ABSTRACT
Background
Learning health system (LHS) frameworks have been presented in multiple forms, but there is no standardized representation that captures both their core concepts and the relationships among them. This limits their practical use by health services seeking to design, implement, and evaluate LHS capabilities in changing sociotechnical environments. This study developed the Smart Learning Health System ontology (SMARTLHS), a formal semantic framework that synthesizes concepts and relationships from published LHS frameworks.
Methods
Relevant literature was identified through a systematic database search. SMARTLHS was developed using an iterative system mapping and ontology engineering approach, comprising five steps: (1) ontology requirements specification, (2) iterative ontology conceptualization, (3) concept and relationship comparison and formalization, (4) ontology evaluation and refinement, and (5) ontology alignment with foundational ontology, to ensure semantic consistency and interoperability. The SMARTLHS ontology was implemented using the Web Ontology Language (OWL) and developed in Protégé.
Results
SMARTLHS contains 447 classes and 134 object properties. Its structure is organized around four high‐level groupings: LHSs, supporting pillars, cross‐cutting themes, and common reference LHS frameworks. The supporting pillars comprise seven domains: Care delivery and organizational strategies, community and patient engagement, culture and change management, data and technology infrastructure, governance and leadership, research and education, and workforce and capacity building. The cross‐cutting themes include ethics and oversight, evaluation and methodology, and value creation and benefits. Together, these classes and relationships provide a computable semantic representation of LHS implementation.
Conclusions
SMARTLHS addresses key limitations of existing LHS frameworks by transforming narrative framework concepts into a formal, extensible semantic knowledge model. By explicitly representing both concepts and relationships, it provides a foundation for organizational assessment, semantic interoperability, and future AI‐enabled applications, including ontology‐guided retrieval and Retrieval‐Augmented Generation (RAG).
Keywords: framework, knowledge graph, learning health system, ontology
1. Introduction
Learning health systems (LHSs) have become an important concept for health services seeking to continuously improve care through routine use of data, evidence, and feedback [1, 2]. The Institute of Medicine described an LHS as a system in which informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices embedded in care processes and patients and providers working together in a coordinated way [3]. This vision has become increasingly relevant as healthcare organizations invest in digital infrastructure, analytics, and system‐wide transformation [4].
However, translating the LHS concept into practice remains difficult. Published LHS frameworks describe similar ideas using different structures, terminology, and emphases. Some focus on technical infrastructure; others on values, governance, or improvement science [5]. As a result, there is no widely adopted, computable representation that consistently captures the key elements of an LHS and the relationships among them [6].
Two limitations are particularly important. First, most existing LHS frameworks are presented as narrative descriptions, diagrams, or tables [7, 8, 9, 10, 11, 12, 13]. These formats are useful for communication but are not easily updated as new sociotechnical capabilities emerge, including advances in artificial intelligence, digital governance, and workforce models [5]. Second, those frameworks do not adequately represent the complexity of interrelationships among LHS elements. In practice, governance, culture, data, technology, workforce capability, and evaluation do not operate independently; they interact in dynamic and overlapping ways.
Ontology offers a way to address these limitations [14]. In healthcare, ontologies are already used to provide a formal representation of domain concepts, definitions, and relationships and to support shared meaning and data integration across complex domains [15]. Applying this approach to LHSs offers the possibility of moving from descriptive frameworks to a reusable, computable knowledge model [16, 17, 18, 19].
This study therefore aimed to develop the Smart Learning Health System ontology (SMARTLHS): an ontology‐based framework that synthesizes key LHS concepts and their relationships from the literature and provides a formal structure for future organizational assessment, semantic querying, and retrieval‐augmented applications [19, 20, 21, 22, 23, 24].
2. Background
Published LHS frameworks describe complementary perspectives on the organizational, technical, and sociotechnical capabilities required to establish a LHS. However, they differ considerably in their emphasis, terminology, and intended application. Table 1 compares representative LHS frameworks and highlights their differing conceptual emphases, illustrating the breadth of organizational, technical, and sociotechnical concepts reported in the literature.
TABLE 1.
