Albert Y Huang1,2,3, Guillaume Joerger4,5,6, Vid Fikfak4,5,7, Remi Salmon4,5, Brian J Dunkin4,5,7, Barbara L Bass4,5, Marc Garbey4,5,7,6. 1. University of Houston, Houston, TX, USA. AYHuang@houstonmethodist.org. 2. Department of Surgery, Houston Methodist Hospital, 6550 Fannin St Suite 1661, Houston, TX, 77030, USA. AYHuang@houstonmethodist.org. 3. LASIE UMR CNRS, University of La Rochelle, La Rochelle, France. AYHuang@houstonmethodist.org. 4. University of Houston, Houston, TX, USA. 5. Department of Surgery, Houston Methodist Hospital, 6550 Fannin St Suite 1661, Houston, TX, 77030, USA. 6. LASIE UMR CNRS, University of La Rochelle, La Rochelle, France. 7. Methodist Institute of Technology Innovation and Education, Houston, TX, USA.
Abstract
BACKGROUND: Despite the significant expense of OR time, best practice achieves only 70% efficiency. Compounding this problem is a lack of real-time data. Most current OR utilization programs require manual data entry. 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. STUDY DESIGN: OR activity was deconstructed into four room states. A sensor network was then developed to automatically capture these states using only three sensors, a local wireless network, and a data capture computer. Two systems were then installed into two ORs, recordings captured 24/7. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time of preoperative patient identification by the surgeon. RESULTS: From November 2014 to December 2015, data on 1003 cases were collected. The mean turnover time was 36 min, and 38% of cases met the institutional goal of ≤30 min. Data analysis also identified outlier cases (>1 SD from mean) in the domains of time from patient entry into the OR to intubation (11% of cases) and time from extubation to patient exiting the OR (11% of cases). Time from surgeon identification of patient to scheduled procedure start time was 11 min (institution bylaws require 20 min before scheduled start time), yet OR teams required 22 min on average to bring a patient into the room after surgeon identification. CONCLUSION: The SmartOR automatically and reliably captures data on OR room state and, in real time, identifies outlier cases that may be examined closer to improve efficiency. As no manual entry is required, the data are indisputable and allow OR teams to maintain a patient-centric focus.
BACKGROUND: Despite the significant expense of OR time, best practice achieves only 70% efficiency. Compounding this problem is a lack of real-time data. Most current OR utilization programs require manual data entry. 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. STUDY DESIGN: OR activity was deconstructed into four room states. A sensor network was then developed to automatically capture these states using only three sensors, a local wireless network, and a data capture computer. Two systems were then installed into two ORs, recordings captured 24/7. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time of preoperative patient identification by the surgeon. RESULTS: From November 2014 to December 2015, data on 1003 cases were collected. The mean turnover time was 36 min, and 38% of cases met the institutional goal of ≤30 min. Data analysis also identified outlier cases (>1 SD from mean) in the domains of time from patient entry into the OR to intubation (11% of cases) and time from extubation to patient exiting the OR (11% of cases). Time from surgeon identification of patient to scheduled procedure start time was 11 min (institution bylaws require 20 min before scheduled start time), yet OR teams required 22 min on average to bring a patient into the room after surgeon identification. CONCLUSION: The SmartOR automatically and reliably captures data on OR room state and, in real time, identifies outlier cases that may be examined closer to improve efficiency. As no manual entry is required, the data are indisputable and allow OR teams to maintain a patient-centric focus.
Authors: Yan Xiao; Peter Hu; Hao Hu; Danny Ho; Franklin Dexter; Colin F Mackenzie; F Jacob Seagull; Richard P Dutton Journal: Anesth Analg Date: 2005-09 Impact factor: 5.108
Authors: Albert Y Huang; Guillaume Joerger; Remi Salmon; Brian Dunkin; Vadim Sherman; Barbara L Bass; Marc Garbey Journal: Surg Endosc Date: 2015-10-30 Impact factor: 4.584
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