Cited by 115Healthcare Technology and Patient MonitoringPatient Safety and Medication ErrorsElectronic Health Records Systems

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Alarm system management: evidence-based guidance encouraging direct measurement of informativeness to improve alarm response

Rayo, Michael F, Moffatt-Bruce, Susan D·2015·BMJ Quality & Safety

DOI 10.1136/bmjqs-2014-003373

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Abstract

Although there are powerful incentives for creating alarm management programmes to reduce ‘alarm fatigue’, they do not provide guidance on how to reduce the likelihood that clinicians will disregard critical alarms. The literature cites numerous phenomena that contribute to alarm fatigue, although many of these, including total rate of alarms, are not supported in the literature as factors that directly impact alarm response. The contributor that is most frequently associated with alarm response is informativeness, which is defined as the proportion of total alarms that successfully conveys a specific event, and the extent to which it is a hazard. Informativeness is low across all healthcare applications, consistently ranging from 1% to 20%. Because of its likelihood and strong evidential support, informativeness should be evaluated before other contributors are considered. Methods for measuring informativeness and alarm response are discussed. Design directions for potential interventions, as well as design alternatives to traditional alarms, are also discussed. With the increased attention and investment in alarm system management that alarm interventions are currently receiving, initiatives that focus on informativeness and the other evidence-based measures identified will allow us to more effectively, efficiently and reliably redirect clinician attention, ultimately improving alarm response.

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Multiple Resources and Mental Workload
Wickens, C. D. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x288394 · Cited by 2,164
Joint Commission on Accreditation of Healthcare Organizations
Patterson, C. H. (1995) · Infection Control and Hospital Epidemiology · doi:10.1086/647001 · Cited by 1,338
Cell phone-induced failures of visual attention during simulated driving.
Strayer, D. L., Drews, F. A., & Johnston, W. (2003) · Journal of Experimental Psychology Applied · doi:10.1037/1076-898x.9.1.23 · Cited by 1,057
Mental workload while driving: effects on visual search, discrimination, and decision making.
Recarte, M. A., & Nunes, L. (2003) · Journal of experimental psychology. Applied · doi:10.1037/1076-898x.9.2.119 · Cited by 757
Monitor Alarm Fatigue : An Integrative Review
Cvach, M. M. (2012) · Biomedical Instrumentation & Technology · doi:10.2345/0899-8205-46.4.268 · Cited by 641
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Dekker, S. W. A. (2017) · CRC Press eBooks · doi:10.1201/9781315239675 · Cited by 538
Ecological Interface Design
Burns, C. M., & Hajdukiewicz, J. R. (2017) · CRC Press eBooks · doi:10.1201/9781315272665 · Cited by 463
The benefits of imperfect diagnostic automation: a synthesis of the literature
Wickens, C. D., & Dixon, S. R. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500370105 · Cited by 423
Human probability matching behaviour in response to alarms of varying reliability
Bliss, J. P., Gilson, R. D., & Deaton, J. E. (1995) · Ergonomics · doi:10.1080/00140139508925269 · Cited by 297
The alarm problem and directed attention in dynamic fault management
Woods, D. D. (1995) · Ergonomics · doi:10.1080/00140139508925274 · Cited by 276

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Authors: Rayo, M. F., & Moffatt-Bruce, S. D.
Affiliations: The Ohio State University (US)
Corresponding author: Rayo, Michael F
ORCID: Rayo, Michael F
Venue: BMJ Quality & Safety
Publisher: BMJ
Published: 2015-03-02
Volume: 24
Issue: 4
Pages: 282-286
ISSN: 1475-3898 (ISSN-L)
2044-5415
2044-5423
DOI: 10.1136/bmjqs-2014-003373
OpenAlex: W2096457720
Cited by: 115
References: 52
Citations by year: 2026: 9 · 2025: 6 · 2024: 5 · 2023: 17 · 2022: 6 · 2021: 10 · 2020: 16 · 2019: 11 · 2018: 13 · 2017: 9 · 2016: 10 · 2015: 3
FWCI: 2.23
Type: journal article
Language: en
Open access: closed
Locations: BMJ Quality & Safety (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords: alarm fatigue, alarm system, alarm response, alarm system design, patient safety
MeSH terms: Attention, Attitude of Health Personnel, Equipment Failure, Humans, Monitoring, Physiologic, Nursing Staff, Hospital, Reaction Time, Workload, Safety Management, Clinical Alarms

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Alarm system management: evidence-based guidance encouraging direct measurement of informativeness to improve alarm response

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