordinated interaction among teams, services, and systems. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The provision of unified, streamlined healthcare services that reduce fragmentation and barriers, allowing patients to smoothly transition between different care settings or providers Seamless Service Delivery A process that protects health data against unauthorised access, misuse, or loss while ensuring ethical, legal, and transparent data governance. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Data security and privacy refer to measures and practices aimed at safeguarding sensitive data from unauthorized access, disclosure, alteration, or destruction while ensuring that individuals' personal information is protected and used appropriately. Protecting sensitive health information can be a significant concern In WHS, data security and privacy assume paramount importance as the field navigates the integration of diverse data sources and innovative technologies to provide holistic and patient-centered care. For example, the interconnectedness of electronic health records, wearable devices, and PRO necessitates rigorous data security measures to shield sensitive health information from breaches.The challenge lies in ensuring that while these WHS practices harness the potential of advanced technologies, patients' data remains safeguarded in compliance with privacy regulations. Moreover, respecting individuals' privacy rights and gaining their trust are essential to maintaining the integrity of WHS, where the holistic well-being of the patient is not only the goal but also a foundational principle. WHS providers are tasked with striking a delicate balance, harnessing the benefits of data-driven insights while upholding the patient-centered approach that defines their practice, thus underscoring the critical importance of data security and privacy in this evolving healthcare landscape. Protecting patient privacy and ensuring data security are paramount. Data governance frameworks and compliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA) are necessary to safeguard sensitive health information. Security and Privacy Management Process A process that supports patients in managing their conditions, treatments, behaviours, and lifestyle decisions. The active role patients play in handling their own health conditions, including adherence to treatment plans, lifestyle adjustments, and monitoring symptoms. Self Management A process that distributes responsibility across teams, departments, and leadership groups to support collective ownership of outcomes and improvement. Menear, M., et al., A framework for value-creating learning health systems. Health research policy and systems, 2019. 17(1): p. 79-79. A principle whereby all stakeholders (e.g., clinicians, administrators, payors, patients) collectively assume responsibility for achieving outcomes and upholding standards Shared Accountability Proccess A process that involves patients and clinicians working together to make informed choices that reflect patient preferences and clinical evidence. A collaborative approach in which patients and healthcare providers jointly make care decisions, incorporating patient values, preferences, and goals alongside clinical evidence Shared Care Decisions A process where teams share timely, relevant patient information enabled by digital platforms to support integrated decision-making. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Dedicated teams responsible for managing and facilitating the exchange of patient health data across systems, providers, or organizations, ensuring timely and accurate information is available for integrated care decisions. Shared Health Information Teams A process that examines the common mental models, viewpoints, problem-solving approaches, and patterns of reasoning that influence how individuals and groups understand situations and respond to challenges within the organisation. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Values and beliefs that justify the visible manifestations Shared Ways Of Thinking A process that decreases hospital stay duration through coordinated, evidence-based, and efficient care pathways. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Improved Efficiency: Shorter hospital stays optimize resource utilisation and bed availability. Patients spend less time in the hospital, freeing up capacity for others. Cost Savings: Reduced length of stay lowers overall healthcare costs. It minimizes expenses related to hospitalization and care delivery refers to reducing the number of days that an in-patient remains in the hospital Shorter Length Of Stay A process that coordinates healthcare activities with social services to address patient needs related to housing, food, safety, or financial stability. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. The systematic coordination between healthcare providers and social service agencies—such as housing, welfare, mental health services—to deliver comprehensive and continuous support that addresses both medical and non-medical patient needs Social Services Integration A process that applies software engineering standards—such as version control, testing, quality assurance, documentation, and security—for digital health tool development. Established coding practices, testing protocols, and quality metrics specific to healthcare software, ensuring reliability and regulatory compliance.” Software Development Standards Process A process that fosters mutual support, shared responsibility, and collective purpose across the organisation to strengthen resilience and cohesion. Menear, M., et al., A framework for value-creating learning health systems. Health research policy and systems, 2019. 