Precision medicine鈥檚 promise to achieve better outcomes through a highly personalized approach is not limited to patients. Some health educators propose that graduate medical education also should be advanced by precision medical education (PME).
The whys and wherefores of combining big data with personalized learning plans and competency-based assessment were the focus of 鈥淧recision Surgical Education: Using Big Data for Targeting Learning鈥� held during Clinical Congress 2025.
Jeremy Lipman, MD, MHPE, FACS, a colorectal surgeon and director of graduate medical education at the Cleveland Clinic in Ohio, and Tyler Loftus, MD, PhD, FACS, program director of the General Surgery Residency Program at the University of Florida in Gainesville, served as moderators.
Ray Phitayakorn, MD, MPHE, FACS, a general and endocrine surgeon at Harvard Medical School in Boston, Massachusetts, opened the session with a brief review of the history of medical education, beginning with a multiplicity of unregulated and nonuniform educational pathways, and progressing through the advent of a standardized, 4-year medical school curriculum, development of the residency system, and rise of multiple examinations and continuous certification.
Dr. Phitayakorn stated that this rigorous but 鈥渙ne size fits all鈥� method is giving way to PME, which functions by helping learners achieve defined competencies, milestones, and entrustable professional activities (EPAs). The change is positive, if challenging, he contended.
鈥淲hat is very exciting to me about precision medical education is that it鈥檚 very focused on the individual learners,鈥� Dr. Phitayakorn said. 鈥淚 can delve deep and really help individual learners, instead of just broadcasting and hoping people learn.鈥�
He also identified the opportunity for 鈥渕icrocredentialing鈥� among surgeons in topics such as artificial intelligence (AI), clinical informatics, and genomics, as well as 鈥渢he classic example,鈥� billing.
In addition, Dr. Phitayakorn noted drawbacks of PME. These included resource-intensiveness, equity issues, and the need to redefine workflows and outcome metrics.
Brenessa Lindeman, MD, MEHP, FACS, assistant dean for graduate medical education at The University of Alabama at Birmingham, further expanded on the pros and cons of PME.
鈥淚 think that precision medical education holds lots of promise. There are many exciting directions this could go in the future,鈥� such as individualized progress and highly specific feedback, as well as transparent decisions about trainees鈥� readiness to perform specific activities, she said.
However, Dr. Lindemann described the need to create new systems for such methods, including ones that gather and safeguard data. This raises complex questions, including on the national rankings of academic medicine: 鈥淗ow comfortable does your institution feel about external access to your data?...There is pressure among institutions to show no weaknesses.鈥�
She also said that underperformance among individuals may not be a good basis for decision-making, while 鈥減utting some groups at systematic disadvantage because of our processes鈥� may require attention as well.
Furthermore, she asked if or when medical students鈥� EPA evaluations should be passed to program directors, and a residents鈥� EPAs to licensing, credentialing, and hiring entities鈥攓uestions she acknowledged as likely to be answered differently by various stakeholders.
Kimberly Lomis, MD, vice president for medical education innovations at the American Medical Association in Chicago, Illinois, focused on transitions into residency and practice. Her presentation included articulating a 鈥渜uintuple aim鈥� for PME: improving outcomes, enhancing learners鈥� experiences, reducing education costs, improving equity in educational processes, and augmenting the work-life of healthcare education professionals.
She also noted the opportunities opened by AI, such as in-depth data mining to determine what cases a resident is really controlling and ensuring that they receive coaching on those specific cases. Additionally, she described records review using agentic large language models, and the option to 鈥渞epurpose the ambient scribe capture to provide feedback on communication skills.鈥�
Aside from technological improvements, Dr. Lomis advocated for a 鈥渄eliberate nurturing environment,鈥� which she said could still be 鈥渄emanding and hard work, but there has to be a mental model that everyone is improving throughout our organization.鈥�
Finally, Brian C. George, MD, MAEd, the Hugh Cabot Professor of Surgery and associate professor of learning health sciences at the University of Michigan in Ann Arbor and involved with the nonprofit The Society for Improving Medical Professional Learning (SIMPL), dove into the details of PME standards.
His slides were bold, stating, 鈥淢edical education is undergoing a scientific revolution,鈥� but his words were more tempered: 鈥淚t鈥檚 very, very early.鈥�
He explained that at SIMPL, healthcare education specialists have begun to consider how to benchmark performance, apply standards, and ensure healthcare institutions can overcome barriers to implementation. The process is challenging, Dr. George said, involving considerable logistical barriers, as well as gaps in research on appropriate standards and assessment methods.
Like Dr. Lomis, he advised AI use. In specific, he mentioned the possibility of AI input on appropriate statistical methods for assessment.
However, he also described inherent subjectivity in how to prioritize what activities should have standards imposed on them. 鈥淚鈥檓 not sure there can be science on this,鈥� he said.
In sum, the session confronted the reality that PME requires a unique combination of new technology and big data, combined with a new approach to communal priority-setting and human interconnectedness鈥攊n its own way, an apt summary of the modern era within and beyond surgeon education.
