ChangeMedEd Initiative

What if CME could be tailored to the patient you’re seeing next?

Timothy M. Smith , Contributing News Writer

There’s a nagging reality about CME: While it’s essential for physicians to maintain their skills, many in practice have little time to do it well.

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A recent AMA Council on Medical Education forum, “Foundational Understandings of Precision Education: Right Training, Right Physician, Right Time,” summarized a pilot project testing whether precision education can reconnect physicians with the learning process in ways that fit easily and effectively into their busy schedules.

One of those ways: By making recommendations based on physicians’ own patients and practice patterns.

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Precision education is much like precision medicine. It’s a system that leverages data and technology to improve personalization of education, efficiency of learning and patient outcomes. One of the ways it can improve physicians’ continuing professional development is by addressing the “unknown unknown.”

“Many of you who participate in CME know that often you select based on things that are particularly of interest to you, which leaves us vulnerable … we aren't selecting the things that we probably most need to learn more about,” said Kimberly Lomis, MD, vice president for undergraduate medical education innovations at the AMA.

Reconnect, a pilot project by multiple business units at the AMA, uses an augmented intelligence (AI) algorithm within the EHR to provide anticipatory guidance on upcoming patient visits. It is informed by feedback from more than three dozen physicians at all career stages and within a variety of disciplines.

“One of the things we heard was basically time and energy pressures have led to a sense of moral distress” around CME, Dr. Lomis said, adding that many of those interviewed said the same thing: I know I should be keeping up better, but it's just not tenable within my day-to-day practice.

Learn more with the AMA about precision education, which allows educators and learners to leverage data and technology to improve the personalization of education and the efficiency of learning.

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Reconnect probes a physician's upcoming clinic session for multivariate nuances within individual patient records, including comorbidities, treatment-diagnosis combinations and medication combinations, and also identifies trends within the physician’s practice patterns.

If a patient on that upcoming slate has a condition that the physician sees regularly, Reconnect prioritizes primary literature and cutting-edge new findings. If the problem is one not frequently encountered by that physician, the algorithm prioritizes secondary resources that are more summary.

“The kernel of this is the AI engine that probes for all this information and then is a recommender,” Dr. Lomis noted in her presentation at the forum, held during the 2023 AMA Annual Meeting in Chicago last month. “You use an AI recommender routinely, but it's often based on your preferences. This is a recommender that is driven by your actual pattern of practice.”

The tool elevates learning resources that may be useful to the physician given that panel of patients; it does not make clinical recommendations specific to patients. Patients’ protected health information (PHI) is hidden in the process and remains in the original EHR—no PHI is transferred to nor stored by the AMA.

The AMA has rolled out Reconnect at one health system and is primarily testing whether it can indeed deliver articles relevant to a physician's practice. Dr. Lomis is leading the education and implementation science elements of the project, and noted that Reconnect wouldn’t be possible without:

  • Dan Pickhardt, AMA director of product development.
  • Brian Tilley, who is leading research and evaluation.
  • Emi Nakamura, project manager.
  • Joe Marks, a consultant in developing the AI engine.

“We hope this will rekindle the excitement around learning and make the time the physician invests in their education higher-yield,” Dr. Lomis said.

The forum also featured a presentation by Jesse Burk-Rafel, MD, assistant director of the Precision Medical Education Lab at New York University Grossman School of Medicine. He detailed case studies on the use of AI in residency selection, linking education to patient care at scale, and precision medicine at the macro level.

A third presentation, by Carla Pugh, MD, PhD, director of the Technology Enabled Clinical Improvement Center at Stanford Medicine, summarized recent advances in medicine and surgery through precision feedback.

Read more about the highlights from the 2023 AMA Annual Meeting.