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Pre-Defense: Optimizing Task Scheduling in Emergency Departments

Abstract: 

An Emergency Department (ED) is a health care service that delivers time-critical care to unscheduled patient arrivals. Due to an ever
increasing number of arrivals, the number of patients often exceed the physical and staffing capacity resulting in long waiting times,
patients leaving without being seen by medical staff and higher mortality levels. In this work we investigate the scheduling of staff and equipment resources in EDs. We present a spatial > agent-based simulation framework to quantify the impacts of staff decision processes, such as patient selection, on patient length of stay and waiting times. Our simulations show there can be a 17%
difference in patient throughput depending upon patient selection policies. To investigate ED administration intuition that reducing
the length of stay of shorter visits (patients with simple cases) increases the patient throughput, we propose a scheduling heuristic that prioritizes shorter visits by using decision trees to classify ED vists. We found that by prioritizing shorter visits patient throughput increases by 8%. We also formulate the ED scheduling problem as a mixed-integer programming model to find the optimal static scheduling for our case study. Finally, we compare the average length of stay our heuristic scheduler against the optimal static scheduling and real data obtained from a NJ ED.

Speaker: 
Ana Paula Centeno
Location: 
CoRE B (305)
Event Date: 
08/13/2018 - 10:30am
Committee: 
Prof. Richard Martin (Chair), Prof. Thu Nguyen, Prof. Uli Kremer, Prof. Aritanan Gruber, University of ABC (Santo Andre, SP, Brazil)
Event Type: 
Pre-Defense
Organization: 
Dept. of Computer Science