Drug safety alert generation and overriding in a large Dutch university medical centre
Abstract
Purpose: To evaluate numbers and types of drug safety alerts generated and overridden in a large Dutch university medical centre. Methods: A disguised observation study lasting 25 days on two internal medicine wards evaluating alert generation and handling of alerts. A retrospective analysis was also performed of all drug safety alerts overridden in the hospital using pharmacy log files over 24 months. Results: In the disguised observation study 34% of the orders generated a drug safety alert of which 91% were overridden. The majority of alerts generated (56%) concerned drug–drug interactions (DDIs) and these were overridden more often (98%) than overdoses (89%) or duplicate orders (80%). All drug safety alerts concerning admission medicines were overridden. Retrospective analysis of pharmacy log files for all wards revealed one override per five prescriptions. Of all overrides, DDIs accounted for 59%, overdoses 24% and duplicate orders 17%. DDI alerts of medium‐level seriousness were overridden more often (55%) than low‐level (22%) or high‐level DDIs (19%). In 36% of DDI overrides, it would have been possible to monitor effects by measuring serum levels. The top 20 of overridden DDIs accounted for 76% of all DDI overrides. Conclusions: Drug safety alerts were generated in one third of orders and were frequently overridden. Duplicate order alerts more often resulted in order cancellation (20%) than did alerts for overdose (11%) or DDIs (2%). DDIs were most frequently overridden. Only a small number of DDIs caused these overrides. Studies on improvement of alert handling should focus on these frequently‐overridden DDIs. Copyright © 2009 John Wiley & Sons, Ltd.
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Alert nonadherence, or clinician overrides, occurs for 49%-96% of alerts.
“Alert nonadherence, or clinician overrides, is one key barrier that occurs for 49%–96% of alerts.13–23”
From Clinician collaboration to improve clinical decision support: the Clickbusters initiative (2022) · cites this paper as [18]
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Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care
Shah, N. R., Seger, A. C., Seger, D. L., Fiskio, J. M., Kuperman, G. J., Blumenfeld, B., Recklet, E. G., Bates, D. W., & Gandhi, T. K. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1868 · Cited by 353 · see citing works
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Medication-related clinical decision support alert overrides in inpatients
Nanji, K. C., Seger, D. L., Slight, S. P., Amato, M. G., Beeler, P. E., Her, Q. L., Dalleur, O., Eguale, T., Wong, A., Silvers, E. R., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx115 · Cited by 179 · see citing works
A framework for evaluating the appropriateness of clinical decision support alerts and responses
McCoy, A. B., Waitman, L. R., Lewis, J. B., Wright, J. A., Choma, D. P., Miller, R. A., & Peterson, J. F. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000185 · Cited by 138 · see citing works
Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review
Poly, T. N., Islam, M. M., Yang, H.-C., & Li, Y.-C. (. (2020) · JMIR Medical Informatics · doi:10.2196/15653 · Cited by 118 · see citing works
Olakotan, O. O., & Mohd Yusof, M. (2021) · Health Informatics Journal · doi:10.1177/14604582211007536 · Cited by 101 · see citing works
Understanding the management of electronic test result notifications in the outpatient setting
Hysong, S. J., Sawhney, M. K., Wilson, L., Sittig, D. F., Esquivel, A., Singh, S., & Singh, H. (2011) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-11-22 · Cited by 68 · see citing works
Clinician collaboration to improve clinical decision support: the Clickbusters initiative
McCoy, A. B., Russo, E. M., Johnson, K. B., Addison, B., Patel, N., Wanderer, J. P., Mize, D. E., Jackson, J. G., Reese, T. J., Littlejohn, S., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac027 · Cited by 41 · see citing works
- Authors
- van der Sijs, H., Mulder, A., van Gelder, T., Aarts, J., Berg, M., & Vulto, A.
- Affiliations
- Erasmus MC (NL)
- Venue
- Pharmacoepidemiology and Drug Safety
- Publisher
- Wiley
- Published
- 2009-07-03
- Volume
- 18
- Issue
- 10
- Pages
- 941-947
- ISSN
- 1053-8569 (ISSN-L)
- 1099-1557
- OpenAlex
- W2019954912
- Cited by
- 119
- References
- 1
- Citations by year
- 2025: 6 · 2024: 2 · 2023: 2 · 2022: 6 · 2021: 7 · 2020: 9 · 2019: 5 · 2018: 4 · 2017: 8 · 2016: 8 · 2015: 10 · 2014: 12 · 2013: 8 · 2012: 8
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pharmacoepidemiology and Drug Safety (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Topics
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- drug safety warnings, drug-drug interactions, medication errors, drug overdose, internal medicine ward
- MeSH terms
- Academic Medical Centers, Drug Interactions, Drug Therapy, Computer-Assisted, Hospital Units, Humans, Internal Medicine, Medication Errors, Netherlands, Pharmacy Service, Hospital, Drug Prescriptions, Retrospective Studies, Time Factors, Reminder Systems, Decision Support Systems, Clinical, Medical Order Entry Systems, Drug Overdose
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