MS - Health Informatics Concentration

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.

The MS degree requires a total of 14 course units. The MS 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 must be approved by the Program Directors.

All courses count as 1 credit unless otherwise noted.

MS Required Courses (9 course units)

• Public Health Primer - 0 units
  
• BIS 633 Population and Public Health Informatics
  
• BIS 634 Computational Methods for Informatics
  
• BIS 560 Introduction to Health Informatics *
  
• BIS 550/CBB 7500 Topics in Biomedical Informatics and Data Science*
  
• PUBH 508 Foundations of Epidemiology and 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

*BIS 560 and BIS 550 are prerequisites for BIS 685 and BIS 686

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

• BENG 5440 Fundamentals of Medical Imaging
• BIS 540 Fundamentals of Clinical Trials
• BIS 555 Machine Learning with Biomedical Data
• BIS 567 Bayesian Statistics
• BIS 568 Applied Machine Learning in Healthcare
• BIS 621 Regression Models
• BIS 623 Advanced Regression Models
• BIS 628 Longitudinal and Multilevel Data Analysis
• BIS 630 Applied Survival Analysis
• BIS 645/GENE 6450/CB&B 6470 Statistical Methods in Human Genetics
• BIS 691 Theory of Generalized Linear Models
• BIS 692/CB&B 6450 Statistical Methods in Computational Biology
• CB&B 5555 Unsupervised Learning for Big Data
• CB&B 5670 Topics in Deep Learning: Methods and Biomedical Applications
• CB&B 5700 Privacy-Enhancing Technologies in Biomedical Data Science
• CB&B 5740 Biomedical Natural Language Processing: Methods and Applications
• CB&B 5750 Bioinformatics Applications in Biomedicine
• CB&B 5760 Foundations of Real World Data Science: Electronic Health Records
• CB&B 5790 Distributed Artificial Intelligence on Biomedical Data
• CB&B 6663/CPSC 5520/AMTH5520 Deep Learning Theory and Applications
• CB&B 7520/MCDB 7520/ CPSC 7500/MB&B 7520 Biomedical Data Science: Mining and Modeling
• CDE 534 Applied Analytic Methods in Epidemiology
• CDE/EHS 566 Causal Inference Methods in Public Health Research
• CPSC 5370 Database Systems
• CPSC 5390 Software Engineering
• CPSC 5371 Database Design and Implementation
• CPSC 5460 Data and Information Visualization
• CPSC 5640 Algorithms and their Societal Implications
• CPSC 5700 Artificial Intelligence
• CPSC 5770 Natural Language Processing
• CPSC 5810 Introduction to Machine Learning
• CPSC 5820 Current Topics in Applied Machine Learning
• CPSC 5830 Deep Learning on Graph-Structured Data
• CPSC 6700 Topics in Natural Language Processing
• EMD 533 Implementation Science
• EMD 538 Quantitative Methods for Infectious Disease Epidemiology
• EMD 539 Intro Analysis and Interpretation of Public Health Surveillance Data
• EMD 553 Transmission Dynamic Models for Understanding Infectious Disease
• EMD 580/HPM 580 Reforming Health Systems: Using Data to Improve Health in Low- and Middle-Income Countries
• PUBH 510 Health Policy and Health Care Systems
• HPM 559 Big Data, Privacy, and Public Health Ethics
• 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 612/ GLBL 6590/ ENV 632 Social Entrepreneurship Lab
• MGT 656 Managing Software Development
• MGT 631 Public Health Entrepreneurship and Intrapreneurship
• MGT 698 Healthcare Policy, Finance, and Economics
• S&DS 5170 Applied Machine Learning and Causal Inference
• S&DS 5300 Data Exploration and Analysis
• S&DS 5620 Computational Tools for Data Science
• S&DS 5630 Multivariate Statistical Methods for the Social Sciences
• S&DS 5650 Introductory Machine Learning
• S&DS 5830 Time Series with R/Python
• S&DS 5840 Applied Graphical Models
• S&DS 6100 Statistical Inference
• S&DS 6310 Optimization and Computation
• S&DS 6630 Computational Methods for Data Science
• S&DS 6640 Information Theory

Please note, the following courses are not approved as electives:
CDE 538 Soda Politics
ECON 566 Machine Learning for Economic Analysis
S&DS 523 YData: An Introduction to Data Science

MENG 4154/BENG 4104: Medical Device Design and Innovation is not a graduate level course and will not count towards the MS degree

Note: Some courses offered outside of YSPH may not be offered

1. Select informatics methods appropriate for a given public health context
2. Compare the health information system structure and function across regional national and international settings
3. Assess population informatics needs, assets and capacities that affect communities’ health
4. Propose strategies to identify stakeholders and build coalitions and partnerships for influencing public health informatics
5. Communicate audience-appropriate public health content, both in writing and through oral presentation
6. Apply systems thinking tools to a public health informatics issue