TY - JOUR
T1 - Detection and Remediation of Misidentification Errors in Radiology Examination Ordering
AU - Sheehan, Scott E.
AU - Safdar, Nasia
AU - Singh, Hardeep
AU - Sittig, Dean F.
AU - Bruno, Michael A.
AU - Keller, Kelli
AU - Kinnard, Samantha
AU - Brunner, Michael C.
N1 - Funding Information:
No conflicts of interest to disclose. No specific funding support was provided toward the work reported in this manuscript. Nasia Safdar receives funding support from the VA Patient Safety Center, and from AHRQ R18 and R03 grants for unrelated work. Dr. Singh’s research is funded by the Veterans Affairs (VA) Health Services Research and Development Service (HSR&D) (CRE-12–033) and the Presidential Early Career Award for Scientists and Engineers (USA 14–274), the Agency for Healthcare Research and Quality (R01HS022087 and R18HS017820), the VA National Center for Patient Safety, and the Houston VA HSR&D Center for Innovations in Quality, Effectiveness, and Safety (CIN 13–413).
Publisher Copyright:
© 2020 American Institute of Physics Inc.. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.1055/s-0039-3402730
DO - 10.1055/s-0039-3402730
M3 - Article
C2 - 31995835
AN - SCOPUS:85078820096
SN - 1869-0327
VL - 11
SP - 79
EP - 87
JO - Applied Clinical Informatics
JF - Applied Clinical Informatics
IS - 1
ER -