Elective Patient Admission and Scheduling under Multiple Resource Constraints

Elective Patient Admission and Scheduling under Multiple Resource Constraints

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Article ID: iaor2016471
Volume: 24
Issue: 12
Start Page Number: 1907
End Page Number: 1930
Publication Date: Dec 2015
Journal: Production and Operations Management
Authors: ,
Keywords: allocation: resources, health services, combinatorial optimization, stochastic processes, control processes, programming: markov decision, programming: dynamic, heuristics
Abstract:

We consider a patient admission problem to a hospital with multiple resource constraints (e.g., OR and beds) and a stochastic evolution of patient care requirements across multiple resources. There is a small but significant proportion of emergency patients who arrive randomly and have to be accepted at the hospital. However, the hospital needs to decide whether to accept, postpone, or even reject the admission from a random stream of non‐emergency elective patients. We formulate the control process as a Markov decision process to maximize expected contribution net of overbooking costs, develop bounds using approximate dynamic programming, and use them to construct heuristics. We test our methods on data from the Ronald Reagan UCLA Medical Center and find that our intuitive newsvendor‐based heuristic performs well across all scenarios.

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