Collaborative Research: SCH: An AI Coach to Enhance Surgical Teamwork in the Cardiac Operating Room

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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.