Individualized Dynamic Patient Monitoring Under Alarm Fatigue
Hossein Piri, Woonghee Tim Huh, Steven M. Shechter, Darren Hudson
Abstract
Individualized Patient Monitoring Under Alarm Fatigue 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. “Individualized Dynamic Patient Monitoring Under Alarm Fatigue” by Piri, Huh, Shechter, and Hudson studies the problem of personalizing alarm thresholds for vital signs at a hospital while considering the ”boy who cried wolf” effect of false alarms. The authors create a model that learns patients’ personal alarm thresholds during their hospital stay and updates their alarm settings dynamically. They formulate the problem as a partially observable Markov decision process. They provide structural properties of the optimal policy and perform a numerical case study based on clinical data from an intensive care unit. They show that dynamic methods of alarm settings that explicitly consider the feedback loop of false positives can significantly reduce patient harm when compared with current methods of alarm settings.