Evaluation of Harm Associated with High Dose-Range Clinical Decision Support Overrides in the Intensive Care Unit
Open accessAbstract
Introduction Medication-related clinical decision support (CDS) alerts have been shown to be effective at reducing adverse drug events (ADEs). However, these alerts are frequently overridden, with limited data linking these overrides to harm. Dose-range checking alerts are a type of CDS alert that could have a significant impact on morbidity and mortality, especially in the intensive care unit (ICU) setting. Methods We performed a single-center, prospective, observational study of adult ICUs from September 2016 to April 2017. Targeted overridden alerts were triggered when doses greater than or equal to 5% over the maximum dose were prescribed. The primary outcome was the appropriateness of the override, determined by two independent reviewers, using pre-specified criteria formulated by a multidisciplinary group. Overrides which resulted in medication administration were then evaluated for ADEs by chart review. Results The override rate of high dose-range alerts in the ICU was 93.0% (total n = 1525) during the study period. A total of 1418 overridden alerts from 755 unique patients were evaluated for appropriateness (appropriateness rate 88.8%). The most common medication associated with high dose-range alerts was insulin regular infusion (n = 262, 18.5%). The rates of ADEs for the appropriately and inappropriately overridden alerts per 100 overridden alerts were 1.3 and 5.0, respectively (p < 0.001). Conclusions Overriding high dose-range CDS alerts was found to be common and often appropriate, suggesting that more intelligent dose checking is needed. Some alerts were clearly inappropriately presented to the provider. Inappropriate overrides were associated with an increased risk of ADEs, compared to appropriately overridden alerts.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [65]
The Results report that 93.0% of high dose-range alerts were overridden in the studied ICUs, and the Discussion recommends turning off unnecessary warnings. The paper does not report physician burnout, whether physical or cognitive, or establish that alert burden caused the overrides. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Nature of Adverse Events in Hospitalized Patients
Leape, L. L., Brennan, T. A., Laird, N. M., Lawthers, A. G., Localio, A. R., Barnes, B. A., Hebert, L. E., Newhouse, J. P., Weiler, P. C., & Hiatt, H. H. (1991) · New England Journal of Medicine · doi:10.1056/nejm199102073240605 · Cited by 3,766 · see citing works
Bates, D. W. (1998) · JAMA · doi:10.1001/jama.280.15.1311 · Cited by 2,107 · see citing works
Self-citationThe Impact of Computerized Physician Order Entry on Medication Error Prevention
Bates, D. W., Teich, J. M., Lee, J., Seger, D., Kuperman, G. J., Ma'Luf, N., Boyle, D., & Leape, L. (1999) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.1999.00660313 · Cited by 1,260 · see citing works
Self-citationThe Costs of Adverse Drug Events in Hospitalized Patients
Bates, D. W. (1997) · JAMA · doi:10.1001/jama.1997.03540280045032 · Cited by 1,072 · see citing works
Self-citationRothschild, J. M., Landrigan, C. P., CRONIN, J. W., Kaushal, R., Lockley, S. W., Burdick, E., Stone, P. H., Lilly, C. M., Katz, J. T., Czeisler, C. A., et al. (2005) · Critical Care Medicine · doi:10.1097/01.ccm.0000171609.91035.bd · Cited by 925 · see citing works
Self-citationPreventable adverse drug events in hospitalized patients
Cullen, D. J., Sweitzer, B. J., Bates, D. W., Burdick, E., Edmondson, A. C., & Leape, L. L. (1997) · Critical Care Medicine · doi:10.1097/00003246-199708000-00014 · Cited by 611 · see citing works
