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).
