Program Overview

BMIF 204: Foundations of Clinical Data and its Applications

BMIF 204 provides a hands-on experience of how clinical data drives modern healthcare and bioinformatics. Spanning 13 weeks, the course begins with a theoretical foundation, exploring clinical decision-making, EHR systems, and the flow of data from clinical care to research.

Course Details

• The course is 4 credits and is offered in the Fall Semester.
• It covers core topics such as disease prediction, treatment outcome prediction, causal inference principles, target trial emulation, and real-world evidence generation.
• The course addresses critical challenges such as high-dimensionality, time-related biases, and temporal shifts in data, emphasizing ethical and regulatory considerations like privacy, fairness, and historical biases in healthcare.

Course Structure

• The first half of the course (Weeks 1–7) focuses on theoretical foundations.
• The second half (Weeks 8–13) transitions to a hands-on lab format, where students work collaboratively on two projects with actual EHR and claims data.

Learning Outcomes

• By the end of the course, participants will have the skills to clean, integrate, and analyze complex clinical datasets.
• They will be able to communicate ethical implications and design robust, data-driven solutions for pressing healthcare challenges.

Faculty

• Sebastian Schneeweiss, MD, ScD, Professor of Medicine, Harvard Medical School, and Professor in Epidemiology, Harvard T.H. Chan School of Public Health.

Schedule

• Class meets Wednesdays 9am - 12pm and Fridays 10am - 11am.
• First Lecture: September 3rd
• Last Lecture (project presentations): December 3rd

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• Department of Biomedical Informatics
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  • Michael Baym
  • Tianxi Cai
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• Artificial Intelligence
• Biomedical Discovery Infrastructure
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• Taxonomy of Disease

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