ArticleLearning health systems2022
Toward an ontology of collaborative learning healthcare systems.
Article in Learning health systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Toward a Smart Learning Health System: An Ontology-Based Framework.Learning health systems · 2026Article
- Automated Waitlists for Ambulatory Appointment Scheduling: Multisite, Mixed Methods Evaluation.Journal of medical Internet research · 2026Article
- Engagement as a mechanism of action in collaborative learning health systems.Learning health systems · 2025Article
- An agent-based model to advance the science of collaborative learning health systems.PloS one · 2025Article
- Tailoring neurosurgical operating room education to medical undergraduates: Integrative review and meta-synthesis.Brain & spine · 2024Review
- Toward an ontology of collaborative learning healthcare systems.Learning health systems · 2022Article
- Making Diabetes Electronic Medical Record Data Actionable: Promoting Benchmarking and Population Health Improvement Using the T1D Exchange Quality Improvement Portal.Clinical diabetes : a publication of the American Diabetes Association · 2022Article
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Authors and funding
8 authors.
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Abstract
Objective: To establish a basis for a domain ontology - a formal, explicit specification of a shared conceptualization - of collaborative learning healthcare systems (CLHSs) in order to facilitate measurement, explanation, and improvement. Methods: We adapted the "Methontology" approach to begin building an ontology of CLHSs. We specified the purpose of an ontology, acquired domain knowledge via literature review, conceptualized a common framework of CLHSs using a grounded approach, refined these concepts based on expert panel input, and illustrated concept application via four cases. Results: The set of concepts identified as important to include in an ontology includes goals, values, structure, actors, environment, and products. To establish this set of concepts, we gathered input from content experts in two ways. First, expert panel methods were used to elicit feedback on these concepts and to test the elicitation of terms for the vocabulary of the Values concept. Second, from these discussions we developed a mapping exercise to test the intuitiveness of the concepts, requesting that network leaders from four CLHSs complete a mapping exercise to associate characteristics of their networks with the high-level concepts, building the vocabulary for each concept in a grounded fashion. We also solicited feedback from these participants on the experience of completing the mapping exercise, finding that the exercise is acceptable and could aid in CLHS development and collaboration. Respondents identified opportunities to improve the operational definitions of each concept to ensure that corresponding vocabularies are distinct and non-overlapping. Discussion: Our results provide a foundation for developing a formal, explicit shared conceptualization of CLHSs. Once developed, such a tool can be useful for measurement, explanation, and improvement. Further work, including alignment to a top-level ontology, expanding the vocabulary, and defining relations between vocabulary is required to formally build out an ontology for these uses.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.