A Data-Driven Approach to Optimize Surgical Instrument Trays
Joshua J. Barrett et al.
What the paper says
Surgical instrument costs, including sterilization, tray assembly, and repurchase, contribute heavily to hospital operating expenses. Over time, trays often accumulate infrequently used instruments, increasing costs. We present a data-driven optimization model for surgical tray design, developed using point-of-use surgical instrument data collected in partnership with OpFlow, a healthcare technology firm. This model streamlines trays and suggests specialized tray composition, improving instrument utilization and delivering significant cost savings. We validated the model through out-of-sample testing and expert review, demonstrating its superiority over both current practices and expert-led reconfiguration. Implementation at UNC Rex Hospital achieved a projected $1.39 million in annual savings for a representative set of trays. Our analysis proves the value of point-of-use data and scalable algorithms for surgical tray optimization. The successful collaboration between academia and industry in this work highlights a path toward further data-driven innovations in healthcare operations. This work summarizes the deployment of a data-driven approach to surgical tray optimization; further background and technical details can be found in the following journal articles: Deshpande et al. (2023), Knowles et al. (2021), and Wood et al. (2021).
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.