Cited by 24Healthcare Technology and Patient MonitoringPatient Safety and Medication ErrorsClinical Reasoning and Diagnostic Skills

Assessing Causes of Alarm Fatigue in Long-Term Acute Care and Its Impact on Identifying Clinical Changes in Patient Conditions

Open access
Baker, Kathy, Rodger, James·2020·Informatics in Medicine Unlocked

Abstract

Physiologic alarms are an important modality in the care of critically ill patients. Yet the many electronic devices used in patient care and the combination of alarms can cause sensory overload in caregivers. This sensory overload can lead to monitor fatigue, and caregivers may miss critical alarms, which can be fatal for patients. Many factors not related to a change in patients' condition can be directly linked to desensitization and alarm fatigue, leading to a failure to recognize or attend to true instability in spite of the alarm. Research demonstrates that the majority of alarms are non-actionable, and staff can develop alarm fatigue trying to determine which alarms are valid and which are not. We postulate that more experience detecting false alarms among professionals in a long-term acute care unit will lead to improved clinical changes and better survival rates among patients. Our proportional hazards model relates missing clinical changes in patients' condition as time passes, after reduced attention to false alarms, to professional experience. In our proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. Therefore, reduced attention to false alarms by experienced professionals decreases the hazard rate for missing a clinical change. We use survival analyses, the hazard function, the receiver-operating characteristic curve, and the Hosmer-Lemeshow test to support our conclusions. Our results show that monitoring equipment is instrumental in alerting staff in a long-term care unit to serious changes in patients’ condition and in preventing false positives and false negatives.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.imu.2020.100300

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Alarm fatigue desensitizes clinicians to real alerts, causing delays or missed recognition of critical patient changes and increasing diagnostic errors.

    “Additionally, alarm fatigue desensitizes healthcare clinicians to real alerts, causing delays or missed recognition of critical patient changes and increasing diagnostic errors due to the overwhelming presence of false or non-actionable alarms [301].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [301]

    The Abstract and Conclusions (page unavailable) link false or non-actionable alarms to desensitization and failure to recognize or attend to genuine changes in a patient’s condition. The Introduction also describes delayed responses and missed alarms. Our copy does not establish that alarm fatigue increases diagnostic errors, so that part of the attributed claim is unsupported. (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.

