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Could Patient Self-reported Health Data Complement EHR for Phenotyping?

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Summary
This summary is machine-generated.

Comparing patient self-reported data with electronic health records (EHRs) for phenotyping reveals EHRs have low sensitivity for diabetes. Combining data sources may improve accuracy.

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Area of Science:

  • Biomedical Informatics
  • Clinical Research
  • Health Data Science

Background:

  • Electronic health records (EHRs) are crucial for phenotyping but face data quality challenges, including missingness.
  • Patient self-reported health data is becoming more accessible, necessitating a comparison with EHR data for phenotyping accuracy.

Purpose of the Study:

  • To compare the effectiveness of patient self-reported data versus EHR data for phenotyping Type 2 diabetes mellitus (DM2).
  • To evaluate the performance of the eMERGE EHR phenotyping algorithm for DM2 detection.

Main Methods:

  • Utilized self-reported diabetes status from 2,249 patients at Columbia University Medical Center.
  • Applied the established eMERGE EHR phenotyping algorithm for DM2 to the patient cohort.
  • Assessed algorithm performance using specificity and sensitivity metrics.

Main Results:

  • The eMERGE algorithm demonstrated high specificity (.97) but low sensitivity (.32) for DM2 in this cohort.
  • Approximately 87% of patients with self-reported diabetes had supporting evidence (ICD-9 codes, medications, or lab results) in their EHRs.
  • A notable 13% of self-reported diabetes cases lacked supporting EHR data, suggesting potential reporting or data quality issues.

Conclusions:

  • EHR-based phenotyping for DM2 exhibits limitations in sensitivity.
  • Self-reported data offers a complementary perspective but may also contain inaccuracies.
  • Combining self-reported and EHR data warrants further investigation to enhance phenotyping accuracy and address data quality gaps.