Course Descriptions

Course director: Fuchiang (Rich) Tsui, PhD

This course is designed to provide a survey of the major topic areas in medical informatics, especially as they apply to clinical research. Through a series of lectures and demonstrations, students will learn about topics such as medical data standards, electronic health record systems, natural language processing, clinical research informatics, clinical decision support, imaging informatics, public health informatics, consumer health informatics, perioperative informatics, and mental health informatics. 

* Prerequisites: 

• (RECOMMENDED)  Basic familiarity with Biomedical Concepts.
• (REQUIRED) Knowledge of basic Pathophysiology.

Course director: Dokyoon Kim, PhD

This course is offered during the spring semester and is intended to provide in-depth, practical exposure to the design, implementation, and use of databases in biomedical research. This course is intended to provide students with the skills needed to design and conduct a research project using primary and secondary data. Topics to be covered include: database architectures, data modeling approaches, data normalization, database implementation, client-server databases, concurrency, validation, Structured-Query Language (SQL) programming, reporting, maintenance, and security. All examples will use problems or data from biomedical domains. MySQL will be used as the database platform for the course, although the principles apply generally to biomedical research and other relational databases.  (This course has been offered in the past as EPID 635.)

*Prerequisites:

• (REQUIRED) Knowledge of basic Pathophysiology.

Course director: Brielin Brown, PhD and Anurag Verma, PhD

In this course, we will use RStudio/R and other freely available software to learn fundamental data science applied to a range of biomedical informatics topics, including those making use of health and genomic data. After completing this course, students will be able to retrieve and clean data, perform explanatory analyses, build and evaluate models to answer scientific questions, and present visually appealing results to accompany data analyses; be familiar with various biomedical data types and resources related to them; and know how to create reproducible and easily shareable results with RStudio/R and GitHub.

Recommended prerequisite: Introductory-level statistics course. Familiarity with programming or a willingness to devote time to learn it. NOTE: Non-majors need permission from the department.

Course director: Shefali Setia Verma, PhD and Rachel Kember, PhD

This course is designed to provide an in-depth look at four topics that are of essential importance in biomedical informatics. Each topic will be allotted four consecutive weeks in the class schedule, as four modules, with the intention that each module becomes its own “mini-course”. The topics for each module may rotate from semester to semester, based on these criteria:

• Historical importance to the current field of biomedical informatics research and/or practice
• Cutting-edge developments in biomedical informatics
• Topics not covered in depth in BMIN 5010
• Consensus of the program leadership and teaching faculty

Possible modules include:

• Deep learning methods for mining biomedical data
• Visualization analytics for clinical research
• Methods for integration of observational and ecological data for public health surveillance
• Informatics implications for distributed research networks
• Human computer interaction and patient safety
• Nature-inspired analytics for biomedical informatics
• Network science applications in biomedical informatics
• Intersections of clinical research, clinical and clinical research informatics, and clinical decision making

* Prerequisites:

• (REQUIRED) BMIN 5010: Introduction to Biomedical and Health Informatics
• (REQUIRED) Knowledge of basic Pathophysiology.
• (RECOMMENDED) BMIN 5020: Databases in Biomedical Research and BMIN 5030 Data Science

Course director: Michael Padula, MD, MBI

This survey course is designed to provide an overview of health information standards and clinical terminologies.  Through a series of lectures, demonstrations, and hands-on exercises, students will learn about topics such as standards, interoperability, data modeling, vocabularies, and health information exchange. 

* Prerequisites:

• (RECOMMENDED) BMIN 501: Introduction to Biomedical and Health Informatics
• (REQUIRED) Knowledge of basic Pathophysiology.

Course director: Ross Koppel, PhD 

The course covers five main topic areas that will employ case studies from health system applications as well as models, techniques, and theory. 
 
1. Sociotechnical and human-centered design everyday life and in biomedical informatics; 2. Evaluation and measurement of usability; 3. The use of AI in medicine and medical informatics; 4. Implementation and optimization—including tensions among existing vs revised workflows, new software vs legacy systems, vendor software vs need for new builds, customization, retrofits, dongles, etc; 5. Ethics, policy, cybersecurity, and advocacy. 
* Prerequisites:

• (RECOMMENDED) BMIN 5060: Standards and Clinical Terminologies 
• (REQUIRED) Knowledge of basic Pathophysiology.

Course Director: Hanieh Razzaghi, PhD

This course provides an in-depth survey of the concepts and principles of learning health systems, focusing on the learning cycle, and the role of biomedical informatics throughout the cycle. Examples of mature learning health systems, as well as those in development, will be covered in detail. Students will gain practical experience in the development of a prototype learning health system. This course is required for the MCI, MSBMI, and PhD degrees.  

*Prerequisites:

• (REQUIRED) Knowledge of basic Pathophysiology.

Course Director: Susan Harkness Regli, PhD

The course provides a foundation for practical, hands-on application of human computer interaction in the design and implementation of technology-based healthcare applications.

Topics will include:

1. Human Computer Interaction history, key concepts, and relation to human factors

2. Complex applications and multiple methods for design in applications such as electronic health records, mobile applications, and safety event reporting

3. Practical application of methods such as problem definition, contextual interview, think-aloud protocol, cognitive walkthrough, and usability testing

4. Artificial Intelligence in healthcare including of Natural Language Processing (NLP) and Large Language Models (LLMs) as tools for documentation and reducing clinician burnout.

Students will be expected to conduct a course-long project applying human computer interaction methodologies to a problem to be chosen using problem definition methods addressed in class, as well as completing all reading assignments and presenting one topic to the class.

 *Prerequisites:

• (REQUIRED) BMIN 5070:  Human Factors

With mentorship from their Capstone Advisor, students will develop and present the results of a clinical informatics project relevant to their interests. During this semester-long course, students will attend a weekly seminar in which they develop, propose, implement, and present their capstone project. Students meet with regularly with their Capstone Advisor, who is also invited to attend the seminars. The seminar affords both students and advisors the opportunity for cross-fertilization of ideas and skills, and ultimately the honing of projects to a high level of value for the students and the clinical environments in which they conduct their projects.

*Prerequisites:

• (REQUIRED) Minimum of 7 CUs of the required coursework of the MCI Program
• (REQUIRED) Knowledge of basic Pathophysiology.