Maintaining efficient levels of reusable medical equipment (RME) inventory is vital for many healthcare systems due to their high cost. On the other hand hospitals cannot risk lower service levels for RME availability in such vital departments as surgery, emergency, and ICU/PACU by optimizing the inventory levels solely based on ordering and holding costs. With the primary goal of access to quality care, frequent RME shortages and subsequent delays and cancellations in healthcare systems is not acceptable and needs to be balanced with the cost of maintaining inventory. In this study, we propose a simulation based framework for determining RME inventory levels in surgical services while balancing these competing performance criteria. Using historical surgery schedules, we first conducted a static and retrospective inventory analysis to determine the RME utilization levels based on historical inventory levels and minimum level RME inventory requirements based on surgery demand. Next, we generated surgery schedules using historical schedule data to be used in optimizing RME inventory levels to balance 1. Waiting time minimization due to RME shortage, 2. Reducing inventory cost of RME trays. We developed an integrated discrete event simulation model to capture the closed-loop circulation of the RME between the sterile processing services (SPS) department and operating theater. We evaluated two SPS RME processing strategies: RME sequencing by prioritization and FIFO system for sequencing RME trays in the sterilization department. Using the simulation model, we studied the impact of two sequencing strategies on RME shortage induced delays. We tested the effect of sequencing strategies using both the historical inventory levels and minimum required RME tray inventory levels obtained through static analysis. Finally, we optimized the inventory levels using infinitesimal perturbation analysis (IPA) by initializing the IPA with the historical and minimum required inventory levels.
[1]
H. Wolfe,et al.
Scheduling a multiple operating room system: a simulation approach.
,
1968,
Health services research.
[2]
Amr Arisha,et al.
Application of Discrete-Event Simulation in Health Care: a Review
,
2009
.
[3]
F. Dexter,et al.
An operating room scheduling strategy to maximize the use of operating room block time: computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time.
,
1999,
Anesthesia and analgesia.
[4]
J. Ledolter,et al.
Analysis of Operating Room Allocations to Optimize Scheduling of Specialty Rotations for Anesthesia Trainees
,
2010,
Anesthesia and analgesia.
[5]
John K. Jackman,et al.
Infinitesimal Perturbation Analysis: A Tool for Simulation
,
1989
.
[6]
David M. Ferrin,et al.
Analyzing incentives and scheduling in a major metropolitan hospital operating room through simulation
,
2004,
Proceedings of the 2004 Winter Simulation Conference, 2004..
[7]
Kathy E. Fitzpatrick,et al.
An application of computer simulation to improve scheduling of hospital operating room facilities in the United States
,
2014
.
[8]
F. Dexter,et al.
Scheduling Surgical Cases into Overflow Block Time— Computer Simulation of the Effects of Scheduling Strategies on Operating Room Labor Costs
,
2000,
Anesthesia and analgesia.
[9]
Amr B. Eltawil,et al.
A Proposed Solution Framework for the Operating Room Scheduling Problems
,
2013
.
[10]
Erik Demeulemeester,et al.
Operating room planning and scheduling: A literature review
,
2010,
Eur. J. Oper. Res..
[11]
Rym M'Hallah,et al.
The planning and scheduling of operating rooms: A simulation approach
,
2014,
Comput. Ind. Eng..