Comparison of representative learning health system frameworks.
| Framework | Primary focus | Representative concepts/components |
|---|---|---|
| Bailey‐Davis et al. [7] | Operational implementation | Data and analytics; people and partnerships; patient and family engagement; ethics and oversight; evaluation and methodology; funding; organization; prioritization; deliverables |
| Lessard et al. [8] | Architectural framework | Goal, scientific, social, technical, ethical and organizational dimensions across multiple decision layers |
| Menear et al. [9] | Value‐creating LHS | LHS pillars, learning processes, knowledge management, ecosystems of change, value creation, outcomes |
| Melder et al. [11] | Healthcare improvement | Stakeholder engagement; priority setting; evidence generation; data systems; benchmarking; implementation; healthcare improvement; culture; standards; people; resources |
| Other contemporary frameworks [10, 12, 25, 26] | Emerging implementation capabilities | Leadership, trust, workforce capability, implementation science, system integration, equity, AI Governance |
Although these frameworks have advanced the field, healthcare organizations increasingly require computable knowledge models that can support organizational assessment, semantic integration of policies and strategies, AI‐assisted knowledge retrieval, and consistent interpretation across multidisciplinary stakeholders.
System mapping and ontology are complementary approaches for developing such a model [27]. System mapping helps identify the components and interactions within a complex domain, while ontology formalizes these concepts into classes and relationships that can be shared, queried, and reused [28, 29]. Ontologies also provide a foundation for knowledge graph applications by supplying standardized definitions and explicit semantics [14]. In biomedical domains, this is strengthened through alignment with established Open Biomedical Ontologies (OBO), including the Basic Formal Ontology (BFO), Relation Ontology (RO), Information Artifact Ontology (IAO), and Ontology for Biomedical Investigations (OBI) [20, 21, 22]. These resources support the development of an ontology‐based knowledge model for LHSs by providing clear distinctions between material entities, processes, and information artifacts, while enabling integration with broader biomedical knowledge resources [23].
3. Methods
SMARTLHS was developed using an iterative system mapping and ontology engineering approach. Concept identification and refinement were guided by the constant comparative method of grounded theory, enabling repeated comparison of concepts identified in the literature, refinement of class structures, and consolidation of overlapping terms and relationships throughout ontology development [30].
A systematic database search was conducted using the terms “learning health system,” “learning health systems,” and “learning health systems framework.” Searches were undertaken in PubMed with Full Text, ProQuest Central with Full Text, and MEDLINE with Full Text for publications from January 2004 to July 2024. A total of 1337 records were retrieved. Ninety‐seven articles were reviewed to gain broader insight into the components and implementation of LHSs. Of these, 18 papers that specifically addressed LHS frameworks in hospital and academic healthcare settings were selected for the iterative ontology development process.
SMARTLHS was developed in Protégé (v5.5.0) and implemented in OWL. Development proceeded through five steps [31].
First, ontology requirements were specified. SMARTLHS was intended to provide a knowledge model of the LHS framework for health services, capture concepts and relationships relevant to planning and operationalizing LHSs, support future integration with large language model applications, and address competency questions (as shown in Table 2) relevant to healthcare leaders and digital health professionals [32].
TABLE 2.
Competency questions for SMARTLHS to answer.
| CQ | Competency questions (CQ) |
|---|---|
| 1 | What are the fundamental components of a LHS? |
| 2 | What benefits and values are derived from implementing a LHS? |
| 3 | What roles do patients and their families play within a LHS? |
| 4 | How do organizational culture and leadership influence the effectiveness of a LHS? |
| 5 | Who are the key stakeholders involved in a LHS? |
| 6 | What essential skills are required for the workforce within a LHS? |
| 7 | What are the critical technologies required to enable the successful implementation and operationalization of a LHS? |
| 8 | How are data security and privacy managed within a LHS? |
| 9 | What roles does governance play in supporting a LHS? |
| 10 | What type of standards are required in a LHS? |
| 11 | How is a patient‐centered model of care facilitated within a LHS? |
| 12 | What strategies are effective in building a learning healthcare organization? |
Second, system mapping and ontology conceptualization was undertaken. Concepts, organizational components, stakeholders, processes, technologies, governance mechanisms, and relationships described in the literature were progressively identified and organized into an evolving conceptual system map. Through repeated comparison across publications, overlapping concepts were consolidated, relationships clarified, and emerging components incorporated. This conceptual system map subsequently provided the basis for formal ontology classes, subclass hierarchies, and object properties [33].
Third, the developing ontology was compared against published frameworks from 2004 to 2024 [7, 8, 9, 10, 11, 12, 13, 26, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]. Equivalent concepts were consolidated, redundant concepts merged, and new concepts incorporated where required. Tables S1 and S2 summarize the iterative refinement process and resulting structural changes.
