From November 2014 to December 2015, intraoperative data were collected on 1003 cases. Of these, 504 were laparoscopic, and the remainder a combination of general surgery, vascular, plastics, neurosurgery, and colorectal procedures. The database we built gives us access to a very broad range of types of procedures from a range of surgical specialties. The results obtained confirm the capability of accurately identifying the steps of the operative procedure in a repeatable and reliable way. When focus is turned to the non-procedural portions of the OR cycle such as the time between cases (turnover time), the system demonstrates how easily and precisely it can automatically detect, quantify, and generate meaningful data to be used by the team to improve efficiency.
Here is an example for turnover time detection: Over 600 segments of time between cases were detected with the system, and out of these, 374 turnover times were identified. Turnover time was defined as the time between two cases that were scheduled to immediately follow one another, meaning the time between a patient leaves the OR and the time the next one enters. Any time between 2 surgical cases that exceeded 60 min was deemed not to be a true turnover time and excluded from the analysis, as there would have been many potential alternative reasons for the extended time between those cases (purposeful scheduling, cancelations, room changes, etc.). Our clinical trial institution's goal is a 30-min turnover time, and we determine the mean to be 36 min with 72% of cases exceeding the 30-min threshold.