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A Representativeness-informed Model for Research Record Selection from Electronic Medical Record Systems.

Victor A Borza1, Ellen Wright Clayton1, Murat Kantarcioglu2

  • 1Vanderbilt University, Nashville, TN.

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Researchers developed a new method to improve participant diversity in clinical studies. This approach enhances the representativeness of electronic medical record cohorts, ensuring more generalizable research findings.

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Area of Science:

  • Clinical Research Methodology
  • Health Informatics
  • Biostatistics

Background:

  • Historical bias in clinical study recruitment has led to underrepresentation of minority and underprivileged populations.
  • This lack of diversity results in inaccurate, non-applicable, and non-generalizable study outcomes.
  • Electronic Medical Record (EMR) systems, increasingly used for research, often reflect these demographic biases.

Purpose of the Study:

  • To introduce a novel method for quantifying cohort representativeness in research.
  • To develop an algorithmic approach for selecting more representative EMR cohorts under resource constraints.
  • To address the critical issue of underrepresentation in clinical research data.

Main Methods:

  • Utilized information theoretic measures to quantify representativeness.
  • Developed an algorithmic selection strategy for EMR cohorts.
  • Applied the method to a large EMR database (2M+ records) at Vanderbilt University Medical Center, selecting cohorts of 2,000-20,000 records.

Main Results:

  • The representativeness-informed approach significantly improved cohort demographics compared to random selection.
  • Assessed representativeness based on key demographics: age, ethnicity, race, and gender.
  • Achieved a cohort approximately 5.8 times more representative than random selection.

Conclusions:

  • The proposed method offers an effective strategy to enhance the representativeness of EMR-derived research cohorts.
  • This algorithmic approach is crucial for overcoming resource limitations in selecting diverse study populations.
  • Improving cohort representativeness is essential for generating more accurate and generalizable scientific and clinical findings.