The Scholarly Concentrations (SC) Program was developed to promote scholarly excellence at The Warren Alpert Medical School; to produce scholarly leaders in medicine, research, education, and advocacy; and, to enrich the student experience, the Medical School community, and the greater society.

Scholars in the Scholarly Concentration in Biomedical Informatics (SC-BMI):

1. Gain familiarization with core biomedical informatics principles through a guided review of foundational literature and topical discussions and
2. Develop a biomedical informatics solution that addresses a specific biomedical or healthcare challenge in collaboration with experts.

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The Biology ScB Tracks consist of three additional courses beyond the core that form a cohesive group in a subdiscipline of the biological sciences. BIOL1565 is required. Two additional courses are selected from: BIOL1555, BIOL1575, and BIOL1595.

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The ScM in Health Informatics program blends theoretical informatics foundations with real-world applications in health and healthcare.

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The electronic health record (EHR) has become an essential tool for supporting and evaluating health care delivery. This pre-clerkship elective will provide a glimpse of how EHRs have evolved, how they can impact clinical practice, and views on their future uses. This course includes lectures, interactive discussions, and first-hand accounts from physicians demonstrating how EHRs are used in practice. A major feature of the course is using a real EHR system for simulating physician/patient interactions. 

Instructors: Multiple
Semester: Spring 2017 (student-led: Aluthge/Bhatia/Sinha), Fall 2019 (faculty-led: Chen/Fraser/Gillerman/Hilliard/Sarkar)
Students: Medical

This intensive five-day short course will include both lectures and hands-on experiences for introducing data science concepts and the field of biomedical informatics with a focus on the sub-discipline of clinical informatics. Participants will be taught foundational computing skills for exploring and analyzing electronic health data. In addition, participants will be exposed to decision support, reporting, and analytic tools associated with electronic health record (EHR) systems at local health systems. The knowledge and skills obtained through this course will prepare participants for more advanced one-day sessions and other courses offered throughout the year (e.g., in statistics and machine learning).

Instructors: Chen/Gillerman/Hilliard/Sarkar
Semester: Fall 2017 (Brown & UVM), Winter 2018, Spring 2020 (canceled)
Students: Medical residents/fellows and junior faculty

Modern health care relies on the ability to best interpret available data and transform it into usable information for healthcare providers and biomedical researchers.  This course provides lectures and hands-on experiences to introduce pre-college students to data science-concepts and the field of biomedical informatics. By the end of the course, students have developed foundational skills for using biomedical and health data that can be used to support biomedical research, medicine, and public health.

Instructors: Chen/Sarkar
Semester: Summer 2016-2018
Students: High school students

This course introduces basic statistical skills using R for supporting research in the life sciences and public health. The overall course is done in the context of student-chosen projects, with the goal of establishing necessary foundational statistical techniques for supporting longer-term research goals.

Instructors: Sarkar/Stey/Sullivan
Semester: Winter 2018-2019
Students: Graduate students

This one-week online module will provide an overview of key aspects of artificial intelligence, with a focus on big data and machine learning, that are increasingly poised to impact the practice and delivery of health care. Through examination of primary literature, students will gain insight to the promises and challenges of machine learning in clinical and public health contexts.

Instructors: Chen/Sarkar
Semester: Summer 2019
Students: Professionals (part of the Data-Driven Decision Making course for the Executive Master of Healthcare Leadership

This course teaches the fundamental theory and methods of artificial intelligence (AI) alongside their application to the biomedical domain. It gives a representative overview of traditional methods as well as modern developments in the areas of (deep) machine learning, natural language processing and information retrieval. The course is designed to be accessible to non-computer science audiences and does not require extensive prior programming experience. The course is accompanied by practical assignments applying the discussed techniques in a biomedical context.

Instructors: Eickhoff
Semester: Spring 2022
Students: Undergraduate and Graduate