Self-citationFortescue, E. B., Kaushal, R., Landrigan, C. P., McKenna, K. J., Clapp, M., Federico, F. A., Goldmann, D. A., & Bates, D. W. (2003) · PEDIATRICS · doi:10.1542/peds.111.4.722 · Cited by 462 · see citing works
Self-citationComputerized Physician Order Entry and Medication Errors in a Pediatric Critical Care Unit
Potts, A. L., Barr, F. E., Gregory, D. F., Wright, L., & Patel, N. R. (2004) · Pediatrics · doi:10.1542/peds.113.1.59 · Cited by 410 · see citing works
Adverse drug events and medication errors: detection and classification methods
Morimoto, T. (2004) · BMJ Quality & Safety · doi:10.1136/qshc.2004.010611 · Cited by 402 · see citing works
Medication Prescribing Errors in a Teaching Hospital
Lesar, T. S. (1990) · JAMA · doi:10.1001/jama.1990.03440170051035 · Cited by 397 · see citing works
Overrides of medication-related clinical decision support alerts in outpatients
Nanji, K. C., Slight, S. P., Seger, D. L., Cho, I., Fiskio, J. M., Redden, L. M., Volk, L. A., & Bates, D. W. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001813 · Cited by 219 · see citing works
Self-citationKaushal, R., Barker, K. N., & Bates, D. W. (2001) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpedi.155.9.1002 · Cited by 204 · see citing works
Self-citationMedication-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
Self-citationAdverse Drug Events in Hospitalized Patients
Classen, D. C., Pestotnik, S. L., Evans, R. S., Lloyd, J. F., & Burke, J. P. (1998) · Survey of Anesthesiology · doi:10.1097/00132586-199806000-00009 · Cited by 170 · see citing works
Lin, C.-P., Payne, T. H., Nichol, W. P., Hoey, P. J., Anderson, C. L., & Gennari, J. H. (2008) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2453 · Cited by 110 · see citing works
Wong, A., Amato, M. G., Seger, D. L., Rehr, C., Wright, A., Slight, S. P., Beeler, P. E., Orav, E. J., & Bates, D. W. (2018) · BMJ Quality & Safety · doi:10.1136/bmjqs-2017-007531 · Cited by 77 · see citing works
Self-citationPatient-specific electronic decision support reduces prescription of excessive doses
Seidling, H. M., Schmitt, S. P. W., Bruckner, T., Kaltschmidt, J., Pruszydlo, M. G., Senger, C., Bertsche, T., Walter‐Sack, I., & Haefeli, W. E. (2010) · BMJ Quality & Safety · doi:10.1136/qshc.2009.033175 · Cited by 71 · see citing works
Wong, A., Amato, M. G., Seger, D. L., Slight, S. P., Beeler, P. E., Dykes, P. C., Fiskio, J. M., Silvers, E. R., Orav, E. J., Eguale, T., et al. (2017) · Journal of Critical Care · doi:10.1016/j.jcrc.2017.02.027 · Cited by 41 · see citing works
Self-citationAutomated Dose-Rounding Recommendations for Pediatric Medications
Johnson, K. B., Lee, C. K. K., Spooner, S. A., et al. (2011) · PEDIATRICS · doi:10.1542/peds.2011-0760 · Cited by 40 · see citing works
Rehr, C., Wong, A., Seger, D. L., & Bates, D. W. (2018) · Applied Clinical Informatics · doi:10.1055/s-0038-1642608 · Cited by 24 · see citing works
Self-citationDetection and prevention of prescriptions with excessive doses in electronic prescribing systems
Seidling, H. M., Barmawi, A. A., Kaltschmidt, J., Bertsche, T., Pruszydlo, M. G., & Haefeli, W. E. (2007) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-007-0370-9 · Cited by 17 · see citing works
Developing strategies for predicting hyperkalemia in potassium-increasing drug-drug interactions
Eschmann, E., Beeler, P. E., Schneemann, M., & Blaser, J. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw050 · Cited by 13 · see citing works
Self-citationDecision support for sensible dosing in electronic prescribing systems
Coleman, J. J., Nwulu, U., & Ferner, R. E. (2011) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1111/j.1365-2710.2011.01310.x · Cited by 7 · see citing works
Deriving Dose Limits for Warnings in Electronic Prescribing Systems
Coleman, J. J., Hodson, J. A., & Ferner, R. E. (2012) · Drug Safety · doi:10.2165/11594810-000000000-00000 · Cited by 7 · see citing works
