Improving Pharmacy Operations Through Simulation
Research Assistant Professor
State University of New York
This presentation presents a simulation approach to study and streamline pharmacy operations, and subsequently eliminate non-value added activities within pharmacy operations, especially within the realm of medication delivery. It should help pharmacy operations take a giant step towards optimizing their activities, from a macro and a micro-level, and consequently providing care more efficiently and effectively.
The focus of pharmacy operations is to deliver the right medications to the right patients at the right time, in the most efficient manner. This includes the choice of delivery routes to individual floors in a hospital and the associated delivery frequency. The relevant operations can be divided into three categories: receiving the orders, processing the orders, and delivering the medications.
The goal of this research endeavor was to study and streamline pharmacy operations, and subsequently eliminate non-value added activities within pharmacy operations, especially within the realm of medication delivery. This research should help pharmacy operations take a giant step towards optimizing their activities, from a macro and a micro-level, and consequently providing care more efficiently and effectively.
The pharmacy studied has a semi-automated structure, with electronic processing of the orders combined with manual pickup and delivery of medications. Apart from the main pharmacy, four decentralized (satellite) pharmacies are located at different floors with a set of pre-assigned nursing stations.
Due to the manual pickup and delivery of most of the orders, relatively high turn around times (or TAT) is seen during the peak periods. A discrete event simulation model was built and validated to study the TAT as well as the utilization of the pharmacy personnel. The results showed that the pattern for order delivery and pickup was the principal contributing factor to the high TAT. Alternative order pickup and drug delivery schedules were simulated and sensitivity analysis was conducted to identify the best practice, where a 50% reduction in TAT can be achieved without the use of additional resources. In addition, the impact of receiving all orders electronically was evaluated as an alternate scenario in the simulation model.