Course Offerings

Explore designing and implementing information technologies to improve healthcare delivery, healthcare management, and health outcomes. Offered on the letter-grade basis only.

No description provided.

Overview of public health and the information systems used to achieve public health goals. This course is divided into three parts: (1) overview of public health, (2) fundamentals of public health informatics, and (3) public health information systems.

Leveraging medical claims data to guide population health interventions, primarily through the use of machine learning models. The course will focus on the data processing pipeline, and no prerequisite knowledge of machine learning models is required

Explore principles and methodologies in health informatics research, including various approaches to data analysis, research design, and the application of informatics to health. Develop skills in reading, reviewing, and writing scientific publications, identifying research questions, initiating research, and communicating findings.

The course is designed for undergraduate students who are interested in understanding, analyzing, designing, evaluating, or developing technologies to serve the health needs of general consumers. It covers the concept of consumer health informatics, health behavior theories, health information seeking and information retrieval, various forms of consumer health systems, and the design and evaluation of such systems.

New Topic for Spring 2025. Description pending submission by instructor, Steve Hershman. Also offered as Informatics 320D.

This course starts by discussing broad landscape of epistemological and theoretical perspectives and styles of reasoning and by situating in it quantitative research. It introduces you to the foundational concepts in quantitative research methods, such as causality, conceptualization, operationalization, measurement and sampling. It presents experimental design, survey design, and basic descriptive and inferential (frequentist) statistics, as well as a brief introduction to Bayesian inference and statistics.

What does it really mean to be user-centered? How do we practice user-centered design in a professional and methodical manner? What research findings can we rely on to help us improve user experiences? This is a readings/discussion course that examines in depth what we know about people (that is, what does scientific research actually tell us) and how can we apply this knowledge in the real-world of experience design. We examine human psychology, from physical ergonomics to cultural dispositions, stopping off on cognition and social analyses en route, so as to have a holistic, robust perspective on what it means to understand users. The readings are complemented with an examination of methods e.g., what is a cognitive walkthrough and how do you do it reliably? what are the limitations of heuristic evaluations? The goal is to give you a solid grounding in the practices of user-centered thinking, regardless of your area of application, and prepare you for professional level contributions in the user-experience world. There is no teamwork, all students deliver individual term papers and design critique diaries. There are also no pre-requisites -- technical or theoretical, the class is open to all.