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Real-time non-intrusive workload monitoring-Integration of human factors in surgery training and assessment

实时非侵入式工作量监测——将人为因素融入手术培训和评估

基本信息

批准号：
9983030
负责人：
Denny Yu
金额：
$ 18.31万
依托单位：
PURDUE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9983030
关键词：
Accreditation Adverse event Algorithmic Software Algorithms Assessment tool Attention Awareness Caring Cognitive Complement Complex Computer software Coupled Environment Equipment Event Feedback Future Health Human Impairment Improve Access Intervention Joints Knowledge Learning Literature Machine Learning Measures Medical Mentors Methods Modality Modeling Monitor Operating Rooms Operative Surgical Procedures Outcome Participant Patient-Focused Outcomes Patients Pattern Performance Physiological Plant Roots Postoperative Period Procedures Psyche structure Public Health Robotics Sentinel Supervision Surgeon Surveys System Task Performances Techniques Technology Technology Assessment Telerobotics Testing Time Training Translating Work Workload base care delivery cognitive load design distraction experience improved individualized feedback innovation motion sensor new technology novel operation patient safety programs recruit robotic training sensor sensor technology simulation simulation environment skills skills training tool vigilance virtual surgery

项目摘要

Project Summary/Abstract (30 lines) High physiological and cognitive workload required in de-coupled surgical work demands may have significant impact on patient outcome, surgical efficacy, and surgical performance. As novel surgical techniques, e.g., telesurgery, are developed, surgical operations will become more complex and the mental and physical demand on surgeons will likely increase, making it critical to develop remote and connected workload monitoring methods for the safe and effective surgical procedure design, testing, and training. This work will implement novel technology and machine learning analytics to quantify real-time and remote workload and test how workload feedback can impact care delivery in both in telesurgery and surgical simulation environments. Our overall hypothesis is that connected sensing technology in telesurgical procedures and simulation can improve surgical training and understanding of the impact of their workload on performance; ultimately improving patient health, surgery efficacy, and patient access (e.g., tele-mentoring) to surgical care. Two specific aims are proposed to investigate this hypothesis. The objective of Specific Aim 1 is to develop a connected sensor system to objectively quantify workload real-time in simulated telerobotic procedures. This involves: 1) integrating non-intrusive sensors into a single system within the simulation trainer or environment, 2) training machine learning techniques to objectively distinguish workload using a simulated surgical skills tasks, and 3) validating metrics across varying levels of cognitive loads under various task difficulty with medical trainees and expert participants. The objective of Specific Aim 2 is to determine the impact of the real-time workload feedback intervention on trainee performance times, errors, and intraoperative workload. Two tasks are proposed: 1) Explore modalities preferred by surgeons for providing real-time feedback on workload and 2) Assess impact of workload feedback on task performance and learning. Our primary hypothesis is that performance times and errors will improve when participants are provided realtime feedback on workload compared to performance with no feedback. The expected deliverables include 1) workload monitoring technology, algorithms, and software for complementing current simulation-based training, 2) objective and automated workload metrics, 3) real-time assistive intervention tool, and 4) preliminary evidence on impact of workload monitoring on training. The technology in this proposed work will improve public health by reducing adverse events due to human factors in surgery and improve access to surgical care with intervention technology that can adaptively train surgeons and remotely assess proficiency.

项目概要/摘要（30行）解耦手术工作要求中所需的高生理和认知工作负荷可能具有对患者结局、手术疗效和手术性能有显著影响。作为新型外科手术可以使用各种技术，随着外科手术的发展，外科手术将变得更加复杂，对外科医生的身体需求可能会增加，这使得开发远程和联网技术变得至关重要。安全有效的外科手术设计、测试和培训的工作负荷监测方法。这工作将实施新技术和机器学习分析来量化实时和远程工作量和测试工作量反馈如何影响外科和外科的护理提供仿真环境。我们的总体假设是，外科手术中的连接传感技术程序和模拟可以提高手术培训和理解他们的工作量的影响，性能;最终改善患者健康、手术疗效和患者进入（例如，远程指导），外科护理。两个具体的目标，提出了调查这一假设。具体目标1的目标是开发一个连接的传感器系统，以客观地量化工作量实时模拟远程机器人程序。这涉及：1）将非侵入式传感器集成到模拟训练器或环境内的单个系统，2）训练机器学习技术，使用模拟的手术技能任务客观地区分工作负荷，以及3）验证不同的不同任务难度下的认知负荷水平。具体目标2的目标是确定实时工作负载反馈的影响对受训者的操作时间、错误和术中工作量进行干预。提出了两项任务：1）探索外科医生首选的模式，以提供关于工作负荷的实时反馈; 2）评估影响对任务表现和学习的反馈。我们的主要假设是，当向参与者提供关于工作量与绩效的实时反馈时，错误将得到改善没有反馈预期的交付成果包括：1）工作负载监控技术、算法和软件，补充当前基于模拟的培训，2）客观和自动化的工作量指标，3）实时辅助干预工具; 4）工作量监测对培训影响的初步证据。的这项拟议工作中的技术将通过减少人为因素引起的不良事件来改善公共卫生在外科手术中，并通过可以自适应地培训外科医生的介入技术来改善获得外科护理的机会远程评估熟练程度