Alarm system management: evidence-based guidance encouraging direct measurement of informativeness to improve alarm response
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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Responses to textual alerts in EHRs are similar to previously documented responses to audible and visual alarms.
“Our findings suggest that responses to textual alerts in EHRs are similar to previously documented responses to audible and visual alarms. [16–18]”
From Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system (2017) · cites this paper as [16]
False alarms reduce responsiveness to alarms and may also reduce overall performance on tasks interrupted by alarms.
“it is well-established in the human factors literature that false alarms reduce responsiveness to alarms and may also reduce overall performance on tasks interrupted by alarms. [16–18]”
From Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system (2017) · cites this paper as [16]
Uninformative alerts are similar to false alarms.
“Uninformative alerts are similar to false alarms”
From Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system (2017) · cites this paper as [16]
The human factors literature in other settings shows that high false alarm rates reduce responsiveness as the individual learns that the alarm is unreliable.
“The human factors literature in other settings shows that high false alarm rates reduce responsiveness as the individual learns, consciously or unconsciously, that the alarm is unreliable [16–18].”
From Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system (2017) · cites this paper as [16]
In other settings, low alarm informativeness and increased cognitive workload predict poor alarm response.
“In other settings, low alarm informativeness and increased cognitive workload predict poor alarm response.”
From Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system (2017) · cites this paper as [16]
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Situated Visual Alarm Displays Support Machine Fitness Assessment for Nonexplainable Automation
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Attention Among Health Care Professionals
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Beyond Monitors: Intravenous Smart Pump Alarm Fatigue As a Safety Concern
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Patient Safety Implications of Electronic Alerts and Alarms of Maternal–Fetal Status During Labor
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Utilizing the Alarm Taxonomy and Classification System (ATACS) to Redesign Landing Gear Warnings
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Guidance for Custom Alarm Design: You Don’t Have to “Crank it up to 11”
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Self-citationPrevalence, taxonomy, and outcomes of inpatient imaging incidental findings
Nair, P., Madhani, N., Block, B., Ripley, L., Warsi, M., Hegde, A. A., & Chu, E. S. (2023) · Human Factors in Healthcare · doi:10.1016/j.hfh.2023.100037
- 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
- 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 › Health Professions › Emergency Medical Services › 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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