The Role of Machine Learning in Management of Operating Room: A Systematic Review

Abstract

Machine learning (ML) is a developing technology that enables the analysis and interpretation of large amounts of data. The purpose of this systematic review was to summarize the available literature on the role of ML in operating room (OR) management. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to search the literature based on pre-defined inclusion and exclusion criteria. A total of 608 studies were found across five different databases (PubMed, EMBASE, Scopus, Web of Science, and IEEE Xplore), of which 21 studies were included in this review after removing duplicates and excluding studies based on the pre-defined inclusion and exclusion criteria. The review highlights how ML has a major impact on surgical case cancellation detection, post-anesthesia unit resource allocation optimization, and surgical case length prediction. Neural networks, XGBoost, and random forests are a few examples of ML algorithms that have shown promise in increasing prediction accuracy and resource efficiency. Nonetheless, issues including privacy concerns and data access remain challenges. The study emphasizes how ML is advancing in surgical medicine and how further innovation is required to fully realize AI's transformative potential for patients, healthcare professionals, and practitioners. Ultimately, integrating AI into OR management holds the potential for improving patient outcomes and healthcare productivity.

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