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Related Concept Videos

Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Wrong-side imaging orders: automated detection using electronic health record data - a retrospective cohort study.

Jerard Z Kneifati-Hayek1, Isaac Peabody2, Charles Baillie3

  • 1Columbia University Irving Medical Center, New York, New York, USA jk4197@cumc.columbia.edu.

BMJ Open Quality
|June 5, 2026
PubMed
Summary

Automated electronic health record (EHR) methods can detect wrong-side diagnostic imaging orders, a preventable patient safety issue. This approach identifies potential errors, improving diagnostic safety through continuous surveillance.

Keywords:
Adverse events, epidemiology and detectionDiagnostic errorsPatient safety

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Area of Science:

  • Medical Informatics
  • Patient Safety
  • Diagnostic Imaging

Background:

  • Wrong-side diagnostic imaging errors are preventable but underdetected due to reporting system limitations.
  • These errors can delay diagnosis and lead to patient harm.

Purpose of the Study:

  • To develop and validate an automated electronic health record (EHR)-based method for detecting potential wrong-side diagnostic imaging order errors.
  • To identify risk factors associated with these errors using an adapted Retract-and-Reorder (RAR) approach.

Main Methods:

  • Retrospective cohort study across six healthcare facilities.
  • Adapted RAR methodology to screen 355,000 imaging orders from 2021, extending detection windows and analyzing side-switching events.
  • Validated the method via chart review and used multivariate logistic regression to identify risk factors.

Main Results:

  • Identified 1667 Retract-and-Reorder (RAR) events (4.70 per 1000 orders), with an 87% positive predictive value for confirmed errors (4.09 per 1000 orders).
  • Outpatient settings (OR 4.53) and administrative staff (OR 2.08) had higher odds of RAR events.
  • CT scans (OR 1.79) showed higher odds compared to X-rays.

Conclusions:

  • A validated, scalable EHR-based method can automatically detect potential wrong-side diagnostic imaging order errors.
  • This approach utilizes readily available EHR data for continuous surveillance and intervention evaluation.
  • The findings support improved diagnostic safety by addressing a critical, yet often undetected, patient harm event.