In this online course on Medical Care Epidemiology (MCE), learners will gain a comprehensive understanding of how population-based data is used to study and improve health system performance. The course focuses on the analysis of geographic and institutional variation in medical care—differences in the rates and types of care patients receive depending on where they live or which hospital they visit. These variations often reveal disparities that are not explained by patient needs or preferences and are considered “unwarranted.” By learning to identify and interpret such variation, students will become equipped to critically assess the quality, efficiency, and equity of healthcare delivery.
The course begins with a foundational overview of epidemiology as it applies to medical care, including how descriptive and inferential studies reveal patterns in healthcare utilization, outcomes, and resource allocation. Learners will explore the history and significance of the Dartmouth Atlas of Health Care, a pioneering effort in mapping and analyzing variations in care, especially in the Medicare population. This context sets the stage for students to understand how empirical evidence can shape health policy, promote transparency, and drive system-level change.
Students will also engage with key frameworks used to analyze variation, such as the distinction between warranted and unwarranted variation and the four primary causes of unwarranted variation: ineffective care, preference-sensitive care, supply-sensitive care, and fragmented (integration-sensitive) care. Through real-world case studies—including examples from pediatric asthma, end-of-life care, and prescription drug use—students will see how these frameworks are applied in practice and what implications they have for patient outcomes and health system accountability.
The course introduces several quantitative tools used to measure variation, including extremal quotients, interquartile ratios, and coefficients of variation. Students will learn how to calculate these statistics, interpret their meaning, and understand their limitations. Special attention is paid to the importance of risk adjustment, bias detection, and fair comparisons across healthcare regions and institutions. Through visualizations such as funnel plots and mapping techniques, learners will develop the ability to communicate complex findings in accessible ways.
A significant portion of the course addresses patient attribution and health service areas—how populations are linked to specific providers or regions for the purpose of measurement and accountability. Learners will gain insight into how health care researchers define these units and the importance of precise attribution in analyzing outcomes and making policy recommendations.
Ultimately, the course prepares learners to think critically about the structures and systems that shape healthcare delivery. By combining epidemiological methods with real-world data and ethical considerations, students will be equipped to contribute to evidence-based reforms that improve healthcare access, quality, and efficiency.
