Date of Award

2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

First Advisor

Qu, Dr. Xiuli

Abstract

Laboratory services in healthcare delivery systems play a vital role in inpatient care. Studies have shown that laboratory data affects approximately 65% of the most critical decisions on admission, discharge, and medication. Laboratory testing accounts for approximately 10% of hospital billing. Reducing laboratory costs would contribute to reducing total healthcare cost, which is one of the major goals for the U.S. healthcare delivery system. This research focuses on improving the performance of the hospital laboratory in a large hospital system. The intention of this study is to identify and then optimize the most critical stage to improve the entire laboratory testing process. Using analytic hierarchy process (AHP) and analytic network process (ANP) modeling, the preanalytical stage was identified as most critical. Then, a two-stage stochastic integer linear programming (SILP) model was formulated to determine better weekly phlebotomist schedules and blood collection assignments in the preanalytical stage. The objective of the two-stage SILP is to balance the workload of the phlebotomists within and between shifts, as reducing workload imbalance would result in improved patient care. Due to the size of the two-stage SILP problem, a scenario reduction model and a heuristic algorithm were proposed to solve the problem. The performance evaluation results show that for practical cases the heuristic algorithm proposed could find near optimal solutions with a relative gap less than 3.5% within 20 minutes. The two-stage SILP model and the heuristic algorithm proposed will assist laboratory management in balancing phlebotomist workload, which could reduce the risk of poor phlebotomist performance and patient neglect caused by work overload. By implementing the recommendations of this study, hospital laboratories should see significant improvements in workload balance and resource utilization, which are both considered cost savings strategies.

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