MS in Clinical & Epidemiological Research Tracks

Overview

The MS program currently offers three optional tracks of specialized instruction for scholars to enroll in. Scholars in these tracks take a set of required coursework reflecting focused instruction in their track’s specific objectives. If you are opting to complete one of these tracks, please review the relevant section below for specific course details. Scholars may choose to join these tracks at any time during their residency in the program.

Clinical Informatics Track

Clinical Informatics is the science of how to use and apply data, information, and knowledge to improve human health and health care services. The Clinical Informatics Track within the MS program seeks to help learners approach clinical informatics research and the translation into practice with scientific rigor. Topics will include electronic health record systems, artificial intelligence for human health care, data systems and standards, clinical decision support, clinical informatics health policy and regulation, and implementation and evaluation.

The Clinical Informatics track is ideal for learners who plan to pursue research using electronic health records data or research on the design, implementation, or evaluation of clinical informatics applications.

Learners in the Clinical Informatics Track will be required to take Introduction to Clinical Informatics (EPI 232), Use of Electronic Health Record Data for Research (EPI 231), and Introduction to Clinical Artificial Intelligence (EPI 233). As we build content within this track, we expect that additional elective courses may be available in the 2026-2027 academic year.

Health Data Science Research Track

Data science in clinical research is an emerging discipline in response to the explosion of available and complex data in biomedicine and related streams. Examples of complex data include those from the laboratory (e.g., genomics and other “-omics”), biomedical imaging, electronic medical records, and other “found” data (e.g., social media). The TICR Program believes data science in the context of clinical research is best understood as an interdisciplinary hybrid of the fields of informatics, computer science, biostatistics, and epidemiology. As such, a data scientist has a broad background and expertise in accessing data, manipulating data, and forming inferences (i.e., summarizing raw data into meaningful messages) from data.

The Health Data Science in Clinical Research Track of the MS in Clinical and Epidemiological Degree Program is tailored for researchers who seek to work in complex data environments (sometimes referred to as “Big Data”) and who desire to become facile in the manipulation of large (and perhaps unstructured and unwieldy) data structures and the summarization of data into meaningful messages. Coursework in the data science track extends upon MS program’s foundation of epidemiology and biostatistics to include focused instruction in advanced data manipulation, prediction, clustering/pattern recognition and data reduction. The Data Science in Clinical Research Track distinguishes itself from other data science training programs by being embedded into the context of human subjects-based health-related research and a solid base of epidemiology and clinical research. Many of the contextual examples used in the courses and student projects are from the life sciences and clinical care. Graduates of the Data Science in Clinical Research Track are poised to work in either leadership or supportive roles in academia, industry, or municipal health systems.

The Data Science track is ideal for learners interested in building careers working with complex data, precision medicine, and electronic health record systems. Learners in the Data Science Track will be required to complete the following courses: DATASCI 213 (2 units), DATASCI 214 (2 units) and DATASCI 216.

Implementation Science Track

Enrollment in this track (or courses) is not guaranteed due to high demand.

Beginning the 2025-2026 academic year, students who wish to complete the Implementation Science Track as part of the MS degree must complete a brief application, which will be evaluated on a case-by-case basis by the MS/ATCR in Clinical and Epidemiologic Research Program Directors. Matriculated Students will receive an application at the start of the summer quarter. Enrollment is not guaranteed.

Implementation science (IS) aims to improve the adoption of evidence-based practices and policies in clinical care and public health, and the development of best evidence through community engagement. Responding to the increasing global concern that the tremendous advances we have achieved in developing effective tests, treatments and preventive measures are not being fully translated into improved population health, the IS track focuses on applying clinical research in real-world settings.

The IS track is ideal for researchers who plan to pursue the development, implementation and/or evaluation of policies, practice-based interventions and/or community-based programs designed to: 1) improve uptake/safety/quality/access; 2) reach diverse populations; 3) reduce the overuse of diagnostic tests or treatments; or 4) provide preventive medicine or health promotion programs.

Students completing the track must complete EPI 245 and two additional ImS electives. Students may not take more than 3 ImS courses as part of the MS program. Students can take ImS coursework in year 1 or year 2. Several ImS courses are impacted and have limited enrollment. Priority is given to MS year 2 students who have committed to the ImS track.