Quantifying the Metrics of Surgical Mastery: An Exploration in Data Science

量化手术熟练度的指标：数据科学的探索

• 批准号: 10457351
• 负责人: CARLA M PUGH
• 金额: $ 65.53万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10457351
• 关键词: Address; American College of Surgeons; Area; Artificial Intelligence; Benchmarking; Caring; Clinical; Cognitive; Computer Vision Systems; Credentialing; Data; Data Analyses; Data Collection; Data Science; Databases; Decision Making; Development; Education; Evaluation; Feedback; Feeds; Goals; Healthcare; Hernia; Hospitals; Learning; Machine Learning; Magnetism; Measurement; Medical; Motion; Operating Rooms; Operative Surgical Procedures; Patient Care; Performance; Physicians; Procedures; Process; Protocols documentation; Quality of Care; Research; Structure; Students; Surgeon; Surgical Specialties; Technical Expertise; Technology; Testing; Time; TimeLine; Translating; United States National Institutes of Health; Validation; Ventral Hernia; Video Recording; Work; base; clinical practice; cognitive skill; data framework; data modeling; data sharing; data standards; design; early experience; force sensor; implementation strategy; improved; motion sensor; multimodal data; multimodality; new technology; repaired; simulation; skill acquisition; skills; success; symposium; tool

PROJECT SUMMARY In surgery, it is accepted that there may be a ten to twenty-year learning curve to reach mastery for certain procedures. We believe this timeline can and should be shortened to improve patient care. Our short-term goal is to make a major contribution to the emerging field of Surgical Data Science by building a database of mastery level surgical performance and generating a roadmap for multimodal data collection and analysis procedures. Sharing our process, procedures and results broadly, will help to change measurement culture in healthcare. Through our newly developed partnership (October 2019) with the American College of Surgeons, we have already experienced early success in starting the conversation through the “Surgical Metrics Project” (https://www.facs.org/education/surgical-metrics). Using a standardized data collection platform (a mastery-level hernia simulation), we will deploy and synchronize multiple data capture approaches (motion tracking, video, audio and validated surgical performance checklists) to build our database. Data analysis will quantify surgical mastery and consist of new applications and discoveries in machine learning. Hypothesis: Using multiple, synchronized data capture approaches and machine learning, it is possible to create a database of mastery level surgical strategies that can be translated into a value-added, surgical navigation tool for surgeons. To test this hypothesis, we will empirically investigate the following paraphrased aims: SPECIFIC AIM 1: Quantify surgical mastery (cognitive and technical) during a simulated laparoscopic ventral hernia (LVH) repair by using a post-procedure analysis of multi-modal performance metrics captured from hospital credentialed surgeons (N~125). SPECIFIC AIM 2: Establish validity evidence for surgical mastery metrics by comparing simulation-based LVH performance with operating room LVH performance from the same surgeons (N~60). SPECIFIC AIM 3: Empirically investigate the best implementation strategy for utilization of a surgical navigation tool designed to deliver value-added information regarding mastery-level surgical performance strategies to a new group of hospital credentialed surgeons (N~125).

项目摘要 在外科手术中，人们普遍认为，要达到熟练程度，可能需要10到20年的学习曲线 程序.我们相信这个时间轴可以而且应该缩短，以改善病人的护理。我们的短期目标 是通过建立一个掌握手术数据的数据库， 水平的手术性能，并为多模式数据收集和分析程序生成路线图。 广泛分享我们的流程、程序和结果，将有助于改变医疗保健领域的测量文化。 通过我们与美国外科医生学院新发展的合作伙伴关系（2019年10月），我们 我已经通过“外科手术计划”在开始对话方面取得了初步成功， （https：//www.facs.org/education/qualitical-metrics）. 使用标准化的数据收集平台（大师级疝气模拟），我们将部署和同步 多种数据采集方法（运动跟踪、视频、音频和经验证的手术性能检查表） 来建立我们的数据库数据分析将量化手术掌握程度，包括新的应用程序和 机器学习的发现 假设：使用多种同步数据捕获方法和机器学习， 创建一个掌握水平的手术策略数据库，可以转化为增值的手术策略， 外科医生的导航工具。 为了检验这一假设，我们将对以下目标进行实证研究： 具体目标1：在模拟腹腔镜腹侧手术期间量化手术掌握（认知和技术） 疝（LVH）修补术，使用从以下数据采集的多模态性能指标的术后分析： 医院认证的外科医生（N~125）。 具体目标2：通过比较基于模拟的LVH，建立手术掌握指标的有效性证据 相同外科医生的手术室LVH性能（N~60）。 具体目标3：实证研究使用手术导航的最佳实施策略 该工具旨在向患者提供关于掌握级手术性能策略的增值信息， 新的一组医院认证的外科医生（N~125）。