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Data-driven Generic Discrete Event Simulation Model of Hospital Patient Flow Considering Surge
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chan, Wai Kin D'Ambrogio, Andrea Zacharewicz, Gregory Mustafee, Navonil Wainer, Gabriel A. Page, E. |
| Copyright Year | 2017 |
| Abstract | Many Canadian hospitals run at or near capacity, frequently experiencing congestion due to surges in demand. Hospital-wide bed management strategies, called “surge protocols”, that formally define when and what kind of operational steps can be taken to alleviate congestion are routinely in use. Decisions across the hospital, regarding bed capacity and allocation, staffing levels, and the master surgical schedule influence the frequency and severity of congestion, which in turn manifests in high bed occupancy, delayed admissions, a crowded emergency department (ED), surgical cancellations and increased use of surge protocols. A generic, data-driven, discrete event simulation (DES) is developed to help hospitals assess the impact of hospital-wide decisions, including surge policies, on congestion. The model has been developed in cooperation with two hospitals, and has been validated at two additional hospitals. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.informs-sim.org/wsc17papers/includes/files/250.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
