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Piri, Hossein

University of British Columbia

This dissertation comprises two studies. In the first two chapters, we study individualized patient monitoring in hospitals under explicit consideration of 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. We develop a partially observable Markov decision process (POMDP) 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. In Chapter 2, we develop structural results of the optimal policy, and in Chapter 3 we perform a numerical case study based on clinical data from an intensive care unit (ICU). We find that compared to current approaches of setting patients’ alarms, our dynamic patient-centered model significantly reduces the risk of patient harm. In Chapter 4, we study elevator queue management during a pandemic. The social distancing requirement during COVID-19 reduced the elevator capacity in high-rise buildings by up to 70 %, which resulted in elevator queue build-up and increased the elevator wait time, thereby increasing the chance of the spread of the disease. We considered a real-life large clinic facility of the Vancouver general hospital (Diamond clinic) and studied the impact of rescheduling the clinic’s start time on patients’ average wait time as well as on the queue length in the lobby during busy periods. Our results showed that by rescheduling the clinics (that are originally scheduled at busy times) by a maximum of 30 minutes, the average wait time can decrease by up to 85%, and the maximum queue length in the lobby can decrease by up to 95%.

Text

2023-11-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83351

Business Administration - Management Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/