Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems
Open accessAbstract
Background Electronic health record (EHR) alert fatigue, while widely recognized as a concern nationally, lacks a corresponding comprehensive mitigation plan. Objectives The goal of this manuscript is to provide practical guidance to clinical informaticists and other health care leaders who are considering creating a program to manage EHR alerts. Methods This manuscript synthesizes several approaches and recommendations for better alert management derived from four U.S. health care institutions that presented their experiences and recommendations at the American Medical Informatics Association 2019 Clinical Informatics Conference in Atlanta, Georgia, United States. The assembled health care institution leaders represent academic, pediatric, community, and specialized care domains. We describe governance and management, structural concepts and components, and human–computer interactions with alerts, and make recommendations regarding these domains based on our experience supplemented with literature review. This paper focuses on alerts that impact bedside clinicians. Results The manuscript addresses the range of considerations relevant to alert management including a summary of the background literature about alerts, alert governance, alert metrics, starting an alert management program, approaches to evaluating alerts prior to deployment, and optimization of existing alerts. The manuscript includes examples of alert optimization successes at two of the represented institutions. In addition, we review limitations on the ability to evaluate alerts in the current state and identify opportunities for further scholarship. Conclusion Ultimately, alert management programs must strive to meet common goals of improving patient care, while at the same time decreasing the alert burden on clinicians. In so doing, organizations have an opportunity to promote the wellness of patients, clinicians, and EHRs themselves.
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Alert burden can lead to high rates of alert overrides and physician burnout (both physical and cognitive).
“Alert burden can lead to high rates of alert overrides and physician burnout (both physical and cognitive).9,16,64–66”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [66]
The Background and Significance section reports high alert-override rates and says clinicians are more likely to ignore alerts as the number they encounter increases. It also reports an association between perceived alert burden and burnout among primary care physicians; the Clinical Relevance Statement identifies burnout as a potential consequence of over-alerting. Neither passage distinguishes physical from cognitive burnout, and the reported association concerns perceived rather than measured alert burden. (LLM, conf=0.86)
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with the HITEC Investigators, Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0430-8 · Cited by 709 · see citing works
Computer Technology and Clinical Work: Still Waiting for Godot
Wears, R. L., & Berg, M. (2005) · JAMA · doi:10.1001/jama.293.10.1261 · Cited by 448 · see citing works
Physician Burnout in the Electronic Health Record Era: Are We Ignoring the Real Cause?
Downing, N. L., Bates, D. W., & Longhurst, C. A. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0139 · Cited by 418 · see citing works
Electronic Health Record Usability Issues and Potential Contribution to Patient Harm
Howe, J. L., Adams, K. T., Hettinger, A. Z., & Ratwani, R. M. (2018) · JAMA · doi:10.1001/jama.2018.1171 · Cited by 281 · see citing works
A Clinical Case of Electronic Health Record Drug Alert Fatigue: Consequences for Patient Outcome
Carspecken, C. W., Sharek, P. J., Longhurst, C., & Pageler, N. M. (2013) · Pediatrics · doi:10.1542/peds.2012-3252 · Cited by 142 · see citing works
Implications of an emerging EHR monoculture for hospitals and healthcare systems
Koppel, R., & Lehmann, C. U. (2015) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-003023 · Cited by 140 · see citing works
Wright, A., Sittig, D. F., Ash, J. S., Bates, D. W., Feblowitz, J., Fraser, G., Maviglia, S. M., McMullen, C., Nichol, W. P., Pang, J. E., et al. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.002030 · Cited by 92 · see citing works
