Models and optimization for elective surgery scheduling under uncertainty considering patient health condition
Abstract
The managerial aspects to run a healthcare system are becoming increasingly important for patient safety. More than one patients are competing each other to be treated using limited medical resources in a healthcare system. The limited medical resources include surgeons, physicians, anesthesiologists, nurses, operating rooms, wards, etc. Therefore, patient safety is related to how to run a healthcare system with the limited resources. Surgery scheduling, one of the managerial aspects to run a healthcare system, can contribute to improving patient safety. Diseases exacerbate patient health condition with the increase of waiting time for surgery. Therefore, surgeons and patients may want to schedule their surgeries as early as possible in order to escape from the risk of patients' deaths or the risk of turning the current diseases into more severe diseases. However, the needs may not be satisfied due to the limited medical resources. This research incorporates deteriorating patient health condition in elective surgery scheduling to improve patient safety. Two different models are presented: elective surgery scheduling models with 1) linearly deteriorating patient health condition and 2) step-deteriorating patient health condition. In this research, the basis to manage uncertainties in surgery durations and/or patient health condition is sample average approximation. However, in general, the sample average approximation algorithm is time consuming. Therefore, a fastest local search and a tabu search are also developed to solve large-size problems.
Degree
Ph.D.
Advisors
Yih, Purdue University.
Subject Area
Industrial engineering|Health care management
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