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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
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Summary
This summary is machine-generated.

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

Keywords:
controlledelectronic health records/standardshealth information systems/standardsinformation storage and retrievalsemanticsvocabulary

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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.