Nature and causes of clinically significant medication errors in a tertiary care hospital
Winterstein, A. G., Johns, T. E., Rosenberg, E. I., Hatton, R. C., Gonzalez-Rothi, R., & Kanjanarat, P. (2004) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/61.18.1908
Flagged first, then by citations
Cited by
Sorted by citations
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
“Multiple studies have reported that a high frequency of clinically irrelevant alerts (repetitive alerts with minimal clinical value), mediocre functionality (minimal integration among various departments and lack of alerts prioritization), and erroneous assessment by physicians are the main reasons for inappropriate o…”
BackgroundInfluentialStructured override reasons for drug-drug interaction alerts in electronic health records
Wright, A., McEvoy, D. S., Aaron, S., McCoy, A. B., Amato, M. G., Kim, H., Ai, A., Cimino, J. J., Desai, B. R., El-Kareh, R., et al. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz033 · Cited by 61 · see citing works
“However, medication-related CDS alerts are frequently overridden,(9,10) sometimes with good cause, as adverse events rarely occur when prescribers override even the highest-level alerts.(11,12) Historically, the rationale for high override rates was alert fatigue and poor usability, but recent research suggests a more…”
BackgroundSelf-citationKawamoto, K., & McDonald, C. J. (2020) · Annals of Internal Medicine · doi:10.7326/m19-0875 · Cited by 72 · see citing works
“However, clinicians have ignored up to 93% of alerts (15), and they complain about excessive rates of false-positive reminders (16–18).”
BackgroundYoo, J., Lee, J.-H., Rhee, P., Chang, D. K., Kang, M., Choi, J. S., Bates, D. W., & Cha, W. C. (2020) · JMIR Medical Informatics · doi:10.2196/23351 · Cited by 27 · see citing works
“However, previous studies have revealed that physicians’ override rates on CDSS alerts are high [7-10], raising concerns about the effectiveness of CDSSs in many implementations [11-13].”
BackgroundSelf-citationKnight, A. M., Maygers, J., Foltz, K. A., John, I. S., Yeh, H. C., & Brotman, D. J. (2019) · Applied Clinical Informatics · doi:10.1055/s-0039-3400447 · Cited by 22 · see citing works
Justinia, T., Qattan, W., Almenhali, A., Abo-Khatwa, A., Alharbi, O. A., & Alharbi, T. (2021) · Acta Informatica Medica · doi:10.5455/aim.2021.29.248-252 · Cited by 16 · see citing works
“This leads to the realization that there is a percentage of inappropriate overriding and sometimes vital alerts that can be missed, increasing the risk of medication errors negatively impacting patient safety (5, 12, 14).”
BackgroundHolbrook, A. M., Silva, J. M., Faruque, J., Deng, J., Schneider, T., & Jaffer, A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf139 · Cited by 19 · see citing works
Wong, A., Berenbrok, L., Snader, L., Soh, Y. H., Kumar, V., Javed, M., Bates, D., Sorce, L., & Kane-Gill, S. (2023) · Critical Care Explorations · doi:10.1097/cce.0000000000000967 · Cited by 13 · see citing works
Self-citationChin, D., Wilson, M. H., Trask, A. S., Johnson, V. T., Neaves, B., Gojová, A., Hogarth, M., Bang, H., & Romano, P. (2020) · Journal of medical systems · doi:10.1007/s10916-020-01603-9 · Cited by 10 · see citing works
Dullabh, P., Zott, C., Gauthreaux, N. A., Swiger, J., Lomotan, E. A., & Sittig, D. F. (2025) · Journal of Medical Internet Research · doi:10.2196/68674 · Cited by 7 · see citing works
“Due to the influx of CDS alerts and reminders in EHR systems that interrupt workflows, it is common for clinicians to override alerts [54,55], with some studies showing that a third of CDS interventions have had no effect [13].”
Clinical Decision Support in Statin Prescription-What We Can Learn From A Negative Outcome.
