Individualized Dynamic Patient Monitoring Under Alarm Fatigue

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.

Mathematics

Audience: researchers in the topic

PIMS-CORDS SFU Operations Research Seminar