The citation lists haven’t been fetched yet

References 26 · Cited by 24

Research works printed but not linked: 27

  1. Lacker C. Physiologic alarm management. Patient Saf Advis 2011;8:105-8.
  2. Graham K, Cvach M. Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care 2010;19:28-34.
  3. Pelczarski K. Joint commission on accreditation of Healthcare organizations. Sound the alarm: managing physiologic monitoring systems. Joint Comm Perspect Patient Saf 2011;11(6-8):11.
  4. Sendelbach & Funk. Alarm fatigue: a patient safety concern. 2013 Oct-Dec AACN Adv Crit Care 2013;24(4):378-86. https://doi.org/10.1097/ NCI.0b013e3182a903f9. quiz 387-8. · DOI
  5. Hravnak M, Pellathy T, Chen L, Dubrawski A, Wertz A, Clermont G, et al. A call to alarms: current state and future directions in the battle against alarm fatigue. J Electrocardiol 2018;51:S44-8.
  6. Cho Ok Min, Kim Hwasoon, Young Whee Lee, Cho Insook. Clinical alarms in intensive care units: perceived obstacles of alarm management and alarm fatigue in nurses. Healthc Inf Res 2016 January;22(1):46-53. https://doi.org/10.4258/ hir.2016.22.1.46. · DOI
  7. Jiang D, Wang H, Li J, Wu Y, Fang M, Yang R. Cox regression model for dissecting genetic architecture of survival time. Genomics 2014;104:472-6.
  8. Vivekanandan T, Narayanan SJ. A hybrid risk assessment model for cardiovascular disease using Cox regression analysis and a 2-means clustering algorithm. Comput Biol Med 2019;113:103400.
  9. Liu C, Wang X, Genchev GZ, Lu H. Multi-omics facilitated variable selection in Cox- regression model for cancer prognosis prediction. Methods 2017;124:100-7.
  10. S€ orensen A, Blazhenets G, Rücker G, Schiller F, Meyer PT, Fring L. Prognosis of conversion of mild cognitive impairment to Alzheimer's dementia by voxel-wise Cox regression based on FDG PET data. Neuroimage: Clin 2019;21:101637.
  11. Cort� es J, Gonz� alez JA, Campbell MJ, Cobo E. A hazard ratio was estimated by a ratio of median survival times, but with considerable uncertainty. J Clin Epidemiol 2014;67:1172-7.
  12. Amanat M, Vaccaro AR, Salehi M, Rahimi-Movaghar V. Neurological conditions associated with spinal cord injury. Inform Med Unlocked 2019;16:51-9.
  13. Sahooa S, Sengupta D. Testing the hypothesis of increasing hazard ratio in two samples. Comput Stat Data Anal 2017;114:119-29.
  14. Barraclough H, Simms L, Govindan R. Biostatistics prime: what a clinician ought to know: hazard ratios. J Thorac Oncol 2011;6:978-82.
  15. Sutradhar R, Austin PC. Relative rates not relative risks: addressing a widespread misinterpretation of hazard ratios. Ann Epidemiol 2018;28:54-7.
  16. Li Q, Clifford GD. Signal quality and data fusion for false alarm reduction in the intensive care unit. J Electrocardiol 2012;45:596-603.
  17. Sabeti E, Reamaroon N, Mathis M, Gryak J, Sjoding M, Najarian K. Signal quality measure for pulsatile physiological signals using morphological features: applications in reliability measure for pulse oximetry. Inform Med Unlocked 2019; 16:100222.
  18. Johansson L, Lindahl B, Knutsson S, € Ogren M, Waye KP, Ringdal M. Evaluation of a sound environment intervention in an ICU: a feasibility study. Aust Crit Care 2018; 31:59-70.
  19. Chaudhary MA, Schoenfeld AJ, Koehlmoos TP, Cooper Z, Haider AH. Prolonged ICU stay and its association with 1-year trauma mortality: an analysis of 19,000 American patients. Am J Surg 2019;218:21-6.
  20. May S, Hosmer DW. Hosmer and Lemeshow type goodness-of-fit statistics for the Cox proportional hazards model. In: Handbook of statistics, vol. 23; 2003. p. 383-94.
  21. Wang B, Yang S, Huang Z, Li D, He J, Wang X. Data mining-based subhealth analysis of Chinese software programmers in 2017. Inform Med Unlocked 2018;10: 134-42.
  22. Hu Z, Chen S, Du J, Gu D, Zheng Z. An in-hospital mortality risk model for patients undergoing coronary artery bypass grafting in China. Ann Thorac Surg, doi: 10.1016/j.athoracsur.2019.08.020. · DOI
  23. Li R, Su Z, Mendelsohn A, Gemmen E. Extension of the Hosmer-Lemeshow goodness of fit statistic to linear models with repeated measurements. Value Health 2013;16:A48.
  24. Donnelly RA. Business statistics. New York: New York: Pearson; 2019. p. 5-7. 359- 365; 684-687; 732-737.
  25. V., Fiedler, K., & Teng, J. (1997). Empirical evidence of Swanson's tricore model of information systems innovation. Information Systems Research, 8(3), 273-287..
  26. Balogh D, Kittinger E, Benzer A, Hackl JM. Noise in the ICU. Intensive Care Med 1993;19:343-6.
  27. Kam PCA, Kam AC, Thompson JF. Noise pollution in the anaesthetic and intensive care environment. Anaesthesia 1994;49:982-6.

Authors
Baker, K., & Rodger, J.
Affiliations
Indiana University of Pennsylvania (US)
Corresponding author
Rodger, James
Venue
Informatics in Medicine Unlocked
Publisher
Elsevier BV
Published
2020
Volume
18
Pages
100300
ISSN
2352-9148 (ISSN-L)
OpenAlex
W3005343301
Cited by
24
References
26
Citations by year
2026: 5 · 2025: 3 · 2024: 4 · 2023: 5 · 2022: 3 · 2021: 3 · 2020: 1
FWCI
2.75
Type
journal article
Language
en
Open access
Locations
  • Informatics in Medicine Unlocked (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
  • Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Keywords
alarm fatigue, long-term acute care, false alarms, desensitization, employee experience, patient survival, intensive care monitoring, proportional hazards model

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