Flagged first, then by citations
Research works printed but not linked: 36
- Kawamanto K, Flynn MC, Kukhareva P, et al. A pragmatic guide to establishing clinical decision support governance and addressing decision support fatigue: a case study. AMIA Annu Symp Proc 2018;2018:624-633
- Phansalkar S, van der Sijs H, Tucker AD, et al. Drug-drug inter- actions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc 2013; 20(03):489-493
- Topaz M, Seger DL, Slight SP, et al. Rising drug allergy alert overrides in electronic health records: an observational retro- spective study of a decade of experience. J Am Med Inform Assoc 2016;23(03):601-608
- Schreiber R, Gregoire JA, Shaha JE, Shaha SH. Think time: A novel approach to analysis of clinician's behavior after reduction of DDI alerts. Int J Med Inform 2017;97:59-67
- Saiyed SM, Greco PJ, Fernandes G, Kaelber DC. Optimizing drug- dose alerts using commercial software throughout an integrated health care system. J Am Med Inform Assoc 2017;24(06): 1149-1154 14
- Silbernagel G, Spirk D, Hager A, Baumgartner I, Kucher N. Elec- tronic alert system for improving stroke prevention among hospitalized oral-anticoagulation-naïve patients with atrial fibrillation: A randomized trial. J Am Heart Assoc 2016;5(07): e003776 15
- van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006;13(02):138-147
- Baysari MT, Tariq A, Day RO, Westbrook JI. Alert override as a habitual behavior -a new perspective on a persistent problem. J Am Med Inform Assoc 2017;24(02):409-412
- Peterson JF, Bates DW. Preventable medication errors: identifying and eliminating serious drug interactions. J Am Pharm Assoc (Wash) 2001;41(02):159-160
- Singh H, Spitzmueller C, Petersen NJ, Sawhney MK, Sittig DF. Information overload and missed test results in electronic health record-based settings. JAMA Intern Med 2013;173(08):702-704
- Gregory ME, Russo E, Singh H. Electronic health record alert- related workload as a predictor of burnout in primary care providers. Appl Clin Inform 2017;8(03):686-697
- Payne TH. EHR-related alert fatigue: minimal progress to date, but much more can be done. BMJ Qual Saf 2019;28(01):1-2 24
- Simpao AF, Ahumada LM, Desai BR, et al. Optimization of drug- drug interaction alert rules in a pediatric hospital's electronic health record system using a visual analytics dashboard. J Am Med Inform Assoc 2015;22(02):361-369
- McEvoy DS, Sittig DF, Hickman T-T, et al. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J Am Med Inform Assoc 2017;24(02):331-338
- Lee EK, Wu TL, Senior T, Jose J. Medical alert management: a real- time adaptive decision support tool to reduce alert fatigue. AMIA Annu Symp Proc 2014;2014:845-854
- Payne TH, Hines LE, Chan RC, et al. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc 2015;22(06):1243-1250
- Phansalkar S, Desai AA, Bell D, et al. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc 2012;19(05):735-743
- Grissinger M. Medication errors involving overrides of healthcare technology. Pennsylvania Patient Safety Advisory 2015;12(04): 141-148
- Classen DC, Resar R, Griffin F, et al. 'Global trigger tool' shows that adverse events in hospitals may be ten times greater than previ- ously measured. Health Aff (Millwood) 2011;30(04):581-589
- Wright A, Ai A, Ash J, et al. Clinical decision support alert malfunctions: analysis and empirically derived taxonomy. J Am Med Inform Assoc 2018;25(05):496-506
- Wright A, Ash JS, Aaron S, et al. Best practices for preventing malfunctions in rule-based clinical decision support alerts and reminders: Results of a Delphi study. Int J Med Inform 2018;118:78-85
- Koppel R. Is healthcare information technology based on evi- dence? Yearb Med Inform 2013;8:7-12
- Khaliq AA, Thompson DM, Walston SL. Perceptions of hospital CEOs about the effects of CEO turnover. Hosp Top 2006;84(04):21-27 39
- Kuperman GJ, Bobb A, Payne TH, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc 2007;14(01):29-40 41
- Murphy DR, Reis B, Sittig DF, Singh H. Notifications received by primary care practitioners in electronic health records: a taxon- omy and time analysis. Am J Med 2012;125(02):209.e1-209.e7
