An AI Coach for Enhancing Teamwork in the Cardiac Operating Room
Overview
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.
Funding
NSF Award Number: 2310187
Collaborators
- Prof. Roger D. Dias, PhD, MBA, MD
- Prof. Eduardo Salas, PhD
- Prof. Julie Shah, PhD
- Prof. Vaibhav Unhelkar, PhD
Publications & Presentations
- Mishra, S., Dias, R. D., Zenati, M. A., & Chaspari, T. (2024). Acoustic Patterns of Interprofessional Communication and Quality of Teamwork in the Cardiac Operating Theatre. The Hamlyn Symposium on Medical Robotics.
- Harari, R., Dias, R. D., Salas, E., Unhelkar, V., Chaspari, T., & Zenati, M. (2024). Misalignment of Cognitive Processes within Cardiac Surgery Teams. The Hamlyn Symposium on Medical Robotics.
- Dias, R. D., Harari, R. E., Zenati, M. A., Rance, G., Srey, R., Chen, L., & Gombolay, M. (2024). A Clinician-Centered Explainable Artificial Intelligence Framework for Decision Support in the Operating Theatre. The Hamlyn Symposium on Medical Robotics.
- Awtry, J., Vaidyanathan, S., Conboy, H. M., Kennedy-Metz, L., Clarke, L. A., Avrunin, G., Jensen, D., & Zenati, M. (2024). Accuracy of Machine Learning Models Predicting Intraoperative Cognitive Workload for AI-based Cognitive Guidance. The Hamlyn Symposium on Medical Robotics.
- Harari, R. E., Dias, R. D., Kennedy-Metz, L. R., Varni, G., Gombolay, M., Yule, S., Salas, E., & Zenati, M. A. (2024). Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills. JAMA Network Open.
- Harari, R., Kennedy-Metz, L., Varni, G., Unhelkar, V., Salas, E., Dias, R. D., & Zenati, M. A. (2024). A Novel Multimodal Perspective on Objective Assessment of Non-Technical Skills in Cardiac Surgery. Academic Surgical Congress Abstracts Archive.
- Harari, R., Zenati M. A., Unhelkar, V., Yule, S. J., & Dias, R. D. (2024). Using Deep Learning to Assess Teamwork During Cardiac Surgery. Clinical Translation of Medical Image Computing and Computer Assisted Interventions.
- Khalid, M., Seo, S., Zenati, M. A., Ebnali, M., Kennedy-Metz, L. R., Dias, R. D., Unhelkar, V. V., & Salas, E. (2023). Opportunities and Challenges of Real-Time Measurement of Team Performance on the Cardiac Operating Room. International Annual Meeting of the Human Factors and Ergonomics Society.
- Seo, S., Kennedy-Metz, L. R., Zenati, M. A., Shah, J. A., Dias, R. D., & Unhelkar, V. V. (2021). Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare. IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA).