2016-07-11 14:00:00 2016-07-11 15:00:00 America/Indiana/Indianapolis PhD Seminar - Joonyup Eun "Models and Optimization for Elective Surgery Scheduling Under Uncertainty Considering Patient Health Condition" GRIS 316
PhD Seminar - Joonyup Eun
| Event Date: | July 11, 2016 |
|---|---|
| Hosted By: | Prof. Yuehwern Yih |
| Time: | 2:00 - 3:00 PM |
| Location: | GRIS 316 |
| Contact Name: | Cheryl Barnhart |
| Contact Phone: | 765-494-5434 |
| Contact Email: | cbarnhar@purdue.edu |
| Open To: | all |
| Priority: | No |
| School or Program: | Industrial Engineering |
ABSTRACT
The managerial aspects to run a healthcare system are becoming increasingly important for patient safety. Patients are competing with 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. Patient safety is related to how to run a healthcare system with limited resources.
Surgery scheduling, one of the managerial aspects to run a healthcare system, can contribute to improving patient safety. Diseases exacerbate patient health conditions with increased 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 current diseases into more severe diseases. However, needs may not be satisfied due to 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 the sample average approximation. However, in general, the sample average approximation algorithm is time-consuming. Therefore, a fastest ascent local search and a tabu search are also developed to solve large-scale problems.