17(1): p. 79-79. All aspects of the LHS will be open and transparent to safeguard and deepen the trust of all stakeholders Solidarity A process that involves internal and external stakeholders—including patients—in governance decisions to support transparency, legitimacy, and shared oversight. Actively involving individuals and groups—such as patients, staff, and community partners—in governance processes and decision-making. Stakeholder Engagement Proccess A process that involves patients, clinicians, leaders, and communities in designing, conducting, interpreting, and using evaluation results. Collaborative Evaluation: Engaging patients, clinicians, and administrators in defining metrics and interpreting findings. Transparent Reporting: Sharing results with stakeholders in an accessible format to build trust and guide decisions. Stakeholder Engagement In Evaluation Process A planned process that develops, applies, and maintains agreed-upon norms, specifications, or criteria to ensure quality, safety, interoperability, usability, ethical conduct, and consistency across Learning Health System activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Standards are agreed-upon norms or criteria within the LHS that define the quality, performance, or interoperability requirements for various processes, data, or systems. They ensure consistency and reliability in the services and research within the LHS. Standards can be about technical aspects (like data formats, coding systems), clinical practices (treatment protocols, care pathways), or ethical considerations (consent procedures, privacy protections). Standards A process through which healthcare organisations define, align, and implement strategic priorities, policies, and incentive mechanisms that drive improvement and learning across the health system. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Strategies in a Learning Health System refer to the deliberate and coordinated efforts aimed at creating an environment where continuous learning, evidence-based practice, and systematic improvement are integral to healthcare delivery. These strategies involve the integration of various elements, including technology, data analytics, patient engagement, and organizational culture, to achieve the goals of an LHS. Strategic Planning Process A process that formulates, aligns, and reviews strategic priorities to embed continuous learning into organisational governance. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Definition: Strategy staff are professionals responsible for developing and implementing the strategic plans of healthcare organizations. They align organizational goals with the principles of a Learning Health System. Role: They identify priorities, allocate resources, and oversee initiatives that promote continuous learning and improvement. They work to ensure that the organization's strategic direction supports high-quality, patient-centered care and the effective use of data and technology. Strategy Staff Workforce Process A process that applies predetermined payment structures to encourage consistent, value-aligned care delivery. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Structure payment to reward continuous learning and improvement in the provision of best care at lower cost. Payers should structure pay-ment models, contracting policies, and benefit designs to reward care that effective and efficient and continuously learns and improves. Structured Payment A planning process that ensures sustainable distribution of financial, human, and technological resources aligned with organisational goals and Learning Health System priorities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Ensuring that sufficient resources are allocated to support the infrastructure, technology, and human capital needed for a Learning Health System. This involves strategic investment in health IT, research, and quality improvement initiatives. Sufficient Resource Allocation A process that enables healthier behaviours through coaching, education, motivational interviewing, and supportive systems. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. By involving patients in their care, healthcare providers can better support behavioral changes that are crucial for managing chronic diseases. This includes promoting healthier lifestyles and adherence to preventive measures . Support For Behavioral Change A plan specification that enumerates the foundational operational processes an LHS must execute. The topclass have main foundational domains for an LHS. Supporting Pillars A process that ensures leaders provide the resources, tools, and organisational conditions required for learning and improvement. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Providing the necessary infrastructure to support continuous learning and improvement is a key leadership responsibility. This includes investing in health information technology, data analytics, and other tools that enable the organization to learn and adapt. Supportive Infrastructure Process A process that develops organisational skills in systems thinking, complexity science, modelling, and whole-system analysis. A process that develops understanding of systems thinking, complexity, and adaptive learning to support LHS design. