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Understanding data requirements of retrospective studies
Edna C Shenvi1, Daniella Meeker2, Aziz A Boxwala3
1Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States.
International Journal of Medical Informatics
|December 3, 2014
Summary
Electronic health records (EHRs) offer significant potential for clinical research, with most data elements mapping to standard dictionaries. Understanding data usage patterns is key to leveraging EHRs effectively for research.
Area of Science:
- Clinical Informatics
- Health Data Science
- Biomedical Research Methodology
Background:
- Electronic health records (EHRs) are increasingly utilized in clinical research.
- Empirical knowledge regarding specific data requirements for diverse EHR-based research is limited.
Purpose of the Study:
- To characterize the types and patterns of data usage from EHRs for clinical research.
- To assess the suitability of EHR data for supporting various retrospective study designs.
Main Methods:
- Analysis of data requirements for over 100 retrospective studies.
- Mapping study selection criteria and variables to standard healthcare and clinical research data dictionaries.
- Validation of findings through author correspondence.
Main Results:
- A high proportion of study variables mapped to one or both data dictionaries.
- Average usage of 4.46 data element types for selection criteria and 6.44 for study variables.
- Frequently used data (e.g., procedures, conditions, medications) are often coded in EHRs; complex criteria and aggregate operations were common.
Conclusions:
- Clinical data warehousing holds substantial potential for facilitating clinical research due to high data element mappability.
- Unmapped data elements highlight challenges in creating comprehensive data dictionaries for EHRs.
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