Abstract
Introduction: OR time is estimated to cost $100 per minute. Despite the significant expense of this valuable resource, best practice achieves only 70 % efficiency.
Compounding this problem is a lack of real-time actionable date. Most current OR utilization programs require labor intensive data entry by a member of the OR team and are subject to scrutiny. Automated systems require installation and maintenance of expensive tracking hardware throughout the institution. This study developed an inexpensive, automated OR utilization system, and analyzed data from multiple operating rooms
Methods and Procedures: OR activity was deconstructed into four room states. A sensor network was then iteratively developed to automatically and reliably capture these states resulting in simplifying the system down to four sensors, a local wireless network, and a data capture computer (SmartOR). Two systems were then installed into two clinical operating rooms, recordings captured 24/7, and data compared to that recorded in the current OR management systems. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time surgeon identified the patient preoperatively.
Results: From November 2014 to May 2015 data was collected from 388 cases.
Comparison to that in the current management system showed excellent correlation. The mean turnover time was 36 minutes. However, only 66 % of cases met the institutional goal of B30 minutes. Data analysis also identified outlier cases (times[ 2 SD from mean) in the domains of time from patient entry into the OR to intubation (10 % of cases) and time from extubation to patient exiting the OR (13 % of cases). In addition, time from surgeon identification of patient to scheduled procedure start time was 8 min 4 s (Institution bylaws require 30 minutes ahead of scheduled start time), yet OR teams required 17 min 56 s on average to bring a patient into the room after surgeon identification. These indisputable findings correlate with the OR manager report of 74 % late first-case starts due to ‘‘unavailability’’ of the surgeon.
Conclusions: The Smart OR automatically and reliably captures data on OR room state and, in real-time, identifies outlier cases that may be examined closer to improve efficiency. Because no manual entry is required the data is indisputable and allows OR teams to maintain a patient-centric focus.
Compounding this problem is a lack of real-time actionable date. Most current OR utilization programs require labor intensive data entry by a member of the OR team and are subject to scrutiny. Automated systems require installation and maintenance of expensive tracking hardware throughout the institution. This study developed an inexpensive, automated OR utilization system, and analyzed data from multiple operating rooms
Methods and Procedures: OR activity was deconstructed into four room states. A sensor network was then iteratively developed to automatically and reliably capture these states resulting in simplifying the system down to four sensors, a local wireless network, and a data capture computer (SmartOR). Two systems were then installed into two clinical operating rooms, recordings captured 24/7, and data compared to that recorded in the current OR management systems. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time surgeon identified the patient preoperatively.
Results: From November 2014 to May 2015 data was collected from 388 cases.
Comparison to that in the current management system showed excellent correlation. The mean turnover time was 36 minutes. However, only 66 % of cases met the institutional goal of B30 minutes. Data analysis also identified outlier cases (times[ 2 SD from mean) in the domains of time from patient entry into the OR to intubation (10 % of cases) and time from extubation to patient exiting the OR (13 % of cases). In addition, time from surgeon identification of patient to scheduled procedure start time was 8 min 4 s (Institution bylaws require 30 minutes ahead of scheduled start time), yet OR teams required 17 min 56 s on average to bring a patient into the room after surgeon identification. These indisputable findings correlate with the OR manager report of 74 % late first-case starts due to ‘‘unavailability’’ of the surgeon.
Conclusions: The Smart OR automatically and reliably captures data on OR room state and, in real-time, identifies outlier cases that may be examined closer to improve efficiency. Because no manual entry is required the data is indisputable and allows OR teams to maintain a patient-centric focus.
| Original language | English (US) |
|---|---|
| Title of host publication | Surgical Endoscopy |
| Subtitle of host publication | And Other Interventional Techniques Official Journal of the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) and European Association for Endoscopic Surgery (EAES) |
| Publisher | Springer US |
| Pages | S257 |
| Number of pages | 1 |
| Volume | 30 |
| Edition | Supplement 1 |
| DOIs | |
| State | Accepted/In press - Mar 1 2016 |
| Event | Society of American Gastrointestinal and Endoscopic Surgeons (SAGES), Boston, Massachusetts, USA, 16–19 March 2016 : 2016 Scientific Session - Boston, MA, United States Duration: Mar 16 2016 → Mar 19 2016 |
Conference
| Conference | Society of American Gastrointestinal and Endoscopic Surgeons (SAGES), Boston, Massachusetts, USA, 16–19 March 2016 |
|---|---|
| Abbreviated title | SAGES |
| Country/Territory | United States |
| City | Boston, MA |
| Period | 3/16/16 → 3/19/16 |