Machine learning models in ambulatory anesthesia can accurately forecast case durations, PACU lengths of stay, and risk of hospital transfers based on preoperative patient and procedural factors.
Machine learning has the potential to optimize operational efficiency and personalize care in ambulatory anesthesia, though challenges like algorithmic opacity and data bias must be addressed.
PURPOSE OF REVIEW: This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care. RECENT FINDINGS: Machine learning models have demonstrated the ability to accurately forecast case durations, Post-Anesthesia Care Unit (PACU) lengths of stay, and risk of hospital transfers based on preoperative patient and procedural factors. These models can inform case scheduling, resource allocation, and preoperative evaluation. Additionally, machine learning can standardize assessments, predict outcomes, improve handoff communication, and enrich patient education. SUMMARY: Machine learning has the potential to revolutionize ambulatory anesthesia practice by optimizing efficiency, personalizing care, and improving quality and safety. However, limitations such as algorithmic opacity, data biases, reproducibility issues, and adoption barriers must be addressed through transparent, participatory design principles and ongoing validation to ensure responsible innovation and incremental adoption.
Anand et al. (Mon,) conducted a review in Ambulatory anesthesia. Machine learning was evaluated. Machine learning models in ambulatory anesthesia can accurately forecast case durations, PACU lengths of stay, and risk of hospital transfers based on preoperative patient and procedural factors.