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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Constructing Epidemiologic Cohorts from Electronic Health Record Data.

Brent A Williams1

  • 1Geisinger Health System, Danville, PA 17821, USA.

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PubMed
Summary
This summary is machine-generated.

Electronic health records (EHR) offer valuable data for epidemiology. Developing "data machinery" is crucial for selecting participants, defining baseline characteristics, and tracking outcomes in EHR-based studies.

Keywords:
cohort studieselectronic health recordsepidemiologyretrospective studies

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Area of Science:

  • Epidemiology
  • Health Informatics

Background:

  • Electronic health records (EHR) are increasingly used in healthcare.
  • EHRs contain extensive patient data (demographic, diagnostic, therapeutic, etc.).
  • EHR data offers cost-efficient opportunities for large-scale epidemiologic research.

Purpose of the Study:

  • To focus on key aspects of developing "data machinery" for EHR-based epidemiology.
  • To discuss participant selection, baseline characteristic definition, and outcome follow-up.
  • To highlight unique challenges and features of EHR-based epidemiologic cohort construction.

Main Methods:

  • Focus on three critical components of "data machinery": participant selection, baseline definition, and outcome follow-up.
  • Discuss specific challenges and defining features for each component within EHR data.
  • Utilize an ongoing example to illustrate practical application and key points.

Main Results:

  • Identified participant selection as a critical first step in EHR cohort building.
  • Highlighted the importance of defining "baseline" and assembling characteristics from EHR data.
  • Emphasized the necessity of robust methods for follow-up using EHR data for future outcomes.

Conclusions:

  • EHR-based epidemiology is a growing field due to data proliferation.
  • Improving methods for EHR-based epidemiology is essential for leveraging healthcare data.
  • The development of effective "data machinery" is fundamental for valid EHR-based epidemiologic research.