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Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms
Mike Conway1, Richard L Berg, David Carrell
1Mayo Clinic, Rochester, MN, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|December 24, 2011
Summary
Formalizing clinical trial eligibility criteria and patient phenotyping is crucial for identifying eligible patients from electronic health records. Analysis of eMERGE phenotyping algorithms reveals complex logic, informing improvements to the CDISC Protocol Representation Model.
Area of Science:
- Health Informatics
- Clinical Trial Design
- Biomedical Data Standards
Background:
- Formal representations of clinical trial eligibility criteria and patient phenotyping are essential for automated patient identification from electronic health records.
- Existing phenotyping algorithms often employ complex logic, necessitating a deeper understanding for model development.
Purpose of the Study:
- To analyze the characteristics of Electronic Health Record (EHR)-oriented phenotyping algorithms developed within the eMERGE network.
- To identify common data elements, logic types, and temporal aspects within these algorithms.
- To inform the enhancement of the CDISC Protocol Representation Model based on these findings.
Main Methods:
- Analysis of fourteen phenotyping algorithms from the eMERGE project.
- Evaluation of constituent data elements, logic complexity (including boolean operations and negation), and temporal characteristics.
- Comparative assessment of algorithm design.
Main Results:
- The majority of analyzed eMERGE algorithms utilize complex, nested boolean logic and negation.
- Several algorithms incorporate cardinality constraints and intricate temporal logic.
- Significant variation exists in the complexity and types of logic employed across algorithms.
Conclusions:
- Understanding the nuances of EHR-based phenotyping algorithms is vital for developing robust computable representations of clinical trial eligibility criteria.
- The insights gained will directly contribute to augmenting the CDISC Protocol Representation Model for improved clinical trial data standardization and patient identification.
- Formalizing eligibility criteria enhances the efficiency and accuracy of patient recruitment for clinical studies.
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