Courses

The Brown Center for Biomedical Informatics (BCBI) offers courses for students at all levels (high school, undergraduate, graduate, and medical students as well as medical residents/fellows and junior faculty). For more information contact us at [email protected].

BIOL 1555/PHP 2561 Methods in Informatics and Data Science for Health
Spring; undergraduate and graduate students
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.
Syllabus

BIOL 1565 Survey of Biomedical Informatics
Fall; undergraduate and graduate students
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.
Syllabus

BIOL 1575 / BIOL 2075 Evaluation of Health Information Systems
Spring; undergraduate and graduate students
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.
Syllabus

BIOL 1595/BIOL2595 Artificial Intelligence in Biomedicine
Spring; undergraduate and graduate students
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.
Syllabus

BIOL 6535 Biomedical Informatics and Data Science Skills
Summer; medical, undergraduate, and graduate students
This three-week preclerkship elective introduces students to basic data analytic skills needed for supporting research in biomedicine and health care.
Syllabus

BIOL 6683 Introduction to the Electronic Health Record
Fall; medical students
The electronic health record (EHR) has become an essential tool for supporting and evaluating health care delivery. This preclerkshipl 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.

CEBI 0971 Biomedical Informatics and Data Science Skills for Biomedicine and Health Care
Summer; high school students
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.

Introduction to Statistics in R
Winter; graduate 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.
Syllabus

5x5 Clinical Informatics Short Course: 5 Topics and Computing Skills in 5 Days
Winter and Summer [planned]; medical residents/fellows and junior faculty
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).
Syllabus

Big Data and Machine Learning in Health Care Module
Summer; part of the Data-Driven Decision Making course for the Executive Master of Healthcare Leadership.
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