Division of Biology and Medicine
Brown Center for Biomedical Informatics


BCBI offers educational programs and courses in informatics and data science for health for students at all levels.

BCBI Education

BCBI provides educational opportunities for students at all levels (high school, undergraduate, graduate, and medical students as well as medical residents/fellows and junior faculty). Programs and courses cover seven core competencies:

  1. Biomedicine and Health
  2. Data Science
  3. Implementation Science
  4. Health Information Technology and Digital Health
  5. Professionalism
  6. Leadership & Mentorship
  7. Team Science


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.

Learn More about SC-BMI SC Program

The Center for Computational Molecular Biology (CCMB) PhD program and its Biomedical Informatics track trains the next generation of scientists to perform cutting-edge research in computational biology and biomedical informatics. Students develop and apply novel computational, mathematical, and statistical techniques to problems in the life sciences. 

CCMB PhD students can opt to pursue a training program in the Biomedical Informatics track, which is affiliated with BCBI. The goal of this track in the program is to train students to study and pursue the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health. 

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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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Current Courses

This semester-long course provides a methodological survey of approaches used in biomedical informatics. Particular emphasis will be given to formalisms and algorithms used within the context of biomedical research and health care. Practical programming skills will also be taught within these contexts. This course has been developed as a Course-based Undergraduate Research Experience (CURE), where students will gain experience with the scientific method, its application, and presentation.

Instructors: Chen/Sarkar
Semester: Spring
Students: Undergraduate and Graduate

This survey course provides an overview of the field of biomedical informatics covering relevant topics in computer science, healthcare, biology, and social science. This course is designed to be complementary to Methods in Biomedical Informatics (BIOL 1555). Emphasis is given to understanding the organization of biomedical information, the effective management of information using computer technology, and the impact of such technology on biomedical research, education, and patient care.

Instructor: Sarkar
Semester: Fall
Students: Undergraduate and Graduate

This course covers the field of evaluation of health information systems (HIS) in a range of roles and environments, in the US and worldwide. It includes topics in health information system (HIS) design and deployment, healthcare workflow, quantitative and qualitative evaluation methods and socio-technical environment for HIS. Emphasis is given to understanding the range of evaluation questions that can be asked, identifying the key stakeholders, understanding available evaluation techniques, and designing rigorous but achievable studies. Examples will include Open Source systems, medical Apps, and economic evaluation, the role of evaluation frameworks and theories, and notable HIS successes and failures. Recommended: past or concurrent enrollment BIOL 1565 or a public health course covering clinical research.

Instructors: Fraser
Semester: Fall
Students: Undergraduate and Graduate

This course was relaunched in Spring 2022 as a student-led preclerkship elective to introduce informatics and data science topics for supporting clinical care, quality improvement, and research. From 2016 to 2021, this elective (formerly "Biomedical Informatics, Data Science, and Implementation Science Skills for Medical Students" and co-instructed by Drs. Chen, Elwy, and Sarkar) was offered during the summer for introducing students to basic data analytic skills needed for supporting research in biomedicine and health care. 

Instructors: Multiple
Semester: Spring
Students: Medical

Past Courses

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