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This summary is machine-generated.

Electronic health records (EHRs) can show racial disparities, but patient data may not represent everyone. Linking EHRs with the American Community Survey (ACS) reveals potential misestimations for Hispanic patients.

Keywords:
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Area of Science:

  • Health Services Research
  • Health Disparities Research
  • Data Science in Healthcare

Background:

  • Electronic health records (EHRs) are increasingly used to study racial and ethnic health disparities.
  • A key challenge is that EHR data may not be representative of the broader population.
  • Linking EHR data to external sources can help evaluate data limitations.

Purpose of the Study:

  • To assess the strengths and weaknesses of EHR-derived data for describing health disparities.
  • To evaluate the representativeness of EHR data by linking it to the American Community Survey (ACS).
  • To understand potential biases in health disparity estimations for different racial and ethnic groups.

Main Methods:

  • Utilized a stratified random sample of approximately 200,000 patient records from a North Carolina health system (2016-2019).
  • Linked EHR data to restricted-use American Community Survey (ACS) microdata (2001-2017).
  • Analyzed linkages by race and ethnicity to assess data representativeness and identify potential biases.

Main Results:

  • Standard adjustments, such as weighting procedures, can improve Black-White health disparity comparisons using EHR data.
  • Differential coverage rates for Hispanic patients in EHRs may lead to misestimation of health disparities for this group.
  • EHR data offers valuable insights but requires careful evaluation for representativeness across diverse populations.

Conclusions:

  • EHR data, when linked with external sources like the ACS, can enhance the understanding of racial and ethnic health disparities.
  • Methodological adjustments are crucial for accurate disparity assessment, particularly for underrepresented groups.
  • Researchers must be cognizant of potential biases in EHR data to avoid misinterpreting health disparities.