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. https://www.ahrq.gov/learning-health-systems/building-workforce.html Competencies Demonstrate knowledge of how systems theories can be used to understand how the interactions of the parts of health systems operate to produce value for stakeholders. Demonstrate systems thinking in the design and conduct of research and implementation of its findings within the context of complex health systems. Demonstrate knowledge of the financing, organization, delivery, and outcomes of health care services and their interrelationships. Demonstrate the ability to assess the extent to which research activities will likely contribute to the quality, equity, or value of health systems. Core Competencies for Learning Health System (LHS) Investigators Understanding how health systems operate and applying systems theory to research and implementation. System Science Capacity Building Process Analyzing adverse events informs system improvements and prevents future errors34. System Wide Learning A process that involves patients in advisory boards, policymaking, and system-wide strategic planning to improve health system performance. Involvement of patients and patient representatives in policy-making, financing, and structural decisions at the healthcare system or governmental level, ensuring patient perspectives shape broader health reforms System Level Engagement A process that cultivates structured, analytical approaches to identifying, understanding, and addressing problems, while encouraging experimentation and rapid-cycle learning. Structured methods (e.g., PDSA cycles, Lean, Six Sigma) for diagnosing issues, testing solutions, measuring outcomes, and refining interventions to continuously improve care processes. Systematic Problem Solving And Experimentation Brooks, D., et al. (2017). "Developing a framework for integrating health equity into the learning health system." Learning health systems 1(3): n/a. The intervention must be evidence‐based, driven by the data, and targeted to address disparities. Data collection may include both qualitative and quantitative methods. Causes of health disparities and solutions can be gained from the community, including individual factors and beliefs contributing to health inequities and the role that SDH play. Quantitative data may come from a variety of sources, including EHR, insurance claims, public health departments, census surveys, patient surveys, and other sources. Individual‐level data must be collected with informed patient consent, and abide by relevant privacy and security regulations. Individuals from underserved communities may prefer more in‐depth explanations of how their data will be used, who will see it, and how it will be protected. The necessary level of disclosure, appropriate communication mechanisms, and transparency should be guided by individuals serving in governance and leadership capacities but will ultimately be determined by the individuals providing the data. The scientific literature should be reviewed to identify effective strategies, keeping in mind that by nature, health disparities manifest differently in different communities. Thus, community input will be needed to fine tune and execute an effective strategy or intervention. Target Health Disparities Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Focused actions or programs directed at population segments with elevated health risks, aiming to prevent disease progression, improve outcomes, or reduce healthcare costs. Targeted Interventions For Highrisk Goups A process in which teams collectively learn through shared reflection, simulation, training, and iterative improvement activities. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Team Based Learning A process that designs, implements, and integrates the technical, procedural, and data infrastructure required to support effective organisational change and learning activities. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. An early analysis of these systems, including strengths, gaps and opportunities for development, is therefore often a prerequisite to developing the technical building blocks. Resources are always limited and this analysis can help to prioritise where they can have most impact. Five Core Aspects of LHS Once an organisation(s) understands why it is developing a Learning Health System and begins to appreciate the complexity and how change will be delivered, it can develop the technical building blocks required. Technical Building Blocks Process A process that manages the design, maintenance, and scalability of physical and virtual IT assets (networks, servers, cloud platforms) supporting learning activities. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. These technologies collectively enable the continuous collection, analysis, and application of health data, forming the backbone of a Learning Health System that aims to improve healthcare quality, efficiency, and outcomes. Technology Management Process Olsen, L., D. Aisner, and J.M. McGinnis, The learning healthcare system workshop summary. 2007, Washington, DC: National Academies Press. While standardization will make data amenable to comprehensive data analysis, an even greater challenge will be to create a learning system in which meaningful insights emerge organically. Currently, data mining isthe source of hundreds of associations, but analysis is most efficiently done [3]when a particular hypothesis is pursued.[3] Data collection must be embedded within a healthcare system, like Kaiser’s, that can serve as a prepared mind—an environment in which managers and clinicians are trained to discern patterns of interest and work within a system with the inclination, resources, and capacity to act upon such findings. [3] the presence of embedded researchers who themselves were active clinicians, managers, policy makers, and developers of VistA-CPRS; a[3] Regulators and patients and their families should encourage AI developers, health system leaders, clinical users, and informatics and health IT experts to evaluate deployed clinical AI for effectiveness and safety based on clinical data. Artificial Intelligence in Health Care A Report From the National Academy of Medicine A process that designs and deploys digital tools, software, and platforms supporting data-driven learning and automation. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Professionals with expertise in health information technology, including EHR system administrators, software developers, and IT support staff. They are essential for implementing and maintaining the digital infrastructure of a Learning Health System. To help achieve a learning health care system, digital technology developers need to play the following roles: • Ensure that electronic health record systems and other digital technologies capture and deliver the core data elements needed to support knowledge generation. • Partner with patients, the delivery system, insurers, researchers, innovators, regulators, and other stakeholders. • Collaborate in the development of core datasets for different diseases and conditions to support clinical care, improvement, and research. • Develop tools that assist individuals in managing their health and health care and that provide opportunities for building communities to support patient efforts. • Consider interoperability and integration in clinical workflows in designing digital health systems. Technology Developer Workforce Process Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Telehealth technologies facilitate remote patient monitoring, virtual consultations, and the delivery of care through digital platforms. These technologies expand access to care, especially in underserved areas, and support continuous patient engagement. Telehealth And Telemedicine A process that assesses and supports the readiness, capabilities, behaviours, and motivations of individuals and teams who are expected to adopt, use, or sustain new practices, technologies, or models of care within a complex health system. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Includes sense-making, behavioural responses, incentives, social influence, and professional norms that shape adoption and implementation. Patients and carers may abandon a systemthat makes unreasonable demands on them. Clinicians mayresist a system that threatens their job or professional identity The Adopter System A process that manages and evaluates the characteristics of the clinical condition or problem space—including complexity, variability, burden, trajectory, and population needs—to guide system design, service delivery, and learning priorities. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Reflects how the nature of the health problem influences the complexity of decision-making, model design, and required organisational responses. Some illnesses are more complex than others, interms of aetiology, prognosis, sociocultural factors, com-orbidities or treatments. It is more challenging to create sys-tems that manage complex conditions. The Condition A process that applies Friedman’s LHS learning cycle—data → knowledge → performance—to drive continuous system improvement. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. e learning cycle referred to by Friedman involves a systematic process for continuous improvement and learning. While the book itself may not directly outline this cycle, it is commonly understood in the following steps: Data Collection: Gather data from clinical practice and patient outcomes through electronic health records (EHRs), patient surveys, and other health information systems. Data Analysis: Analyze the collected data to identify trends, patterns, and areas for improvement. This involves using statistical methods, data analytics, and machine learning techniques. Knowledge Generation: Convert analyzed data into actionable knowledge. This includes developing clinical guidelines, best practices, and evidence-based recommendations. Implementation: Apply the generated knowledge in clinical practice. This involves training healthcare providers, updating protocols, and integrating new practices into the healthcare delivery process. Evaluation: Assess the impact of the implemented changes on patient outcomes, care quality, and system efficiency. Collect feedback and additional data to determine the effectiveness of the interventions. Feedback and Adaptation: Use the evaluation results to refine and adapt practices. Continuously loop back to the data collection step to ensure ongoing improvement and learning. The Learning Cycle Proposed By Friedman A process that examines and strengthens the organisational structures, culture, workflows, governance mechanisms, and operational capabilities that influence how a health system responds to complexity and supports continuous learning. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Includes leadership behaviours, collaboration structures, resource flows, and the organisation’s capacity to adapt and change. The organisation's capacity for innovationand its other priorities will influence its ability to develop aLearning Health System The Organisation A process where the organisation integrates evidence-based treatments, guidelines, or tools into care delivery. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Organisation Adopts or Implements Evidence Based Treatments A process in which the organisation develops new evidence through repeated learning cycles, evaluations, and research activities aligned with strategic objectives. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Organisation Builds Knowledge Or Evidence A process where the organisation undertakes formal research activities, including study design, data collection, analysis, and reporting. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Organisation Conducts Research A process that places trained researchers within clinical or operational teams to support real-time learning and evaluation. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Organisation Employs Embedded Researchers A process that establishes formal units dedicated to research, analytics, learning, improvement science, or implementation support. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Organisation Has A Dedicate Center Or Instiute For LHS Work A process where internal operational budgets are allocated to support research, evaluation, evidence generation, and innovation. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Organisation Invests Its Own Funds To Conduct Research A process that uses structured, consistent, and replicable methods to implement changes based on evidence. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Organisation Is Systematic In Its Implementation Processes A process that conducts applied research that is feasible within operational constraints while producing useful, practice-relevant insights. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. The Research Conducted Is Practical Or Needs To Balance Practical With Rigorus A process that considers the design, implementation, integration, usability, and real-world performance of technologies within a complex adaptive system, ensuring that digital tools support learning, decision-making, and adaptive care delivery. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Includes interoperability, workflow fit, technical readiness, human-technology interaction, and sociotechnical influences. Usability, dependability, maturity and require-ments to integrate with other systems can all impact success The Technology A process that evaluates and aligns the benefits, outcomes, costs, and motivations associated with a Learning Health System initiative to ensure that the value created is meaningful for patients, clinicians, organisations, and the broader system. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. The intervention must deliver value tothe developer, the organisation, the patient, etc. or it will notbe maintained or used The Value Proposition A process that monitors and interprets external contextual factors—such as policy, regulation, funding, social expectations, workforce markets, and community conditions—that influence how a Learning Health System adapts and evolves. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Laws, regulation, politics, market conditions andother events can intervene to impact a Learning Health System The Wider Context A process that builds cultural capability through formal and informal training, education, and skill development initiatives that support learning and improvement competencies. Formal or informal initiatives designed to build knowledge, skills, and competencies among staff, equipping them to engage effectively in continuous learning and improvement activities. Training And Educational Program A process that creates policies ensuring open reporting, responsible conduct, and accountability mechanisms. A process that publicly reports decisions, data practices, evaluation outcomes, and governance actions to build trust and ensure accountability in the Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Data Provenance: Metadata about the origin and transformation of data (who collected it, when, and how). Documentation of data lineage to ensure traceability. Audit Trails: Logs of decision-making processes related to data ethics. Records of system activities for accountability and oversight. Ensuring that healthcare organizations are transparent about their performance and accountable for their outcomes. This includes reporting on quality metrics and outcomes to stakeholders and the public. Transparency And Accountability Policy Process A process that builds and maintains trust by promoting openness, accountability, and visibility of decisions, actions, and learning outcomes. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Building an atmosphere where information is openly shared and stakeholders can trust that their contributions and concerns are valued. Full transparency—A learning health care system systematically monitors the safety, quality, processes, prices, costs, and outcomes of care, and makes information available for care improvement and informed choices and decision making by clinicians, patients, and their families. Transparency And Trust A process that monitors and addresses disparities in data representation, ensuring that datasets and analyses reflect equitable population coverage and support inclusive learning. Angela Melder, T.R., Ian Mcloughlin, Rick Ledema and Helena Teede, An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system. Royal Australasian College of Physicians, 2020. Equity and Transparency in Data Use Process A process that ensures leaders disclose decisions, performance, risks, and outcomes openly to maintain system trust and accountability. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Transparency in reporting outcomes and accountability for results are critical elements. Leaders must ensure that performance metrics are openly shared and that there is accountability for achieving the set goals and standards. Transparency And Accountability Process A process that disseminates governance decisions, rationales, and performance results openly to build trust and ensure accountability. Openly sharing information about decisions, performance metrics, and risks, fostering trust and collaborative problem-solving. Transparent Communication Process A process that deepens organisational learning by questioning not just actions (single loop) and assumptions (double loop) but also underlying values, culture, and governance structures. Easterling, D., et al., Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature. Learning health systems, 2022. 6(2): p. e10287-n/a. Triple Loop Learning A planned process that identifies, produces and evaluates value for consumers, clinicians, organisations and populations from LHS activities. The Vallue Creation and Benefits component of a Learning Health System (LHS) framework ensures that the benefits and incentives for LHS are clearly identified to support seven pillars of LHS framework. Value Creation And Benefits Process A process that documents and interprets the observable expressions of organisational culture, such as behaviours, rituals, routines, language, symbols, documents, tools, and physical layout. Foley, T. and L. Vale (2023). "A framework for understanding, designing, developing and evaluating learning health systems." Learning health systems 7(1): e10315-n/a. Professional roles, physical layout of facili-ties, pathways of care, etc. Visible Manifestations A process where leaders articulate a clear vision, strategic direction, and long-term goals that guide the Learning Health System’s evolution. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Leaders must set a clear vision and strategic direction for the organization. This includes articulating the goals of the Learning Health System and ensuring that all members of the organization understand and are committed to these objectives. Vision And Strategy Process A process that develops dashboards, indicators, and visual tools to communicate key ethical and oversight metrics such as consent rates, privacy controls, compliance status, and risk profiles. To operationalize these elements: Dashboards for real-time tracking of privacy, consent, and compliance metrics. Workflow Automation for ethical approvals and audit logs. AI Tools for identifying risks such as bias and security vulnerabilities. Visualizing Ethics And Oversight Data Elements Process A process that identifies and manages issues associated with high-volume, high-velocity, or complex data that exceed system processing or management capacity. Guo, L., et al. (2024). "Enhancing data practices for Whole Health: Strategies for a transformative future." Learning health systems 8(Suppl 1): e10426-n/a. Data volume and handling refer to the quantity and management of data, respectively, focusing on efficiently storing, processing, and analyzing large amounts of data. WHS practices generate substantial amounts of data due to a holistic approach, which considers not only medical records but also PRO, lifestyle factors, and complementary therapies0 This complex data collection provides a rich source of information for healthcare analytics, enabling personalized treatment plans and data-driven insights to improve patient outcomes. Still, it also poses a unique challenge within the context of WHS. This challenge becomes particularly pronounced as the field embraces the emerging trend of incorporating new technologies such as telehealth,wearable health devices, and digital health,34 which are all integral to fueling AI-driven insights. As practitioners navigate this data-rich landscape, they face the challenge of unlocking the full potential of the abundance of collected data while upholding the core principles of WHS, where the patient's well-being remains at the forefront of care. Volume And Handling Process A collective process that identifies, engages, and supports diverse professional roles contributing to the operation and advancement of Learning Health Systems. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Represents role-based workforce segments as participants in learning processes, not as individuals (so, processes are still valid in BFO). Each subclass reflects a specialised contribution to LHS capability. the "workforce" is broadly defined as a multidisciplinary team that brings together a diverse range of people, skills, specialisms, and subject matter expertise. This team is essential for the functioning of an LHS and typically includes patients, clinicians, researchers, designers, strategy and operational delivery staff, information analysts, and other support professionals. The composition of the team varies depending on the nature of the work and may include not only directly employed staff but also contractors, consultants, and others employed by platform providers. Leadership and organizational understanding and buy-in are crucial, given the scope of an LHS​​.(https://learninghealthcareproject.org/strategy/workforce/) Workforce Process A planned process that develops, sustains, and mobilises a skilled, adaptable, and interdisciplinary workforce capable of driving and sustaining learning and improvement within a Learning Health System. Efforts aimed at developing and maintaining a skilled, adaptable, and adequately resourced healthcare workforce capable of advancing a Learning Health System Workforce And Capacity Building Process A strategic process that plans, educates, and empowers the health workforce to build capabilities in data literacy, quality improvement, and adaptive leadership required for a Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press. Investing in the education and training of healthcare professionals to equip them with the skills needed to participate in continuous learning and improvement activities. This includes training in data analytics, quality improvement, and patient engagement techniques. Workforce Development A process that develops, assesses, and applies the key knowledge, skills, and competencies required to operate, sustain, and evolve a Learning Health System. Smith, M. D. (2013). Best care at lower cost : the path to continuously learning health care in America. Washington, District of Columbia, National Academies Press Each subclass below represents a specific competency development process that occurs through training, education, and reflective practice. These are correctly occurrent (they unfold over time). Workforce Skillsets Patient-Reported Experience Measures (PREMs): PREMs collect information about patients’ views and observations regarding their experiences with health services. These measures assess aspects such as accessibility, waiting times, cleanliness, and patient-clinician interactions. PREMs inform ongoing improvements in healthcare quality and service delivery1. Patient-Reported Experience Measures (PREMs) PROMs focus on patients’ perceptions of their health status. Questionnaires capture information on quality of life, daily functioning, symptoms, and overall well-being. PROMs provide valuable insights for tracking patient progress and evaluating outcomes of care2. Patient-Reported Outcome Measures (PROMs)