Fourth, the ontology was evaluated iteratively and refined throughout the development using three approaches (as shown in Table S3). Domain experts provided feedback on content, structure, and prospective use cases. Ontology rigor was assessed through review of scope, naming consistency, definitions, logic, and reasoning validity using the HermiT reasoner. Twelve competency questions were evaluated using SPARQL queries to determine whether the ontology could retrieve the intended concepts, definitions, hierarchical relationships, and semantic associations (as shown in Table S4).
Ontology statistics were generated programmatically from the released SMARTLHSONTO ontology using a reproducible Python script (Table S5).
Fifth, the ontology was realigned with foundational ontology principles, particularly BFO. Earlier versions had treated the LHS as a broad conceptual parent for multiple types of entities. During realignment, distinctions were introduced between continuants and occurrents. LHS was modeled as a sociotechnical system, learning health organizations as organizational entities, supporting pillars, and cross‐cutting themes as planned process classes, and learning use‐case specifications as information content entities [21]. Relationships between systems, processes, and specifications were represented through object properties rather than subclassing. This strengthened semantic clarity and improved interoperability with OBO‐aligned ontologies [44].
4. Results
SMARTLHS contains 447 classes linked by 134 object properties (the released merged ontology contains 498 OWL classes and 142 object properties, including imported BFO/OBO terms. Statistics reported in this paper refer to SMARTLHSONTO‐defined entities only). Following realignment, SMARTLHS is organized around the LHS as the overarching sociotechnical system, supported by three conceptual groupings: Supporting pillars, cross‐cutting themes, and common reference LHS frameworks (Figure 1).
FIGURE 1.
The Smart Learning Health System (SMARTLHS) framework.
The supporting pillars comprise seven organizational domains, while the cross‐cutting themes provide system‐wide processes that interact across all pillars. Common reference LHS frameworks such as Menear, Foley, PETAL, and Monash Melder are represented as information content entities.
Class distribution within SMARTLHS is uneven, reflecting the detail available in the literature (Table 3). Care delivery and organizational strategies contains the greatest number of subclasses, followed by governance and leadership, culture and change management, and data and technology infrastructure. This indicates that the literature places strong emphasis on organizational design, governance, change management, and digital capability as major enablers of LHS implementation. In contrast, Community and Patient Engagement and Research and Education contain fewer classes, reflecting comparatively less detailed conceptualization within the published literature rather than reduced importance within the SMARTLHS framework. The hierarchy levels shown in Table 3 correspond to successive layers of semantic specialization, progressing from high‐level conceptual domains to intermediate concept groups and finally to specialized ontology classes. Figure S1 provides an example of this hierarchical organization for the Data and Technology Infrastructure supporting pillar.
TABLE 3.
Conceptual hierarchical distribution of classes in the SMARTLHS ontology.
| Classes | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 | Level 6 | Level 7 | Level 8 | No. of classes (%) |
|---|---|---|---|---|---|---|---|---|---|
| Core SMARTLHS classes | 3 | 8 | 11 (2.46%) | ||||||
| Learning health systems | 1 | 1 (0.22%) | |||||||
| Learning health organizations | 1 | 8 | 9 (2.01%) | ||||||
| SMARTLHS framework | 1 | 1 (0.22%) | |||||||
| Supporting pillars | 1 | 7 | 31 | 136 | 136 | 30 | 7 | 3 | 351 (78.52%) |
| Care delivery and organizational strategies process | 1 | 3 | 25 | 44 | 8 | 7 | 3 | 91 (20.36%) | |
| Consumer and community engagement process | 1 | 7 | 11 | 19 (4.25%) | |||||
| Culture and change management process | 1 | 4 | 27 | 16 | 9 | 57 (12.75%) | |||
| Data and technology infrastructure process | 1 | 2 | 14 | 26 | 13 | 56 (12.53%) | |||
| Governance and leadership process | 1 | 4 | 18 | 45 | 68 (15.21%) | ||||
| Research and education process | 1 | 9 | 13 | 4 | 27 (6.04%) | ||||
| Workforce and capacity building process | 1 | 2 | 28 | 1 | 32 (7.16%) | ||||
| Cross‐cutting themes | 1 | 3 | 21 | 14 | 16 | 8 | 63 (14.09%) | ||
| Value creation and benefits process | 1 | 2 | 14 | 16 | 8 | 41 (9.17%) | |||
| Evaluation and methodology process | 1 | 11 | 12 (2.68%) | ||||||
| Ethics and oversight process | 1 | 8 | 9 (2.01%) | ||||||
| Common reference LHS frameworks | 1 | 4 | 17 | 22 (4.92%) | |||||
| Foley LHS framework | 1 | 5 | 6 (1.34%) | ||||||
| Health equity PETAL framework | 1 | 5 | 6 (1.34%) | ||||||
| Menear value‐creating LHS framework | 1 | 3 | 4 (0.89%) | ||||||
| Monash Melder LHS framework | 1 | 4 | 5 (1.12%) | ||||||
| Total number of SMARTLHS classes | 6 | 22 | 69 | 150 | 152 | 38 | 7 | 3 | 447 (100.00%) |
Note: Class distribution was calculated according to the conceptual structure of SMARTLHS rather than solely from the formal BFO‐aligned OWL superclass hierarchy.
