Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support
Open accessAbstract
Background Recognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas. Objectives Our dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders. Methods Iterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts. Results At baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to −1.3 minutes. Conclusion CDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.
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False positive rates can contribute to unnecessary diagnostics and antimicrobial use, potentially avoidable ICU transfers, and alert fatigue.
“False positive rates, on the other hand, can contribute to unnecessary diagnostics and antimicrobial use, potentially avoidable intensive care unit (ICU) transfers, and alert fatigue.6,7”
From A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools (2026) · cites this paper as [7]
The Background and Significance section says unnecessary interruptive alerts pose a risk of alert fatigue, and the Abstract reports that redesigning a frequently firing COVID alert reduced alert burden. The paper does not link false-positive rates to unnecessary diagnostics, antimicrobial use, or avoidable ICU transfers. Page numbers were not available for these passages. (LLM, conf=0.85)
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Cites
Ogrinc, G., Davies, L., Goodman, D., Batalden, P., Davidoff, F., & Stevens, D. (2016) · BMJ Quality & Safety · doi:10.1136/bmjqs-2015-004411 · Cited by 2,996 · see citing works
The Improvement Guide: A Practical Approach to Enhancing Organizational Performance
(1997) · Quality Management Journal · doi:10.1080/10686967.1998.11919154 · Cited by 2,876 · see citing works
Bates, D. W., Kuperman, G. J., Wang, S., Gandhi, T., Kittler, A., Volk, L., Spurr, C., Khorasani, R., Tanasijevic, M., & Middleton, B. (2003) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1370 · Cited by 1,346 · see citing works
Overriding of Drug Safety Alerts in Computerized Physician Order Entry
van der Sijs, H., Aarts, J., Vulto, A., & Berg, M. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1809 · Cited by 1,117 · see citing works
Association of Interruptions With an Increased Risk and Severity of Medication Administration Errors
Westbrook, J. I. (2010) · Archives of Internal Medicine · doi:10.1001/archinternmed.2010.65 · Cited by 772 · see citing works
The Health Care Data Guide: Learning from Data for Improvement
Provost, L., & Murray, S. K. (2011) · CERN Document Server (European Organization for Nuclear Research) · Cited by 766 · see citing works
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
Improving Outcomes with Clinical Decision Support: An Implementer's Guide, Second Edition
Osheroff, J. (2012) · doi:10.4324/9781498757461 · Cited by 517 · see citing works
Horsky, J., Schiff, G. D., Johnston, D., Mercincavage, L. M., Bell, D. S., & Blackford Middleton, A. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.09.002 · Cited by 290 · see citing works
Montini, T., & Graham, I. D. (2015) · Implementation Science · doi:10.1186/s13012-015-0211-7 · Cited by 180 · see citing works
Electronic Health Record Alert-Related Workload as a Predictor of Burnout in Primary Care Providers
Russo, E., Singh, H., & Gregory, M. E. (2017) · Applied Clinical Informatics · doi:10.4338/aci-2017-01-ra-0003 · Cited by 169 · 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
Patient Safety Issues From Information Overload in Electronic Medical Records
Nijor, S., Rallis, G., Lad, N., & Gokcen, E. (2022) · Journal of Patient Safety · doi:10.1097/pts.0000000000001002 · Cited by 137 · see citing works
(2014) · PubMed Central · Cited by 117 · see citing works
Reducing Interruptive Alert Burden Using Quality Improvement Methodology
Chaparro, J. D., Hussain, C., Lee, J., Hehmeyer, J., Nguyen, M., & Hoffman, J. (2020) · Applied Clinical Informatics · doi:10.1055/s-0039-3402757 · Cited by 79 · see citing works
Chaparro, J. D., Beus, J. M., Dziorny, A. C., Hagedorn, P. A., Hernandez, S., Kandaswamy, S., Kirkendall, E. S., McCoy, A. B., Muthu, N., & Orenstein, E. W. (2022) · Applied Clinical Informatics · doi:10.1055/s-0042-1748856 · Cited by 76 · see citing works
Van Dort, B. A., Zheng, W. Y., Sundar, V., & Baysari, M. T. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa279 · Cited by 75 · see citing works
Trinkley, K. E., Blakeslee, W. W., Matlock, D. D., Kao, D. P., Matre, A. V., Harrison, R., Larson, C., Kostman, N., Nelson, J. A., Lin, C., et al. (2019) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-000015 · Cited by 59 · 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
Ng, H. J. H., Kansal, A., Abdul Naseer, J. F., Hing, W. C., Goh, C. J. M., Poh, H., D’souza, J. L. A., Lim, E. L., & Tan, G. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad056 · Cited by 37 · see citing works
Elias, P., Peterson, E., Wachter, B., Ward, C., Poon, E., & Navar, A. M. (2019) · Applied Clinical Informatics · doi:10.1055/s-0039-1700869 · Cited by 33 · see citing works
Frequency of passive EHR alerts in the ICU: another form of alert fatigue?
