Studies of medication warning optimization in the electronic health record generally report reduced alert burden and improved clinician responses after removal of low-value warnings, adjustment of severity and triggering criteria, incorporation of patient-specific information, and selective use of noninterruptive decision support. These efforts commonly involve multidisciplinary review, clinician feedback, and ongoing monitoring. Pediatric evidence emphasizes customized dosing rules that acco...
General population:
Several reviews have evaluated strategies to optimize medication-related electronic health record (EHR) alerts. A 2023 scoping review of 16 post-implementation medication-alert optimization studies identified six primary interventions: alert inactivation, severity reclassification, addition of contextual information, adjustment of triggering thresholds, provision of additional information within alerts, and encounter-specific suppression. Alerts were commonly selected for optimization based on firing frequency, clinician or pharmacist feedback, or review of clinical significance, generally through multidisciplinary processes involving pharmacists and physicians. Alert inactivation reduced alert quantity by 1% to 25.3%, while severity reclassification reduced alert quantity by 1% to 16.5% in 6 of 7 studies and increased alert acceptance by 4.2% to 50.2%; high-severity alerts reached 100% acceptance in studies reporting this outcome. Overall, alert quantity or f...
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A search of the published medical literature revealed
11 studies investigating the researchable question:
Please provide a literature review of studies assessing medication warning optimization in the electronic health record in general patients as well as pediatric and oncology populations.
Level of evidence
C - Multiple studies with limitations or conflicting results
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[1] Ledger TS, Brooke-Cowden K, Coiera E. Post-implementation optimization of medication alerts in hospital computerized provider order entry systems: a scoping review. J Am Med Inform Assoc. 2023;30(12):2064-2071. doi:10.1093/jamia/ocad193
[2] Van Dort BA, Zheng WY, Sundar V, Baysari MT. Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals. J Am Med Inform Assoc. 2021;28(1):177-183. doi:10.1093/jamia/ocaa279
[3] Chaparro JD, Beus JM, Dziorny AC, et al. Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts. Appl Clin Inform. 2022;13(3):560-568. doi:10.1055/s-0042-1748856
[4] Kim GR, Lehmann CU; Council on Clinical Information Technology. Pediatric aspects of inpatient health information technology systems. Pediatrics. 2008;122(6):e1287-e1296. doi:10.1542/peds.2008-2963
[5] Dufendach KR, Lehmann CU, Spooner SA; COUNCIL ON CLINICAL INFORMATION TECHNOLOGY . Special Requirements of Electronic Health Record Systems in Pediatrics: Clinical Report. Pediatrics. 2024;154(4):e2024068509. doi:10.1542/peds.2024-068509
[6] Goldspiel B, Hoffman JM, Griffith NL, et al. ASHP guidelines on preventing medication errors with chemotherapy and biotherapy. Am J Health Syst Pharm. 2015;72(8):e6-e35. doi:10.2146/sp150001
[7] Elsaid KA, Garguilo S, Collins CM. Chemotherapy e-prescribing: opportunities and challenges. Integr Pharm Res Pract. 2015;4:39-48. Published 2015 May 21. doi:10.2147/IPRP.S84232
[8] Sklarin NT, Granovsky S, O'Reilly EM, Zelenetz AD. Electronic Chemotherapy Order Entry: A Major Cancer Center's Implementation. J Oncol Pract. 2011;7(4):213-218. doi:10.1200/JOP.2011.000266
[9] Potashner R, Meyer N, Patterson E, Jessa K, Yan AP. A Case Study: Optimizing CDS for Pediatric Oncology Trials by Transitioning from Interruptive to Passive Alerts. Appl Clin Inform. 2025;16(3):589-594. doi:10.1055/a-2555-2441