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Clinical element models in the SHARPn consortium
Thomas A Oniki1, Ning Zhuo2, Calvin E Beebe3
1Department of Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA Tom.oniki@imail.org.
Journal of the American Medical Informatics Association : JAMIA
|November 17, 2015
Summary
The Strategic Health IT Advanced Research Project (SHARPn) successfully normalized electronic health record (EHR) data using Clinical Element Models (CEMs) for high-throughput phenotyping. This demonstrates CEMs
Area of Science:
- Health Informatics
- Biomedical Data Science
- Clinical Data Management
Background:
- Electronic Health Records (EHRs) contain valuable data for secondary use, but require normalization for consistency.
- High-throughput phenotyping necessitates efficient and accurate processing of large EHR datasets.
- The Strategic Health IT Advanced Research Project area four (SHARPn) aimed to address these challenges.
Purpose of the Study:
- To develop open-source tools for normalizing EHR data for secondary use, specifically high-throughput phenotyping.
- To evaluate the role of Intermountain Healthcare's Clinical Element Models (CEMs) as normalization targets within the SHARPn project.
Main Methods:
- Repurposed or created CEMs to define valid structure and semantics for clinical data.
- Expressed CEMs in a computable syntax for compilation into implementation artifacts.
- Agilely gathered requirements and iteratively developed and refined models with SHARPn colleagues.
Main Results:
- Developed 28 statement models and numerous component CEMs with associated terminology for phenotyping.
- Created structural and semantic mappings for data normalization.
- Normalized source EHR data instances to CEM-conformant data, stored in CEM instance databases.
- Built a model browser and request site to support development.
Conclusions:
- CEMs effectively captured and normalized diverse EHR data, serving as suitable targets for secondary use.
- Addressed challenges in context differences and granularity, highlighting the need for iso-semantic models and intelligent tooling.
- Demonstrated the feasibility of a CEM-based approach for EHR data normalization and secondary use, with considerations for scalability and sustainability.
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