Kizzier-Carnahan, V., Artis, K. A., Mohan, V., & Gold, J. A. (2019) · Journal of Patient Safety · doi:10.1097/pts.0000000000000270 · Cited by 32 · see citing works
Orenstein, E. W., Kandaswamy, S., Muthu, N., Chaparro, J. D., Hagedorn, P. A., Dziorny, A. C., Moses, A., Hernandez, S., Khan, A. N., Huth, H. B., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab179 · Cited by 26 · see citing works
Formative Usability Testing Reduces Severe Blood Product Ordering Errors
Orenstein, E. W., Boudreaux, J. M., Rollins, M. R., Jones, J., Bryant, C., Karavite, D., Muthu, N., Hike, J., Williams, H., Kilgore, T., et al. (2019) · Applied Clinical Informatics · doi:10.1055/s-0039-3402714 · Cited by 24 · see citing works
Miller, S. D., Murphy, Z. R., Gray, J., Marsteller, J. A., Oliva‐Hemker, M. M., Maslen, A., Lehmann, H. P., Nagy, P. G., Hutfless, S. M., & Gürses, A. P. (2023) · Applied Clinical Informatics · doi:10.1055/a-2040-0578 · Cited by 18 · see citing works
Optimizing drug-dose alerts using commercial software throughout an integrated health care system
Saiyed, S. M., Greco, P. J., Fernandes, G., & Kaelber, D. C. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx031 · Cited by 15 · see citing works
Samal, L., Wu, E., Aaron, S., Kilgallon, J. L., Gannon, M., McCoy, A. B., Blecker, S., Dykes, P. C., Bates, D. W., Lipsitz, S. R., et al. (2023) · Applied Clinical Informatics · doi:10.1055/s-0043-1768994 · Cited by 8 · see citing works
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Sorted by citations
Justice, C., Nevin, C., Neely, R. L., Dilcher, B., Scherrer, N. K., Carter‐Templeton, H., Ostrowski, A. M., Krafcheck, J., Smith, G. S., McCarthy, P. J., et al. (2025) · Applied Clinical Informatics · doi:10.1055/s-0044-1791822 · Cited by 6 · see citing works
A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools
Patel, H., Crusco, S., Hansen, D., Morales, J., Patale, T., Chiuzan, C., Sands, M., Naidich, J., Myers, K., Davidson, K. W., et al. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10381-y · Cited by 4 · see citing works
Sportel, E. T., Movig, K. L. L., Thio, B. J., van der Kamp, M. R., van der Palen, J. A. M., & Brusse-Keizer, M. G. J. (2025) · JMIR Pediatrics and Parenting · doi:10.2196/78526 · Cited by 3 · see citing works
Right Idea, Wrong Time: Focusing on Alert Timing for Effective Decision Support
Wilson, A. E., Bain, A. P., Webb, J. W., Lehmann, C. U., Moran, B., Shah, N., & O’Connell, E. J. (2025) · Applied Clinical Informatics · doi:10.1055/a-2605-4510 · Cited by 3 · see citing works
The Elements of Style for Interruptive Electronic Health Record Alerts
Pourian, J., Blebea, C., Subramanian, C. R., Auerbach, A. D., & Khanna, R. R. (2024) · Applied Clinical Informatics · doi:10.1055/a-2508-7039 · Cited by 2 · see citing works
Potashner, R., Meyer, N., Patterson, E., Jessa, K., & Yan, A. P. (2025) · Applied Clinical Informatics · doi:10.1055/a-2555-2441 · Cited by 2 · see citing works
Liu, Y., Lin, G., Scholl, J., Hung, Y.-C., Lin, Y.-M., Li, Y., & Yang, H. (2025) · Journal of Medical Internet Research · doi:10.2196/70731 · Cited by 1 · see citing works
Sandhu, M. M., Latif, S., Bayor, A. A., Lü, W., Kholghi, M., Prabhu, D., & Silvera‐Tawil, D. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106262 · Cited by 2 · see citing works
Designing a clinical decision support tool to promote timely palliative care in heart failure
