Skip to main content

ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Health Informatics
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1455477
This article is part of the Research Topic Artificial Intelligence for Smart Health: Learning, Simulation, and Optimization View all 8 articles

Quantifying the Impact of Surgical Teams on Each Stage of the Operating Room Process

Provisionally accepted
Adam Meyers Adam Meyers 1*Mertcan Daysalilar Mertcan Daysalilar 1Arman Dagal Arman Dagal 2,3Michael Wang Michael Wang 3Onur Kutlu Onur Kutlu 4Mehmet Akcin Mehmet Akcin 1,4
  • 1 Department of Industrial and Systems Engineering, College of Engineering, University of Miami, Coral Gables, Florida, United States
  • 2 Department of Anesthesiology, Perioperative Medicine and Pain Management, Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, United States
  • 3 Department of Neurological Surgery, Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, United States
  • 4 DeWitt Daughtry Family Department of Surgery, Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, United States

The final, formatted version of the article will be published soon.

    Introduction: Operating room (OR) efficiency is a key factor in determining surgical healthcare costs. To enable targeted changes for improving OR efficiency, a comprehensive quantification of the underlying sources of variability contributing to OR efficiency is needed. Previous literature has focused on select stages of the OR process or on aggregate process times influencing efficiency. This study proposes to analyze the OR process in more fine-grained stages to better localize and quantify the impact of important factors. Methods: Data spanning from 2019-2023 were obtained from a surgery center at a large academic hospital. Linear mixed models were developed to quantify the sources of variability in the OR process. The primary factors analyzed in this study included the primary surgeon, responsible anesthesia provider, primary circulating nurse, and procedure type. The OR process was segmented into eight stages that quantify eight process times, e.g., procedure duration and procedure start time delay. Model selection was performed to identify the key factors in each stage and to quantify variability. Results: Procedure type accounted for the most variability in three process times and for 44.2\% and 45.5\% of variability, respectively, in procedure duration and OR time (defined as the total time the patient spent in the OR). Primary surgeon, however, accounted for the most variability in five of the eight process times and accounted for as much as 21.1\% of variability. The primary circulating nurse was also found to be significant for all eight process times. Discussion: The key findings of this study include the following. (1) It is crucial to segment the OR process into smaller, more homogeneous stages to more accurately assess the underlying sources of variability. (2) Variability in the aggregate quantity of OR time appears to mostly reflect the variability in procedure duration, which is a subinterval of OR time. (3) Primary surgeon has a larger effect on OR efficiency than previously reported in the literature and is an important factor throughout the entire OR process. (4) Primary circulating nurse is significant for all stages of the OR process, albeit their effect is small.

    Keywords: Operating room, Surgery, Efficiency, Case delay, duration, Surgical team, human factors, linear mixed model

    Received: 27 Jun 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Meyers, Daysalilar, Dagal, Wang, Kutlu and Akcin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Adam Meyers, Department of Industrial and Systems Engineering, College of Engineering, University of Miami, Coral Gables, FL 33146, Florida, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.