I-Corps: Translation potential of an Artificial Intelligence-based tool to provide feedback to medical students

The broader impact and commercial potential of this I-Corps project is the development of a software simulation platform that provides medical students and their educators with actionable, performance-based feedback on their readiness to practice medicine. While simulation experiences are ubiquitous in health professions education in the form of Objective Structured Clinical Evaluations (OSCEs), current information systems are not yet able to “close the loop” and provide students and their teachers with immediate feedback.

The proposed technology provides students participating in authentic, immersive clinical simulations with comprehensive, real-time, actionable feedback designed to help them improve their clinical competencies efficiently. The platform is designed to provide seamless data capture, analysis, visualization and individualized reporting, which may provide solutions to real challenges faced by medical schools, residency training programs, colleges of pharmacy, and physician associate training programs.

This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a simulation platform that is designed to prepare medical school students near graduation for transition to independent practice. Current Complex Objective Structured Clinical Evaluations (OSCEs) require significant resources, and data reporting may takes weeks, reducing the impact.

In this simulation, students demonstrate to 14 evaluators that they can perform 13 core professional activities (as defined by the American Association of Medical Colleges) unsupervised. The value of the simulation in preparing medical students for safe and effective clinical practice and providing medical schools and residency training programs with critical programmatic information has been demonstrated.

Strategies have been tested for data collection, analysis, benchmarking, interpretation, visualization and data reporting to guide performance improvement and remediation. Artificial intelligence is being incorporated to address the unreliable process of manually grading patient care notes ensuring that all students receive consistent and actionable feedback.

The proposed technology may reduce the effort involved in simulation implementation and performance data reporting.

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.