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

通常需要心脏手术来解决一些最严重的心脏问题，每年需要进行90多万次心脏手术。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、输液师和护士--合作管理这一生命关键护理。为了成功地实施护理，手术团队的所有成员都应该步调一致地执行任务，并充分了解手术过程中遇到的动态情况。然而，在心脏手术的复杂环境中，实现如此理想的团队合作是困难的，在这种环境中，人类的表现会受到高工作量、疲劳以及手术期间中断或中断等因素的不利影响。该项目解决了减少这些可预防的人为错误并通过设计人工智能(AI)启用的指导系统(AI Coach)来监控、评估和加强心脏手术中的手术团队合作的迫切需要。AI Coach的核心功能将是一套新颖的机器学习和可解释的人工智能算法，以计算方式生成可解释的反馈和干预，以基于多模式传感器数据加强手术团队合作。该项目将在智能健康的多学科研究领域培训学生。该项目将通过将研究成果纳入计划中的博物馆人类与人工智能协作展览，增加公众对人工智能的参与。该项目的总体目标是设计AI教练系统，该系统由多模式传感硬件、数据驱动的算法和用户界面组成，以加强心脏手术中的手术团队合作。AI Coach将通过两个并行的策略来实现其目标：(I)解决外科手术团队合作的建模问题；(Ii)通过计算生成反馈以改进这种团队合作。该项目团队将首先开发一个新颖的团队马尔科夫模型(TMKM)，以反映手术团队的心理模型。然后，系统的计算核心将通过以下开发实现：(A)基于新颖的多智能体模拟学习方法的机器学习算法，以得出明确依赖于潜在表现塑造因素的团队合作的预测模型，例如心理模型，以及(Ii)可解释的人工智能技术，以计算产生可解释的反馈和干预，以加强团队合作。由于外科团队合作收集大数据集的挑战，算法开发将强调样本和标签高效技术。项目团队将采用迭代的、以用户为中心的设计方法，对集成系统的可用性进行原型和测试。这些解决方案将使用多模式专家注释的外科团队合作数据进行开发和评估，并在最先进的或模拟设备中建立原型。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.