- Longhurst C, Sharp C. Implementation and transition to operations. In: Payne TH, ed. Practical Guide to Clinical Computing Systems: Design, Operations, and Infrastructure. 2nd ed. Amsterdam: Elsev- ier; 2015:99-110
- Schreiber R, Knapp J. Premature condemnation of clinical decision support as a useful tool for patient safety in computerized provider order entry. J Am Geriatr Soc 2009;57(10):1941-1942
- Aaron S, McEvoy DS, Ray S, Hickman T-TT, Wright A. Cranky com- ments: detecting clinical decision support malfunctions through free- text override reasons. J Am Med Inform Assoc 2019;26(01):37-43
- Wright A, McEvoy DS, Aaron S, et al. Structured override reasons for drug-drug interaction alerts in electronic health records. J Am Med Inform Assoc 2019;26(10):934-942
- Horsky J, Phansalkar S, Desai A, Bell D, Middleton B. Design of decision support interventions for medication prescribing. Int J Med Inform 2013;82(06):492-503
- Feblowitz J, Henkin S, Pang J, et al. Provider use of and attitudes towards an active clinical alert: a case study in decision support. Appl Clin Inform 2013;4(01):144-152
- Dowding D, Merrill JA. The development of heuristics for evaluation of dashboard visualization. Appl Clin Inform 2018;9(03):511-518
- Wright A, Aaron S, Sittig DF. Testing electronic health records in the "production" environment: an essential step in the journey to a safe and effective health care system. J Am Med Inform Assoc 2017;24(01):188-192
- Ashton M. Getting rid of stupid stuff. N Engl J Med 2018;379(19): 1789-1791
- Hussain MI, Reynolds TL, Zheng K. Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review. J Am Med Inform Assoc 2019;26(10):1141-1149
- Kane-Gill SL, O'Connor MF, Rothschild JM, et al. Technological distractions (part 1): summary of approaches to manage alert quantity with intent to reduce alert fatigue and sugges- tions for alert fatigue metrics. Crit Care Med 2017;45(09): 1481-1488
Cited by
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Asgari, E., Kaur, J., Nuredini, G., Balloch, J., Taylor, A. M., Sebire, N., Robinson, R., Peters, C., Sridharan, S., & Pimenta, D. (2024) · JMIR Medical Informatics · doi:10.2196/55499 · Cited by 142 · see citing works
Olakotan, O. O., & Mohd Yusof, M. (2021) · Health Informatics Journal · doi:10.1177/14604582211007536 · Cited by 101 · see citing works
Chen, J., Cutrona, S. L., Dharod, A., Bunch, S. C., Foley, K. L., Ostasiewski, B., Hale, E. R., Bridges, A., Moses, A., Donny, E. C., et al. (2023) · JMIR Medical Informatics · doi:10.2196/43097 · Cited by 4 · see citing works
“We focused on interruptive alerts because they were more likely to add a “burden” on providers [30-33].”
BackgroundInfluentialChen, J., Cutrona, S. L., Dharod, A., Moses, A., Bridges, A., Ostasiewski, B., Foley, K. L., Houston, T. K., iDAPT (Implementation & Informatics Developing Adaptable Processes and Technologies for Cancer Control) Implementation Science Center for Cancer Control, Chen, J., et al. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf050 · Cited by 3 · see citing works
Qualitative Study of Inpatient Hospital Medicine Interruptive Alerts and Clinical Decision Support
Khan, A., & Schnipper, J. (2026) · Applied Clinical Informatics · doi:10.1055/a-2955-9555
- Authors
- McGreevey, J. D., Mallozzi, C. P., Perkins, R. M., Shelov, E., & Schreiber, R.
- Affiliations
- University of Pennsylvania Health System (US)
- Moffitt Cancer Center (US)
- Children's Hospital of Philadelphia (US)
- Geisinger Health System (US)
- Corresponding author
- McGreevey, John D.
- Venue
- Applied Clinical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2020-01
- Volume
- 11
- Issue
- 01
- Pages
- 001-012
- ISSN
- 1869-0327 (ISSN-L)
- PMC
- PMC6938713
- OpenAlex
- W2997923706
- Cited by
- 165
- References
- 7
- Citations by year
- 2026: 17 · 2025: 45 · 2024: 36 · 2023: 30 · 2022: 17 · 2021: 13 · 2020: 6 · 2017: 1
- FWCI
- 14.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied Clinical Informatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- clinical decision support alerts, alert management
- MeSH terms
- Humans, Medical Order Entry Systems, Clinical Governance, Electronic Health Records
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