Alexander Joseph Podboy, MD, Academic Fellow, Doctor, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, United States. alexander.podboy@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Alexander Joseph Podboy, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA 94305, United States
David Scheinker, Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA 94305, United States
David Scheinker, Department of Preoperative Services, Lucile Packard Children's Hospital Stanford, Stanford, CA 94304, United States
Author contributions: All authors equally contributed to this paper, in regards to conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.
Institutional review board statement: This research was approved by the institutional review board at Stanford University.
Informed consent statement: The informed consent was waived.
Conflict-of-interest statement: Scheinker D serves as an advisor to Carta Healthcare - a healthcare analytics company. No other potential conflicts of interest or financial support to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Alexander Joseph Podboy, MD, Academic Fellow, Doctor, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, United States. alexander.podboy@gmail.com
Received: April 23, 2020 Peer-review started: April 23, 2020 First decision: June 4, 2020 Revised: June 15, 2020 Accepted: June 17, 2020 Article in press: June 17, 2020 Published online: July 28, 2020 Processing time: 94 Days and 14.3 Hours
Abstract
BACKGROUND
The use of machine learning (ML) to predict colonoscopy procedure duration has not been examined.
AIM
To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.
METHODS
Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected. ML models predicting procedure duration were developed using data available at time of scheduling. The top performing model was compared against historical practice. Models were evaluated based on accuracy (prediction – actual time) ± 5, 10, and 15 min.
RESULTS
ML outperformed historical practice with 77.1% to 68.9%, 87.3% to 79.6%, and 92.1% to 86.8% accuracy at 5, 10 and 15 min thresholds.
CONCLUSION
The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.
Core tip: Machine learning has been utilized to predict surgical procedure duration and enhance operating room proficiency, however its usefulness for predicting colonoscopy procedure duration has not been examined. Procedure duration predictions from a machine learning algorithm trained on data from the Clinical Outcomes Research Initiative database outperformed historical practice.