Maddox, T. (2020) · JAMA cardiology · doi:10.1001/jamacardio.2020.4756 · Cited by 6 · see citing works
Chen, 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
High alert medications off the radar: A systematic review
Menezes, M. S., Valença-Feitosa, F., Góes, A. S., dos Santos, M. R., Silva, L. S., dos Santos, S. N. P., de Lyra, D. P., & de Oliveira, A. D. (2024) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2024.100551 · Cited by 2
“42,43 In a study carried out in the USA, where a program was created to generate alerts in the prescription of doses greater than or equal to 5 % above the maximum daily dose recommended by protocols and any high dose of high-risk drugs, as identified by the ISMP, 1 a total of 139 drugs were…”
Yoo, J., Lee, J., Rhee, P., Chang, D. K., Kang, M., Choi, J., Bates, D., & Cha, W. (2020) · doi:10.2196/preprints.23351 · Cited by 1
Self-citationRan, S., Gu, D., Xie, J., Jiang, K., Yao, N., Fang, Z., Asakawa, T., Zhang, N., & Fang, H. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag072
“19 It is also known that inappropriate alert overrides may lead to ADEs causing patient harm 20–22 and resulting in significant costs.”
Naji, A. M., Bagal, R. M. A., Khormi, A. H. I., Masrai, A. Y. A., Hakami, A., Maswdi, A. M. M., Sayed, M., Zalah, A. A. A., Khormi, M. M. A., Mashragi, T. Q. M., et al. (2025) · Saudi Journal of Medicine and Public Health · doi:10.64483/jmph-184
“This is an initial quality check of utmost significance; one study indicated that an NLP system was able to detect potential errors in 15% of prescriptions already cleared by the technician, an important protection prior to final checking by the pharmacist (12, 13).”
Bakhtiar, M. I., Yasin, N., Andalusia, L. R., & Sari, I. P. (2025) · Research Journal of Pharmacy and Technology · doi:10.52711/0974-360x.2025.00733
Shakiba, C., Esmaeili, A., Pirnejad, H., & Niazkhani, Z. (2024) · Frontiers in Health Informatics · doi:10.30699/fhi.v13i0.547
“Among the proposed strategies to reduce alert fatigue are the consideration of patient conditions (e.g., age, prescribed drugs, and history of renal failure), monitoring of their laboratory test results [6, 47]. and categorization of alerts by type and clinical significance [48].”
BackgroundPractice-enhancing publications about the medication-use process in 2019.
Vest, T. A., Gazda, N. P., Schenkat, D. H., & Eckel, S. F. (2020) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxaa355
Outcome of overriding high drug dose-range alerts
(2018) · Reactions weekly · doi:10.1007/s40278-018-55806-7
Chen, H., Liu, F., & Zhao, L. (2024) · Journal of Health Informatics and Digital Systems · doi:10.68159/e176116242
Omar, I. A., Simsekler, M. C. E., Salah, K., & Omar, M. (2026) · Safety Science · doi:10.1016/j.ssci.2026.107458
- Authors
- Wong, A., Rehr, C., Seger, D. L., Amato, M. G., Beeler, P. E., Slight, S. P., Wright, A., & Bates, D. W.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Bates, David W.
- ORCID
- Venue
- Drug Safety
- Published
- 2018-12-01
- Volume
- 42
- Issue
- 4
- Pages
- 573-579
- ISSN
- 0114-5916 (ISSN-L)
- 1179-1942
- OpenAlex
- W2902213486
- Cited by
- 21
- References
- 0
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 1 · 2021: 1 · 2020: 6 · 2019: 2 · 2018: 1
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Drug Safety (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- American College of Clinical Pharmacy Research Institute (2018)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Keywords
- clinical decision support alerts, alert override, adverse drug reactions, intensive care unit, insulin infusion, drug safety
- MeSH terms
- Data Collection, Pharmaceutical Preparations, Female, Humans, Intensive Care Units, Male, Medication Errors, Middle Aged, Prospective Studies, Decision Support Systems, Clinical, Medical Order Entry Systems, Drug-Related Side Effects and Adverse Reactions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
