Applying Scenario Reduction Heuristics in Stochastic Programming for Phlebotomist Scheduling

摘要

Laboratory services in healthcare play a vital role in inpatient care. Studies have indicated laboratory data affects approximately 65% of the most critical decisions on admission, discharge, and medication. Laboratory testing accounts for approximately 10% of hospital billing. Most hospital laboratories face the challenge to reduce costs and improve service quality. Reducing laboratory costs could contribute to reducing total healthcare cost, which is one of the major goals for the healthcare delivery system. This research focuses on improving phlebotomy performance in laboratory facilities of large hospital systems. A two-stage stochastic integer linear programming (SILP) model is formulated to determine better weekly phlebotomist schedules and blood collection assignments. The objective of the two-stage SILP model is to balance the workload of the phlebotomists within and between shifts, as reducing workload imbalance will result in improved patient care. Due to the size of the two-stage SILP model, a scenario reduction model has been proposed as a solution approach. The scenario reduction heuristic is formulated as a linear programming model and the results indicate the scenarios with the largest likelihood of occurrence. These selected scenarios will be tested in the two-stage SILP model to determine weekly scheduling policies and blood draw assignments that will balance phlebotomist workload and improve overall performance.