Integration of Digital Health and Data Science on Medical Education Curriculum

Augusta Webster, MD, of the Office of Medical Education (AWOME), has begun to introduce a new vital component of the medical education curriculum to current medical students: education in Digital Health and Data Science.

Mahesh Vaidyanathan, MD, MBA, assistant professor of anesthesiology, and David Liebovitz, MD, associate vice president for clinical informatics in the Department of Medicine and co-director of the Center for Medical Education in Data Science and Digital Health, worked together to create the curriculum.

As students study the health data ecosystem, the health information regulatory environment, data science research and methods, digital health decision support, prejudice, ethics, and health equity, and the socio-technical context for digital health and data science, the new curriculum component's main goal is to see students encounter a variety of critical competencies and learning outcomes.

"The tools that data science offers to clinical care enable more effective and personalised care for our patients," said Liebovitz, who is also an associate professor of Medicine in the Division of General Internal Medicine and Geriatrics and of Preventive Medicine in the Division of Health and Biomedical Informatics. As a result, data science and digital health, along with anatomy, physiology, and other long-standing areas in medical school curricula, are now required components of our students' education."

Healthcare Quality and Patient Safety, Lifestyle Medicine, Health Equity and Advocacy, Medical Decision-Making and Diagnostic Testing, and Teamwork and Leadership are the other five strands of Feinberg's programme.

As students form small discussion groups, Liebovitz and Vaidyanathan have augmented the curriculum with topics on healthcare quality and patient safety. They've also created collaborative projects using online data analysis and visualisation platforms like Orange and Google Colaboratory, as well as developed online tools that clearly demonstrate machine learning to students.