Collaborative Research: SCH: An AI Coach for Enhancing Teamwork in the Cardiac Operating Room

合作研究：SCH：增强心脏手术室团队合作的人工智能教练

• 批准号: 2204914
• 负责人: Julie Shah
• 金额: $ 24.93万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204914&HistoricalAwards=false
• 关键词: Collaborative; Research; SCH; AI; Coach

Cardiac surgery is often needed to address some of the most serious heart problems, resulting in administration of more than 900,000 cardiac procedures each year. The cardiac Operating Room (OR) is a complex environment where healthcare professionals from multiple disciplines -- including surgeons, anesthesiologists, perfusionists, and nurses -- collaborate to administer this life-critical care. To successfully administer care, all members of the surgical team are expected to perform their tasks in lockstep and with full awareness of dynamic situations encountered during surgery. However, achieving such ideal teamwork is difficult in the complex environment of cardiac OR, where human performance is adversely affected by factors such as high workload, fatigue, and interruptions or disruptions during surgery. This project addresses an urgent need for mitigating these preventable human errors and improving patient safety through the design of an Artificial Intelligence (AI)-enabled coaching system (AI Coach) for monitoring, assessing, and enhancing surgical teamwork in the cardiac OR. Central to the functioning of the AI Coach will be a set of novel machine learning and explainable artificial intelligence algorithms to computationally generate interpretable feedback and interventions for enhancing surgical teamwork based on multimodal sensor data. The project will train students in the multi-disciplinary research area of Smart Health. The project will increase public engagement with AI, by incorporating the research results into a planned museum exhibit on human-AI collaboration. The project’s overarching goal is to design the AI Coach system comprised of multimodal sensing hardware, data-driven algorithms, and a user interface to enhance surgical teamwork in the cardiac OR. AI Coach will achieve its objectives by pursuing two parallel strategies: (i) addressing the problem of modeling surgical teamwork; (ii) computationally generating feedback to improve this teamwork. The project team will first develop a novel Team Markov Model (TMkM) that reflects the surgical team’s mental model. Then, the computational core of the system will be realized through the development of (a) machine learning algorithms based on novel multi-agent imitation learning methods to arrive at predictive models of teamwork that explicitly depend on latent performance-shaping factors, such as mental models, and (ii) explainable AI techniques to computationally generate interpretable feedback and interventions for enhancing teamwork. Due to the challenge of collecting large data sets of surgical teamwork, the algorithm development will emphasize sample- and label-efficient techniques. The project team will prototype and test usability of the integrated system by employing iterative, user-centered design approaches. The solutions will be developed and evaluated using multi-modal expert-annotated data of surgical teamwork and prototyped in a state-of-the-art OR simulation facility.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通常需要进行心脏手术来解决一些最严重的心脏问题，每年需要进行超过 900,000 例心脏手术。心脏手术室 (OR) 是一个复杂的环境，来自多个学科的医疗保健专业人员（包括外科医生、麻醉师）在此进行手术。 、灌注师和护士——合作实施这种生命攸关的护理，手术团队的所有成员都应该同步执行任务，并充分了解手术过程中遇到的动态情况。理想的在心脏手术室的复杂环境中，团队合作非常困难，手术期间的高工作量、疲劳以及中断或干扰等因素会对人的表现产生不利影响。该项目迫切需要通过减少这些可预防的人为错误并提高患者安全。设计一个支持人工智能 (AI) 的教练系统（AI Coach），用于监控、评估和增强心脏手术中的手术团队合作。AI Coach 功能的核心将是一套新颖的机器学习和技术。可解释的人工智能算法该项目将根据多模式传感器数据生成可解释的反馈和干预措施，以增强手术团队合作。该项目将通过将研究结果纳入计划中，增加公众对人工智能的参与。该项目的总体目标是设计由多模态传感硬件、数据驱动和用户界面组成的 AI Coach 系统，以增强心脏手术中的手术团队合作，从而实现其目标。通过追求两种算法并行策略：（i）寻址手术团队合作建模的问题；（ii）通过计算生成反馈以改进团队合作。项目团队将首先开发反映手术团队心理模型的新颖的团队马尔可夫模型（TMkM）。通过开发（a）基于新颖的多智能体模仿学习方法的机器学习算法来实现团队合作的预测模型，该模型明确依赖于潜在的绩效塑造因素，例如心理模型，以及（ii）可解释的人工智能技术通过计算生成可解释的由于收集外科团队合作的大型数据集的挑战，算法开发将强调样本和标签高效的技术，项目团队将通过采用迭代、用户来原型化和测试集成系统的可用性。该解决方案将使用多模式专家注释的手术团队数据进行开发和评估，并在最先进的手术室模拟设施中进行原型设计。该奖项反映了 NSF 的法定使命，并被认为是值得的。通过评估提供支持利用基金会的智力优势和更广泛的影响审查标准。