[CCoE Notice] Ph.D. Dissertation Defense Announcement

[Khator, Suresh]

University of Houston

Department of Industrial Engineering





Ph.D. Dissertation Defense


Development of the Patient Scheduling System to
Handle Patient No-Show Problem
Using Data Mining and Simulation-Based Optimization Techniques

Ph.D. Candidate: Parviz Kheirkhah
Advisor: Dr. Qianmei Feng

Thursday November 18th 2010, 1:00 PM
Industrial Engineering Conference Room (E214), Eng Bldg 2





Abstract

The number of patients who do not show up for their appointments significantly impacts the delivery of care, cost, and resource planning in most healthcare systems. We studied the effects of the no-show problem on Veterans Health care Administration (VHA) in zone 16 and specifically Michael E. DeBakey VA Medical Center in Houston (MEDVAMC) with more than 750,000 visits per year. One of the promising methodologies to resolve the no-show problem is overbooking, i.e., booking more patients than available appointment slots.

The objective of this research is developing the methodology and the decision support system to assist the appointment scheduler in patient overbooking and scheduling that minimize the patients' waiting times and the overtime working of physicians and staffs, and also maximize the resource utilization. The relevant historical data for scheduled and walk-in patients are collected for two recent years in MEDVAMC.

We conducted statistical and economic analysis of no-show rates and costs in VA hospitals in zone 16 and specifically in MEDVAMC. The significant factors on no-show are identified by maximum likelihood estimates of the logistic regression model and the stepwise selection technique. Furthermore, we developed two data mining models based on logistic regression and support vector machines (SVMs) for probability estimating of patient's no-show. We also developed the prediction model for the number of walk-ins in Primary Care clinic by using the density function technique.

We developed the dynamic outpatient appointment overbooking and scheduling model that builds the patients schedule sequentially through a call-in process. A simulation-based optimization model is developed that can involve the realistic characteristics of the appointment scheduling system. The proposed model relaxes unrealistic assumptions in the exiting analytical models. We designed sensitivity analyses for the coefficients of the objective function and simulation iteration using numerical experiments. Finally, the efficiency of our model is tested by numerical experiments.

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