Individualized Dynamic Patient Monitoring Under Alarm Fatigue
Hossein Piri, Woonghee Tim Huh, Steven M. Shechter, Darren Hudson
Operations Research · 2022 · Volume 70 · Issue 5
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
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