TY - JOUR
T1 - Capturing and Improving Case Charge Accuracy in Robotic Surgery Programs
AU - Gerull, William D.
AU - Pierce, Andrew
AU - Mody, Jessica
AU - Awad, Michael M.
AU - Martin, Jackie
AU - Wellen, Jason R.
N1 - Funding Information:
Dr Awad's institute receives educational grants for its simulation center from Applied Medical, Baxter, Bard, Ethicon, Intuitive Surgical, Medtronic, and Stryker.
Publisher Copyright:
© 2022 by the American College of Surgeons.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - The robotic platform offers many benefits to patients and surgeons; however, incorporating this new surgical tool has also introduced challenges in intraoperative documentation accuracy. In 2019, we began to investigate our institution's robotic intraoperative supply documentation accuracy. We identified a 60% case error rate between the robotic items logged by the operating room staff in the electronic medical record and the true robotic items used for a case as logged on the Intuitive platform. This can be a widespread and unrecognized problem for other organizations as well. We then addressed this problem through patient safety and quality improvement-based interventions including error notification to operating room personnel, a barcode scanning system, peer-to-peer education, improving robotic item descriptions, and procedure receipt messaging. These interventions helped us decrease our institution's case error rate from 60% to 16.9% during the past 2 years, which generated a cumulative 2.1% net increase in our billed robotic items, through the addition and/or subtraction of robotic items from each case. Through our multiple interventions, we have created a robust, flexible, and efficient item-capturing system for robotic surgery cases.
AB - The robotic platform offers many benefits to patients and surgeons; however, incorporating this new surgical tool has also introduced challenges in intraoperative documentation accuracy. In 2019, we began to investigate our institution's robotic intraoperative supply documentation accuracy. We identified a 60% case error rate between the robotic items logged by the operating room staff in the electronic medical record and the true robotic items used for a case as logged on the Intuitive platform. This can be a widespread and unrecognized problem for other organizations as well. We then addressed this problem through patient safety and quality improvement-based interventions including error notification to operating room personnel, a barcode scanning system, peer-to-peer education, improving robotic item descriptions, and procedure receipt messaging. These interventions helped us decrease our institution's case error rate from 60% to 16.9% during the past 2 years, which generated a cumulative 2.1% net increase in our billed robotic items, through the addition and/or subtraction of robotic items from each case. Through our multiple interventions, we have created a robust, flexible, and efficient item-capturing system for robotic surgery cases.
UR - http://www.scopus.com/inward/record.url?scp=85128264266&partnerID=8YFLogxK
U2 - 10.1097/XCS.0000000000000128
DO - 10.1097/XCS.0000000000000128
M3 - Article
C2 - 35426413
AN - SCOPUS:85128264266
SN - 1072-7515
VL - 234
SP - 964
EP - 968
JO - Journal of the American College of Surgeons
JF - Journal of the American College of Surgeons
IS - 5
ER -