Quantitative Analysis of Operating Room Inventory Management Practices at a Tertiary Cancer Center
- Fayez A. Quereshy, MD, MBA, FRCS(C)⇓ and
- Joseph Milner, PhD
+ Author Affiliations
- University Health Network; and Rotman School of Management, University of Toronto, Toronto, Ontario, Canada
- Corresponding author: Fayez A. Quereshy, MD, MBA, FRCS(C), Princess Margaret Hospital, University Heath Network, Department of Surgical Oncology, Room 3 130, 610 University Ave, Toronto, Ontario, Canada M5G 2M9; e-mail: email@example.com.
Purpose: In Ontario, health care spending has grown to 45% of total government expenditures. In a public health care system, changes in demographics and the emergence of innovative technologies challenge our ability to adapt to evolving patient needs. To maintain a high standard of clinical effectiveness, there is a need to identify opportunities to improve health care delivery. This study was structured to meet the following objectives: to understand the operating room (OR) inventory practices at a tertiary academic hospital, to mathematically model this process to ascertain service levels based on changes in inventory and demand, and to define the appropriate level of reusable inventory for open and laparoscopic colorectal surgery.
Methods: We retrospectively reviewed OR throughput for all cases of colorectal cancer from January 1, 2010, to January 31, 2011. The process flow of OR instrumentation was studied to understand delays in the provision of inventory. Combining total surgeries performed with surgeon-specific instrument preferences generated daily instrument demand. We fitted parametric demand distributions for two instrument sets for major colon resections. Markovian models were used to estimate the distribution of available inventory and the likelihood of insufficient instruments on any given day.
Results: We reviewed 1,458 cases, 39.5% of which involved major open surgery, whereas 26.2% involved laparoscopic surgery. Demand for open and laparoscopic instrument sets was observed to fit binomial (20, 0.15) and Poisson (1.41) distributions, respectively. On the basis of these curves, we estimated the probability distribution of the in-stock inventory and, subsequently, the probability that demand would exceed supply on any given day (Table 1). In particular, with 10 open and six laparoscopic sets currently owned by the institution, the probabilities that there would be insufficient inventory were 3.02% and 2.17%, respectively.
Conclusion: This analysis will guide purchasing decisions based on desired service levels and forecasted changes in demand. Furthermore, by ensuring that demand is being serviced, this analysis will help to curb loss of revenue, decrease wait times, and limit potential patient morbidity. Strategic purchasing can also reduce excessive inventory and therefore minimize shrinkage and obsolescence and increase working capital and institutional flexibility.
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