Abstract
Objective
Operating room (OR) delays are costly and frequently related to modifiable process factors. At our institution, inaccurate preference card assignment and incomplete surgical instrument inventory data were identified as contributors to instrument unavailability and delayed case starts. This quality improvement initiative aimed to reduce common-cause variability related to preference card accuracy and instrument tray availability.Methods
A cross-disciplinary quality improvement project was conducted using the Plan-Do-Study-Act (PDSA) framework. An apparent cause analysis, including Gemba walks and Ishikawa diagrams, was performed to identify contributors to limited tray availability. Scope inventory audits were performed for rigid endoscopes and laparoscopes used by pediatric surgery, neurosurgery, and otorhinolaryngology. Preference cards were systematically reviewed for the audited procedures to evaluate alignment between assigned case cards and surgeon-specific requirements. A sample of scheduled cases within pediatric surgery, neurosurgery, and otorhinolaryngology was examined. For each case, the assigned base or surgeon-specific preference card was compared to the instruments actually requested intraoperatively and documented in the Sterile Processing Microsystem (SPM). Product Usage Reports (PURs) from the sterile processing tracking system (SPM) were compared with physical inventory counts. Additional inventory was acquired when maximum utilization exceeded 100%.Results
Inventory audits revealed discrepancies between reported and actual available inventory, resulting in underestimated utilization rates. Following inventory correction and targeted inventory expansion, maximum utilization decreased for selected instruments, including pediatric laparoscopes (133% to 80%), ENT sinus instrument and scope trays (200% to 133%), and Clip Appliers Long & Short Tray (200% to 100%). These changes reduced same-day immediate turnover events for the audited trays.Conclusions
Accurate preference card assignment combined with reliable inventory data enables identification of high-utilization instruments and supports data-driven decisions regarding inventory expansion. While findings are limited to a small number of instruments at a single institution and measured at discrete time points, this approach demonstrates a practical framework that may support improved OR workflow and resource availability.