Men, Y., Wright, N., O’Connor, G., Kaur, H., Suh, E. Y. J., Chen, J., Kadhim, B. A., Ahmad, T., Beasley, M., Segar, N. O., et al. (2026) · Heart & Lung · doi:10.1016/j.hrtlng.2026.102821 · Cited by 1 · see citing works
Hossen, M. N., & Mascellino, M. T. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1842688 · Cited by 1 · see citing works
Identifying Patients with Heart Failure Eligible for Guideline-Directed Medical Therapy
Subramaniam, S., Hassan, S., Ünlü, O., Kumar, S., Zelle, D., Ostrominski, J. W., Nichols, H., Chasse, J., McPartlin, M., Twining, M., et al. (2024) · Population Health Management · doi:10.1089/pop.2024.0132 · Cited by 1 · see citing works
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Potashner, R., & Yan, A. P. (2025) · Applied Clinical Informatics · doi:10.1055/a-2594-3722
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Wahsh, E. A., Juskewitch, J. E., Eichenlaub, C., Holbrook, C., & Caraballo, P. J. (2025) · Applied Clinical Informatics · doi:10.1055/a-2646-6297
Schildt, E. E., Sutton, P. R., Lees, A. F., Ahmad, H. B., Lee, A. C., Leu, M. G., Wedgeworth, P., & White, A. A. (2025) · Applied Clinical Informatics · doi:10.1055/a-2661-3670
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Bradford, N., Whalan, E., Condon, P., Semerci, R., Bowers, A. P., & Ross, X. S. (2026) · Children · doi:10.3390/children13020164
Characterizing Push Notification Volume and Delivery Patterns in Hospital Medicine
Wilson, A. E., Bain, A. P., Goyal, S., Thomas, A. K., Turer, R. W., Glazer, C. S., Willett, D. L., Yin, W., & McDonald, S. (2026) · Applied Clinical Informatics · doi:10.1055/a-2802-2912
Booth, S. E., Rubins, D. M., O’Donoghue, A. L., Horng, S., Miller, A., Markson, L. J., Gould, R. M., Leventhal, E. L., Marshall, J., & Stevens, J. P. (2026) · Applied Clinical Informatics · doi:10.1055/a-2902-8381
Chejfec‐Ciociano, J. M., Barach, P. R., & Gelb, A. W. (2026) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000008195
Doan, N. N., Elen, B., Sun, C., Dumontier, M., & Ertaylan, G. (2026) · doi:10.2196/preprints.112384
Alavijeh, Z. S., & Borhani, M. (2026) · International Conference on Intelligent Systems and Artificial Intelligence Applications (ISAA) · doi:10.1109/isaa67392.2026.11726643
Bye, E. M., Anstett, T. J., Sciarra, B., Mitchell, M., Stenehjem, E. A., & Frasca, K. (2026) · HIV Medicine · doi:10.1111/hiv.70336
Gupta, M., Huang, A., Ness, K., Carlin, K. E., & Rutman, L. (2026) · Pediatric Quality and Safety · doi:10.1097/pq9.0000000000000939
- Authors
- Fallon, A., Haralambides, K., Mazzillo, J., & Gleber, C.
- Affiliations
- University of Rochester Medicine (US)
- Corresponding author
- Fallon, Anne
- Venue
- Applied Clinical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2023-12-12
- Volume
- 15
- Issue
- 01
- Pages
- 101-110
- ISSN
- 1869-0327 (ISSN-L)
- PMC
- PMC10830237
- OpenAlex
- W4389639911
- Cited by
- 22
- References
- 27
- Citations by year
- 2026: 12 · 2025: 8 · 2024: 2
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied Clinical Informatics (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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- alarm fatigue, interruptive alerts, electronic health records, clinician workload, patient safety, acute care
- MeSH terms
- Humans, Decision Support Systems, Clinical, Medical Order Entry Systems, Clinical Governance, Electronic Health Records, Patient Safety
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