Appl Clin Inform 2020; 11(01): 079-087
DOI: 10.1055/s-0039-3402730
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Detection and Remediation of Misidentification Errors in Radiology Examination Ordering

Scott E. Sheehan
1   Department of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States
Nasia Safdar
2   Department of Medicine, William S. Middleton Memorial Veterans Hospital and University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States
Hardeep Singh
3   Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center and Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
Dean F. Sittig
4   School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States
Michael A. Bruno
5   Department of Radiology, Penn State Hershey, Hershey, Pennsylvania, United States
Kelli Keller
1   Department of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States
Samantha Kinnard
1   Department of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States
Michael C. Brunner
6   Department of Radiology, William S. Middleton Memorial Veterans Hospital and University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States
› Author Affiliations
Funding None.
Further Information

Publication History

13 May 2019

06 December 2019

Publication Date:
29 January 2020 (online)


Background Despite progress in patient safety, misidentification errors in radiology such as ordering imaging on the wrong anatomic side persist. If undetected, these errors can cause patient harm for multiple reasons, in addition to producing erroneous electronic health records (EHR) data.

Objectives We describe the pilot testing of a quality improvement methodology using electronic trigger tools and preimaging checklists to detect “wrong-side” misidentification errors in radiology examination ordering, and to measure staff adherence to departmental policy in error remediation.

Methods We retrospectively applied and compared two methods for the detection of “wrong-side” misidentification errors among a cohort of all imaging studies ordered during a 1-year period (June 1, 2015–May 31, 2016) at our tertiary care hospital. Our methods included: (1) manual review of internal quality improvement spreadsheet records arising from the prospective performance of preimaging safety checklists, and (2) automated error detection via the development and validation of an electronic trigger tool which identified discrepant side indications within EHR imaging orders.

Results Our combined methods detected misidentification errors in 6.5/1,000 of study cohort imaging orders. Our trigger tool retrospectively identified substantially more misidentification errors than were detected prospectively during preimaging checklist performance, with a high positive predictive value (PPV: 88.4%, 95% confidence interval: 85.4–91.4). However, two third of errors detected during checklist performance were not detected by the trigger tool, and checklist-detected errors were more often appropriately resolved (p < 0.00001, 95% confidence interval: 2.0–6.9; odds ratio: 3.6).

Conclusion Our trigger tool enabled the detection of substantially more imaging ordering misidentification errors than preimaging safety checklists alone, with a high PPV. Many errors were only detected by the preimaging checklist; however, suggesting that additional trigger tools may need to be developed and used in conjunction with checklist-based methods to ensure patient safety.

Protection of Human and Animal Subjects

This quality improvement study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and received exemption from the University of Wisconsin Health Sciences Institutional Review Board.

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