Vedula S Swaroop, Ishii Masaru, Hager Gregory D
Malone Center for Engineering in Healthcare, Department of Computer Science, The Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland 21218; email:
Department of Otolaryngology-Head and Neck Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21287.
Annu Rev Biomed Eng. 2017 Jun 21;19:301-325. doi: 10.1146/annurev-bioeng-071516-044435. Epub 2017 Mar 27.
Training skillful and competent surgeons is critical to ensure high quality of care and to minimize disparities in access to effective care. Traditional models to train surgeons are being challenged by rapid advances in technology, an intensified patient-safety culture, and a need for value-driven health systems. Simultaneously, technological developments are enabling capture and analysis of large amounts of complex surgical data. These developments are motivating a "surgical data science" approach to objective computer-aided technical skill evaluation (OCASE-T) for scalable, accurate assessment; individualized feedback; and automated coaching. We define the problem space for OCASE-T and summarize 45 publications representing recent research in this domain. We find that most studies on OCASE-T are simulation based; very few are in the operating room. The algorithms and validation methodologies used for OCASE-T are highly varied; there is no uniform consensus. Future research should emphasize competency assessment in the operating room, validation against patient outcomes, and effectiveness for surgical training.
培养技术娴熟、能力胜任的外科医生对于确保高质量医疗服务以及最大限度减少有效医疗服务获取方面的差距至关重要。传统的外科医生培训模式正受到技术的快速进步、强化的患者安全文化以及对价值驱动型医疗系统的需求的挑战。与此同时,技术发展使得能够捕获和分析大量复杂的手术数据。这些发展推动了一种“手术数据科学”方法用于客观的计算机辅助技术技能评估(OCASE-T),以实现可扩展、准确的评估;个性化反馈;以及自动辅导。我们定义了OCASE-T的问题空间,并总结了代表该领域近期研究的45篇出版物。我们发现,大多数关于OCASE-T的研究基于模拟;在手术室进行研究的很少。用于OCASE-T的算法和验证方法差异很大;没有统一的共识。未来的研究应强调在手术室进行能力评估、针对患者结果进行验证以及对外科培训的有效性。