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Lessons learned in detailed clinical modeling at Intermountain Healthcare
Thomas A Oniki1, Joseph F Coyle1, Craig G Parker1
1Department of Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA.
Summary
Developing detailed clinical models (DCMs) like the clinical element model (CEM) requires careful decision-making. Intermountain Healthcare shares lessons learned and guidelines to improve data interoperability and sharing through CEM development.
Area of Science:
- Health Informatics
- Clinical Data Modeling
- Semantic Interoperability
Background:
- Intermountain Healthcare utilizes coded terminology and detailed clinical models (DCMs) for data governance.
- The clinical element model (CEM) is the latest iteration of DCMs at Intermountain.
- CEMs facilitate decision support and semantic interoperability.
Purpose of the Study:
- To describe lessons learned from developing clinical element models (CEMs).
- To provide insights and guidelines for subjective decisions in CEM authoring.
- To propose strategies for reconciling conflicting use cases in DCM development.
Main Methods:
- Authored approximately 5000 CEMs using the Clinical Element Modeling Language (CEML).
Main Results:
- Formulated guidelines for modelers on subjective decisions during CEM authoring.
- Guidelines cover precoordination/postcoordination, content division, logical attribute modeling, and iso-semantic modeling.
- Explored benefits of an implementation layer, iso-semantic framework, and ontologic technologies.
Conclusions:
- Detailed clinical models (DCMs) can enhance interoperability and data sharing.
- Developed guidelines, an implementation layer, and an iso-semantic framework support progress toward interoperability goals.