Individualized dynamic patient monitoring under alarm fatigue
Hossein Piri1, Steven Shechter1, Tim Huh1, Darren Hudson2
1University of British Columbia, Canada; 2University of Alberta, Canada
Discussant: Andrew Daw (University of Southern California, Marshall School of Business)
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. We find that compared to current approaches of setting patients’ alarms, our dynamic patient-centred model significantly reduces the risk of patient harm.
Split liver transplantation: An analytical decision support model
Yanhan Tang1, Alan Scheller-Wolf1, Sridhar Tayur1, Emily Perito2, John Roberts2
1Carnegie Mellon University, United States of America; 2The University of California, San Francisco, United States of America
Discussant: Vahid Sarhangian (University of Toronto)
Split liver transplantation (SLT) can save two lives using one liver. To facilitate increased SLT usage, we formulate a multi-queue fluid model, incorporating size matching specifics, dynamic health conditions, transplant type, and fairness. We find the optimal organ allocation policy, and evaluate its performance versus other common allocations.
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