What is it about?
In the present clinical research landscape, there is tremendous excitement and hope around the potential of the electronic medical record (EMR) to be used to identify the causes of diseases and make predictions about who will respond best to which treatments. To accomplish this, the EMR is often thought of as a research database with little consideration for its intended purpose – as an agent of medical billing. This Opinion summarizes the historical context that led up to the current state of EMR-based research, describes the limitations in reliability and validity in the EMR, and propose solutions to create study cohorts that can enhance confidence when using the EMR for clinical research. Throughout, it uses multiple sclerosis (MS) as an example, and describes ways to leverage the EMR to better understand depression in MS.
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Why is it important?
The EMR has the potential to be extremely powerful in answering research questions that require large samples of individuals. This article provides a framework for understanding how information gets into the EMR, the degree of trust we can have with certain types of information, and how to best use this knowledge to improve patient care across broad populations.
Perspectives
As a consultation-liaison psychiatrist that takes care of patients with psychiatric needs during medical hospitalizations, I've seen first-hand that nearly all evidence based treatment recommendations are based on studies that would have excluded my patients due to their medical complexity. We need a way forward that is inclusive and generalizable to all patients. Research using the electronic medical record has incredible potential, as long as it is done carefully, thoughtfully, and with rigor.
Erica Baller
University of Pennsylvania
Read the Original
This page is a summary of: From Billing Codes to Breakthroughs: Filtering Signal From Noise in Electronic Medical Records–Based Research, Journal of Neuropsychiatry, September 2025, American Psychiatric Association,
DOI: 10.1176/appi.neuropsych.20250029.
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