SMARTLHS makes interconnections among LHS components explicit through 134 object properties. At the top level, the LHS is related to cross‐cutting themes through has Theme and to supporting pillars through has Pillar. The cross‐cutting themes also connect horizontally to the supporting pillars (Table 4). For example, ethics and oversight guides pillar processes, evaluation and methodology evaluates them, and value creation and benefits reflects, influences, or depends on other organizational domains. This horizontal structure is important because it captures the nonlinear nature of LHS implementation.
TABLE 4.
Horizontal view of relationships among SMARTLHS cross‐cutting themes and supporting pillars.
| Source class | VCB | EO | EM | WCB | RE | CDOS | GL | DTI | CCM | CCE |
|---|---|---|---|---|---|---|---|---|---|---|
| Value creation and benefits | X | Aligns with | Depends on | Influences | Reflects in | |||||
| Ethics and oversight | X | Guides | Guides | Guides | Guides | Guides | Guides | Guides | ||
| Evaluation and methodology | Evaluates | Aligns with | X | Evaluates | Evaluates | Evaluates | Evaluates | Evaluates | Evaluates | Evaluates |
| Workforce and capacity building | supports | Supports | depends On | X | Supports | Employs | Embraces | |||
| Research and education | Creates value for | Supports | Is closely linked with | X | Enables | Supports | Depends on | Influences | Supports | |
| Care delivery and organizational strategies | Creates value for | aligns With | Develops | Enables | X | Conforms with | Depends on; invests In | Reinforces | Empowers; supports | |
| Governance and leadership | Measures | Empowers | Guides | Is accountable for | X | Guides; leverages | Defines | Informs | ||
| Data and technology infrastructure | Creates value for | Enables | Supports | Supports | X | Supports | Enables | |||
| Culture and change management | Creates value for | Integrates with | Enables; requires | Influences | Shapes | Enables | X | |||
| Consumer and community engagement | Creates value for | Influences | Informs | Promotes | Depends on | Aligns with | X |
Note: X denotes the same source and target class. Relationships to classes outside this 10‐domain matrix are not shown.
Abbreviations: CCE, consumer and community engagement; CCM, culture and change management; CDOS, care delivery and organizational strategies; DTI, data and technology infrastructure; EM, evaluation and methodology; EO, ethics and oversight; GL, governance and leadership; RE, research and education; VCB, value creation and benefits; WCB, workforce and capacity building.
In the aligned ontology, Learning Health Organizations implement the SMARTLHS framework and realize its supporting pillars and cross‐cutting themes as processes as illustrated in Figure S2. Learning use‐case specifications are modeled as plan specifications that define one or more learning approach processes and are enacted by participating organizations, people, and technologies.
Care delivery and organizational strategies interact with patient engagement, culture, and evaluation rather than operating independently in Figure 2 below.
FIGURE 2.
Interrelationship of class strategical planning process with other classes of SMARTLHS.
Competency question testing demonstrated that SMARTLHS successfully supports semantic retrieval across all major domains of the ontology (as shown in Table S5). The SPARQL queries confirmed that SMARTLHS could represent and retrieve organizational knowledge required for LHS planning, implementation, governance, and evaluation. During query development, missing definitions, comments, and semantic relationships were identified and resolved through iterative ontology refinement.
5. Discussion
This study developed SMARTLHS, an ontology‐based framework that formalizes the concepts and relationships found across published LHS frameworks. To our knowledge, it is the first study to represent the LHS framework using OWL as a structured, computable ontology.
SMARTLHS addresses a major limitation of existing LHS frameworks by explicitly representing concepts and relationships in a computable form. Competency question evaluation demonstrated that these semantic relationships support querying and knowledge retrieval across organizational domains.
A second contribution is adaptability. Rather than representing a fixed view of LHSs, SMARTLHS was designed as an extensible ontology in which new concepts, relationships, and emerging domains can be incorporated without compromising the integrity of the broader semantic structure. This is particularly important as LHS implementation increasingly intersects with AI capability, digital governance, human–computer interaction, and evolving organizational models [45]. The alignment with the OBO‐compatible design patterns further supports this adaptability by providing a stable semantic and extendable foundation. Accordingly, SMARTLHS should be viewed as a living semantic framework rather than a static conceptual model.
