Wrong-side imaging orders: automated detection using electronic health record data - a retrospective cohort study.
Authors
Affiliations (13)
Affiliations (13)
- Columbia University Irving Medical Center, New York, New York, USA [email protected].
- Columbia University Irving Medical Center, New York, New York, USA.
- Medicine, Columbia University Irving Medical Center, New York, New York, USA.
- Biostatistics, Columbia University Mailman School of Public Health, New York, New York, USA.
- Primary Care, Weill Cornell Medicine, New York, New York, USA.
- Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.
- Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA.
- Columbia University Department of Radiology, New York, New York, USA.
- Columbia University Department of Biomedical Informatics, New York, New York, USA.
- Department of Sociomedical Sciences, Columbia University Mailman School of Public Health, New York, New York, USA.
- Pediatrics, Children's Hospital at Montefiore and the Albert Einstein College of Medicine, Bronx, New York, USA.
- Quality and Patient Safety, NewYork-Presbyterian Hospital, New York, New York, USA.
- Emergency Medicine, Columbia University Irving Medical Center, NY, NY, USA.
Abstract
Wrong-side diagnostic imaging order errors are preventable errors that can delay diagnosis and cause patient harm yet remain underdetected due to limitations in existing reporting systems. To develop and validate an automated electronic health record (EHR)-based method for detecting potential wrong-side diagnostic imaging order errors using an adapted Retract-and-Reorder (RAR) approach and to identify associated risk factors. Retrospective cohort study. Six-facility health system comprising inpatient, outpatient and emergency room sites. We screened 355 000 imaging orders with side specified, placed during 2021 across our healthcare system. We adapted the RAR methodology, originally developed to detect near-miss medication errors, by extending detection windows to 24 hours and identifying any orders switching from one side to the contralateral side, accounting for multiprovider workflows inherent in imaging. We validated the method through chart review of 100 randomly selected RAR events, then applied the query across all imaging orders. Multivariate logistic regression was used to identify risk factors associated with RAR events. We identified 1667 RAR events (4.70 per 1000 orders). Validation yielded a positive predictive value of 87% (95% CI 79.0% to 92.2%), estimating 4.09 confirmed wrong-side errors per 1000 orders. The odds of an RAR event were significantly higher in outpatient settings compared with inpatient settings (OR 4.53; 95% CI 3.80 to 5.42) and among administrative staff compared with attending physicians (OR 2.08; 95% CI 1.73 to 2.49). CT scans showed 79% higher odds of an RAR event compared with X-rays (OR 1.79; 95% CI 1.34 to 2.39). This validated approach offers a scalable solution for automated detection of potential wrong-side diagnostic imaging order errors. The methodology leverages commonly available EHR data to support continuous surveillance and intervention evaluation for improved diagnostic safety.