手术室管理
围手术期
计算机科学
限制
人工智能
健康信息学
领域(数学)
调度(生产过程)
机器学习
运营管理
运筹学
医学
护理部
外科
工程类
公共卫生
机械工程
数学
纯数学
作者
Valentina Bellini,Marco Guzzon,Barbara Bigliardi,Monica Mordonini,Serena Filippelli,Elena Bignami
标识
DOI:10.1007/s10916-019-1512-1
摘要
We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus on the administrative and managerial Operating Room (OR) perspective. Studies conducted on adult (≥ 18 years) patients between 2015 and February 2019 were deemed eligible. A total of 19 papers were included. Our review suggests that the use of Machine Learning (ML) in the field of OR organization has many potentials. Predictions of the surgical case duration were obtain with a good performance; their use could therefore allow a more precise scheduling, limiting waste of resources. ML is able to support even more complex models, which can coordinate multiple spaces simultaneously, as in the case of the post-anesthesia care unit and operating rooms. Types of Artificial Intelligence could also be used to limit another organizational problem, which has important economic repercussions: cancellation. Random Forest has proven effective in identifing surgeries with high risks of cancellation, allowing to plan preventive measures to reduce the cancellation rate accordingly. In conclusion, although data in literature are still limited, we believe that ML has great potential in the field of OR organization; however, further studies are needed to assess the effective role of these new technologies in the perioperative medicine.
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