In this course on Medical Care Epidemiology, learners will explore the foundational principles and applied practices of measuring and understanding health care delivery through the lens of epidemiology. The course begins by examining how epidemiology traditionally focuses on disease prevalence and incidence but extends that framework to evaluate health care systems—specifically, the variation in medical care practices across different regions, providers, and populations.

A major focus of the course is the concept of unwarranted variation —differences in health care utilization that cannot be explained by patient illness or preference. Learners will analyze how such variation reflects inefficiencies and inequities in the health system and reveals opportunities for improvement in care quality, access, and cost-effectiveness. By reviewing seminal studies such as those by Wennberg and Gittelsohn, as well as the development and findings of the Dartmouth Atlas of Health Care, learners will grasp how data-driven approaches illuminate regional and provider-level disparities in service use and outcomes.

Students will examine various study designs used in medical care epidemiology, including cross-sectional, cohort, and ecologic studies. Emphasis will be placed on understanding the appropriate units of analysis—such as hospital service areas, referral regions, and provider groups—and how these influence data interpretation. The course also covers threats to valid inference, including chance, bias, and confounding, and provides learners with tools to critically evaluate research findings.

Another core component is the classification of variation into types: effective care , preference-sensitive care , and supply-sensitive care. Each type highlights different root causes and requires different remedies. For example, preference-sensitive care often results from a lack of shared decision-making, while supply-sensitive care stems from provider behavior influenced by resource availability rather than patient need. Through case studies and data visualizations, students will see how these concepts apply in real-world contexts such as neonatal intensive care, end-of-life treatment, and preventive screening.

The course also introduces students to methods of visualizing variation , including caterpillar plots, funnel plots, and geographic mapping of hospital service metrics. These tools help illustrate how variation manifests across the health system and inform stakeholders about areas needing reform or oversight.

Finally, students will reflect on how capacity—defined in terms of clinician labor, hospital beds, and technology—is distributed and its influence on care delivery. They will learn that increased capacity does not always translate to better outcomes and may lead to overuse, inefficiency, or harm. The course emphasizes the importance of aligning health care resources with patient needs and evidence-based practices.

By the end of the course, learners will be equipped to interpret health system performance through epidemiologic methods, critically assess health care variation, and contribute to improving quality, equity, and efficiency in health services.