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Leveraging electronic health records (EHRs) offers opportunities for health insights, but understanding data limitations like bias and missing information is crucial for accurate clinical applications and avoiding incorrect conclusions.

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Area of Science:

  • Biomedical Informatics
  • Health Data Science
  • Clinical Data Analytics

Background:

  • Growing availability of longitudinal, real-world clinical data in electronic health records (EHRs).
  • Increasing interest in using EHR data for understanding health and disease, and for clinical applications.
  • Need to address data limitations, including biases and missing information, in EHR-based studies.

Purpose of the Study:

  • To discuss key considerations for designing, implementing, and interpreting EHR-based informatics studies.
  • To highlight opportunities and potential pitfalls in leveraging EHR data for clinical insights.
  • To inform studies in population and precision medicine to avoid erroneous conclusions.

Main Methods:

  • Literature review of EHR-based informatics studies.
  • Discussion of considerations across hypothesis generation, testing, and machine learning applications.
  • Examples from existing research to illustrate points.

Main Results:

  • EHR data presents significant opportunities for health research and clinical applications.
  • Data limitations (bias, missingness) can lead to flawed conclusions if not addressed.
  • Evolving AI capabilities enhance possibilities for association studies and predictive modeling.

Conclusions:

  • Careful design and interpretation are essential for valid EHR-based informatics studies.
  • Addressing data limitations is critical for reliable population and precision medicine.
  • Future research should focus on maximizing EHR utility while mitigating inherent challenges.