SMARTLHS supports several practical use cases.
One use case is ontology‐guided Semantic Coverage Analysis of Organizational Strategies and Governance Frameworks for hospital executives, implementation scientists, and health services researchers. Healthcare organizations increasingly aim to become LHSs or learning organizations through strategic planning, policy alignment, and organizational transformation [12]. SMARTLHS provides a semantic reference model for analyzing organizational documents against recognized LHS components [46]. Recent ontology‐based semantic analytics frameworks have demonstrated how semantic models can support governance‐oriented decision making through semantic coverage analysis [47]. By mapping organizational strategies, governance frameworks, and policies to ontology concepts and relationships, it identifies areas of strong coverage, partial implementation, and potential gaps, supporting strategic planning and organizational improvement.
Another use case is ontology‐guided Retrieval‐Augmented Generation (RAG), where SMARTLHS provides the semantic knowledge model that guides retrieval of organizational knowledge to support LHS implementation by implementation scientists, health services researchers, Digital Health teams, and LHS practitioners [48]. Recent ontology‐driven knowledge graph frameworks demonstrate that semantic models can support transparent, explainable, and ontology‐guided assessment across diverse domains [49]. In such an implementation, users can query the organizational capabilities, processes, and evidence required to implement specific LHS components. Relevant organizational documents are first semantically retrieved through ontology concepts and relationships before being supplied to the LLM to generate grounded responses. By grounding retrieval and AI‐generated responses in both the ontology and local organizational documents, the approach provides transparent, explainable, and context‐specific decision support for LHS planning and implementation [50].
The study has limitations. SMARTLHS was derived from published frameworks and related literature and therefore reflects both the strengths and biases of that literature base. Although expert feedback and competency testing supported refinement, the ontology has not yet been fully validated through large‐scale implementation in operational healthcare environments. In addition, some domains, such as privacy and security, will require further integration with jurisdiction‐specific policies and standards in downstream applications.
6. Conclusions
SMARTLHS provides the first formal ontology‐based representation of LHS frameworks. By synthesizing concepts and relationships from established LHS literature into a computable semantic knowledge model, it addresses the limitations of narrative and static framework representations while supporting semantic querying, organizational assessment, and knowledge integration. Designed as an extensible ontology aligned with foundational ontology principles, SMARTLHS provides a robust semantic foundation for future AI‐enabled applications and ontology‐guided organizational transformation. Continued application and evaluation in operational healthcare settings will further refine the ontology and strengthen its practical utility for implementing and evaluating LHSs [51].
Funding
The authors have nothing to report.
Ethics Statement
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Figure S1: Complete hierarchy of the Data and Technology Infrastructure supporting pillar.
Figure S2: Relationships between the top‐level SMARTLHS classes and their associated ontology classes.
Table S1: SMARTLHS iterative conceptualization process.
Table S2: Reviews of the papers where the refinement of the ontology classes were made.
Table S3: Evaluation of SMARTLHS.
Table S4: Competency questions, SPARQL queries, and example expected results.
Figure S3: Execution of the CQ1 SPARQL query in Protégé.
Figure S4: Execution of the CQ2 SPARQL query in Protégé.
Figure S5: Execution of the CQ8 SPARQL query in Protégé.
Table S5: SMARTLHSONTO ontology statistics.
Data Availability Statement
The data that support the findings of this study are openly available in NCBO BioPortal at https://bioportal.bioontology.org/ontologies/SMARTLHSONTOV1.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1: Complete hierarchy of the Data and Technology Infrastructure supporting pillar.
Figure S2: Relationships between the top‐level SMARTLHS classes and their associated ontology classes.
Table S1: SMARTLHS iterative conceptualization process.
Table S2: Reviews of the papers where the refinement of the ontology classes were made.
Table S3: Evaluation of SMARTLHS.
Table S4: Competency questions, SPARQL queries, and example expected results.
Figure S3: Execution of the CQ1 SPARQL query in Protégé.
Figure S4: Execution of the CQ2 SPARQL query in Protégé.
Figure S5: Execution of the CQ8 SPARQL query in Protégé.
Table S5: SMARTLHSONTO ontology statistics.
Data Availability Statement
The data that support the findings of this study are openly available in NCBO BioPortal at https://bioportal.bioontology.org/ontologies/SMARTLHSONTOV1.