In 2015, the Obama administration launched the Precision Medicine Initiative as a means to support “the emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” Precision medicine signifies a transition away from “one-size-fits-all” medical research and practice and towards that which takes into account individual genetics, environment, and preferences. While precision medicine and the older, more established “personalized medicine” share the same founding principles, precision medicine is a little more clear: neither precision nor personalized medicine aimed to develop unique treatments for specific individuals. Rather, they are flexible approaches for the development of treatment plans that take into account genetic, environment, and personal choice factors.
All of this to introduce “Precision Education,” a play on precision medicine that several medical schools – including Johns Hopkins and Stanford – have made in the last several years. Of note, the precision education world seems to be relatively small: The former dean of Johns Hopkins School of Education, David Andrews, is the new president of National University. Other big names in the small field are Sara Hart (Florida State University) and Carole Beal (University of Florida), who work in reading and open data, respectively.
Big Data and Learning Algorithms: National University School of Medicine
At National University, faculty are developing micro-competencies for their basic science courses. For example, if a course has eight key concepts, each concept is divided into 5-6 micro-competencies. The instructor develops quizzes for each micro-competency. When students show a weakness in a micro-competency, they are prompted to remediate.
Faculty are encouraged to find open educational resources (OER) and multimedia content – multiple material types for each concept. As the school collects student learning data, they will work to identify which student profiles match which material type. Once these patterns are established, they will be able to make recommendations to students on how to best remediate.
Connected and Crowdsourced: Johns Hopkins School of Medicine
Johns Hopkins appears to be taking the Connected Learning route for Precision Education. It is focused on enhancing student agency and success through a focus on personal interests, peer support, and personalized feedback.
As part of the new Genes to Society curriculum, students are taught a new model of health: one that treats health and disease in the context of all factors (genetics – environment – socio-cultural – personal) simultaneously and at every student level. Digital materials are written by students and instructors to help new students make the connections across basic science, practice, and population medicine. These resources live in an accessible digital space, where students can access and make contributions across space and time.
Additionally, digital networks of crowdsourced articles, videos, discussion, and notes prepared by peers allow students to explore facets of subjects they find personally compelling before, during, and after whole-group learning. but that this approach allows courses to grow into dynamic collections of materials shared by faculty as well as students.
Students were given the option of face-to-face or digital learning activities to supplement their clinical experiences in the required neurology clerkship. Student learning preferences (abstract v. concrete, teacher- v. student-structured; and type of learning environment) significantly impacted their personal satisfaction with the digital vs. face-to-face formats. Assessment outcomes were the same across groups.
In the Surgery Department, resident physicians received EMR-generated “electronic scorecards” for how consistently they ordered the appropriate DVT prophylaxis for their patients. Residents received their scorecards monthly and low performers were provided with individualized remediation.
Optimizing Self-Directed Learning: Standford University School of Medicine
Stanford University is also trying to improve student options for outside-of-class learning by enhancing student access to quality multimedia learning tools. This includes app-driven case-based learning (with built-in knowledge checks) and a partnership with Osmosis online medical education videos. The university is attempting to generate a recommendation engine (like Netflix) to help students make good decisions about self-directed study.
Additionally, Standford University’s Discovery Curriculum offers 2- and 3-year pre-clerkship options. The three-year option provides time for independent research, dual degree options, or any other longitudinal scholarship or leadership activities.