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MS - Health Informatics Concentration

What is Health Informatics?

Health informatics is the intersection of health care, information technology, and data science. It involves the collection, management, and use of information to improve patient care, health care outcomes, and the overall efficiency and effectiveness of the health care system and health interventions.

Terika McCall, PhD, MPH, MBA, Assistant Professor in the Biostatistics Department (Health Informatics Division) at the Yale School of Public Health, discusses what brought her into health informatics, as well as the real-world impact of her work and the field.

About the Program

The science of health informatics drives innovation-defining future approaches to information and knowledge management in biomedical research, clinical care, and public health. Health informatics (HI) comprises applied research and the practice of informatics across clinical and public health domains. Informatics researchers develop, introduce, and evaluate new biomedically motivated methods in areas as diverse as data mining, natural language or text processing, cognitive science, human-computer interaction, decision support, databases and algorithms for analyzing large amounts of data generated in public health, clinical research and genomics/proteomics.

The MS degree will provide well-rounded training in Health Informatics, with a balance of core courses from such areas as information sciences, clinical informatics, clinical research informatics, consumer health and population health informatics, data science and more broadly health policy, social and behavioral science, biostatistics and epidemiology. The length of study for the MS in HI is two academic years. First-year courses survey the field; the typical second-year courses are more technical and put greater emphasis on mastering the skills in health informatics. The degree also requires a capstone project in the second year.

Applicants should typically have an undergraduate degree with a focus in health, computer science or mathematics/statistics. Students with a master’s degree or other related degrees may be allowed to enroll in additional elective courses in lieu of required courses, if they can demonstrate prior proficiency in required courses.

The length of study for the MS in Health Informatics is two years. Part-time enrollment is not an option.

This program does not require General GRE scores.


For more information and to apply to the MS program, visit the Yale Graduate School of Arts and Sciences website. Please choose "Public Health" as the program. Then select Health Informatics as the concentration. Do not try to use SOPHAS.

I love the mentality and supportive atmosphere at YSPH. The confidence and the passion from faculty members and students were inspiring and made me want to join this big Y family.

Huan Li
MS '21

Degree Requirements - MS in Health Informatics

The M.S. degree requires a total of 14 course units. The M.S in Health Informatics requires the student to complete or acquire an exemption from the following courses. Full time students must carry a minimum of 4 course units each semester. If a course is waived, a substitute course must be identified. Alternative electives may be approved in consultation with the student’s advisor and DGS.

2023-24 Matriculation

All courses count as 1 credit unless otherwise noted.

MS Required Courses (10 course units)

  • BIS 633 Population and Public Health Informatics
  • BIS 634 Computational Methods for Informatics
  • BIS 560/ CBB 740 Introduction to Health Informatics
  • BIS 550/CBB 750 Topics in Biomedical Informatics and Data Science
  • EPH 508 (fall) Foundations of Epidemiology and Public Health or EPH 509 (spring) Fundamentals of Epidemiology
  • EPH 608 Frontiers of Public Health*
  • BIS 638 Clinical Database Management Systems and Ontologies
  • BIS 562 Clinical Decision Support or BIS 640 User-Centered Design of Digital Health
  • BIS 685 and BIS 686 Capstone in Health Informatics- 2 units

Informatics, Statistics and Data Science Electives: Minimum of 4 of the following REQUIRED

  • BIS 540 Fundamentals of Clinical Trials
  • BIS 557 Computational Statistics
  • BIS 567 Bayesian Statistics
  • BIS 568 Applied Machine Learning in Healthcare
  • BIS 620 Data Science Software Systems
  • BIS 621 Regression Models
  • BIS 628 Longitudinal and Multilevel Data Analysis
  • BIS 630 Applied Survival Analysis
  • BIS 691 Theory of Generalized Linear Models
  • CB&B 555 Unsupervised Learning for Big Data
  • CB&B 567 Topics in Deep Learning: Methods and Biomedical Applications
  • CB&B 645 Statistical Methods in Computational Biology
  • CB&B 663/CPSC 552/AMTH552 Deep Learning Theory and Applications
  • CB&B 745 Advanced Topics in Machine Learning
  • CDE/EHS 566 Causal Inference Methods in Public Health Research
  • CPSC 546 Data and Information Visualization
  • CPSC 564 Algorithms and their Societal Implications
  • CPSC 577 Natural Language Processing
  • CPSC 582 Current Topics in Applied Machine Learning
  • EMD 533 Implementation Science
  • EMD 553 Transmission Dynamic Models for Understanding Infectious Disease
  • EPH 510 Health Policy and Health Care Systems
  • HPM 560 Health Economics and U.S. Health Policy
  • HPM 570 Cost-Effectiveness Analysis and Decision-Making
  • IMED 625 Principles of Clinical Research
  • MGT 525 Competitive Strategy
  • MGT 534 Personal Leadership: Leading the Self Before Others
  • MGT 656 Managing Software Development
  • SBS 512/ MGT 612/ GLBL 6590/ ENV 632 Social Entrepreneurship Lab
  • S&DS 517 Applied Machine Learning and Causal Inference
  • S&DS 530 Data Exploration and Analysis
  • S&DS 562 Computational Tools for Data Science
  • S&DS 565 Introductory Machine Learning
  • S&DS 583 Time Series with R/Python
  • S&DS 584 Applied Graphical Models
  • S&DS 610 Statistical Inference
  • S&DS 663 Computational Methods for Data Science
  • S&DS 664 Information Theory
  • S&DS 670 Theory of Deep Learning

*Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.

rev. 06.26.23