Pacu公司
患者安全
回廊的
质量(理念)
计算机科学
医学
医疗保健
麻醉
外科
哲学
认识论
经济
经济增长
作者
Karisa Anand,Suk Ki Hong,Kapil Anand,Joseph M. Hendrix
出处
期刊:Current Opinion in Anesthesiology
[Ovid Technologies (Wolters Kluwer)]
日期:2024-07-09
标识
DOI:10.1097/aco.0000000000001410
摘要
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.
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