A patient arrives with a persistent, rattling cough. The novice clinician runs through a series of hypothesis-driven questions and conducts their physical exam until they identify the cause and decide on the best treatment plan. All the while, the ambient listening capabilities of artificial intelligence (AI) record the encounter and analyze their clinical reasoning skills.
That's the vision of CRISP (Clinical Reasoning Insights for Shaping Performance), a new Penn Medicine initiative that will use AI-enabled systems to deliver robust, data-driven feedback to medical students and postgraduate trainees, accelerating their growth as expert clinicians. The project is led by Perelman School of Medicine faculty Jessica Dine, MD, MSHP; Janae Heath, MD, MSCE; Jennifer Kogan, MD; and Ilene Rosen, MD, MSCE, with support from the Department of Informatics, Biostatistics and Epidemiology.
"Clinical reasoning is a pivotal piece of excellent patient care," said Heath, associate program director for the Internal Medicine Residency Program and assistant professor of Pulmonary, Allergy and Critical Care. "And so an improvement in those skills, resulting in higher levels of expertise, is going to directly benefit our patients."
CRISP was one of 11 innovative projects recently selected for a four-year, $1.1 million grant through the American Medical Association's Transforming Lifelong Learning Through Precision Education Grant Program. Precision education aims to tailor training to meet each learner's needs instead of the traditional "one size fits all" approach to medical education. It's also part of Penn Medicine's goal to individualize learning across the medical education continuum.
"It's incredibly exciting because not only is this the first true precision education at Penn, but I think it's a really unique way to do precision education," said Dine, associate dean of Assessment, Evaluation and Medical Education Research and a professor of Pulmonary, Allergy and Critical Care.
This new initiative in medical education and training is one of many ways that Penn Medicine is harnessing AI to improve the way that clinicians work and ultimately strengthen patient care, from a new tool that can quickly synthesize a patient's medical history to an AI-enabled system that assists with replies to patient messages to an AI "scribe" that helps with note-taking during patient visits.
Working together to strengthen clinical reasoning
While clinical reasoning is at the heart of effective, high-quality medicine, it can be tricky to teach—it's shaped by chance patient encounters and subjective assessments. Faculty can't observe every patient interaction in real time, so they often rely on students' and trainees' presentation of a case to retroactively assess their clinical reasoning skills.
"Clinical reasoning is a really hard skill to capture well," said Heath, drawing on her work as part of the project team. "But over the last two years, there's been an explosion of new technologies available, including ones that Penn Medicine has embraced, that have created this new opportunity to capture clinical reasoning in different domains."
The new initiative will include undergraduate and graduate medical education learners in four specialties: Internal Medicine, Emergency Medicine, Surgery and Radiology. The goal is to create a scalable, precision education tool that supports individualized coaching and competency-based progression across medical specialties.
Penn Medicine is an ideal environment to explore novel assessment models because of its educational structure—which is integrated under shared leadership with the health system's clinical care—and a culture where people regularly collaborate across medical specialties and academic disciplines, project leaders said.
"Penn Medicine is thinking about education across the continuum and through a really innovative lens," Heath said. "We have team members across the institution, including the informatics team, who are pivotal to this project. That's allowing us to tackle this issue, thinking about it differently than anyone's thought about it before."
