Analysis of Healthcare Effectiveness and Outcomes

Description from DukeHub

Participants in the health care ecosystem, including lifesciences product manufacturers, medical service providers, health and wellness app developers, and payers, increasingly have access to vast amounts of observational data. This course focuses on the analysis of real-world observational data to draw inferences about effectiveness of various interventions at individual patient and/or community levels. An important component of the course is the study of potential confounding factors and the use of quasi-experimental methods, such as propensity score matching, instrumental variables regression, and difference-in-differences estimation, to mitigate their effects. The course will demonstrate these methods using examples from various health care sectors. This course is for MSQM: Health Analytics students.

Skills

• data and statisticsdescription: "regression" and 1 more

AI-resilient: 1 of 5 1/5

• No: Physical, lab or field work
• ✓Yes: Live work with people · "patient"
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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