FAST-framework for AI-based surgical transformation.

Abstract

Background: The use of machine learning (ML) in surgery till date has largely focused on predication of surgical variables, which has not been found to significantly improve operating room efficiencies and surgical success rates (SSR). Due to the long surgery wait times, limited health care resources and an increased population need, innovative ML models are needed. Thus, the Framework for AI-based Surgical Transformation (FAST) was created to make real time recommendations to improve OR efficiency.

Methods: The FAST model was developed and evaluated using a dataset of n=4796 orthopedic cases that utilizes surgery and team specific variables (e.g. specific team composition, OR turnover time, procedure duration), along with regular positive deviance seminars with the stakeholders for adherence and uptake. FAST was created using six ML algorithms, including decision trees and neural networks. The FAST was implemented in orthopedic surgeries at a hospital in Canada's capital (Ottawa).

Results: FAST was found to be feasible and implementable in the hospital orthopedic OR, with good team engagement due to the PD seminars. FAST led to a SSR of 93% over 23 weeks (57 arthroplasty surgery days) compared to 39% at baseline. Key variables impacting SSR included starting the first surgery on time, turnover time, and team composition.

Conclusions: FAST is a novel ML framework that can provide real time feedback for improving OR efficiency and SSR. Stakeholder integration is key in its success in uptake and adherence. This unique framework can be implemented in different hospitals and for diverse surgeries, offering a novel and innovative application of ML for improving OR efficiency without additional resources.