Individualized Dynamic Patient Monitoring Under Alarm Fatigue

Hossein Piri (), Woonghee Tim Huh (), Steven M. Shechter () and Darren Hudson ()
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Hossein Piri: Haskayne School of Business, University of Calgary, Calgary, Alberta T2N 1N4, Canada
Woonghee Tim Huh: Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
Steven M. Shechter: Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
Darren Hudson: Department of Critical Care Medicine, University of Alberta, Edmonton, Alberta T6G 2B7, Canada

Operations Research, 2022, vol. 70, issue 5, 2749-2766

Abstract: Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. We develop a partially observable Markov decision process model for recommending dynamic, patient-specific alarms in which we incorporate a cry-wolf feedback loop of repeated false alarms. Our model takes into account patient heterogeneity in safety limits for vital signs and learns a patient’s safety limits by performing Bayesian updates during a patient’s hospital stay. We develop structural results of the optimal policy and perform a numerical case study based on clinical data from an intensive care unit. We find that compared with current approaches of setting patients’ alarms, our dynamic patient-centered model significantly reduces the risk of patient harm.

Keywords: Policy Modeling and Public Sector OR; warning systems; alarm fatigue; cry-wolf effect; partially observable Markov decision process; threshold policy; neural networks (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:70:y:2022:i:5:p:2749-2766

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