Health Informatics Concentration (HI)
The M.S. with a concentration in Health Informatics is a two-year program that provides 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, and data science, and more broadly health policy, social and behavioral science, biostatistics, and epidemiology. 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.
Applicants should typically have an undergraduate degree with a focus in health, computer science, and mathematics/statistics. Students whose native language is not English must take and submit scores from the TOEFL or IELTS examination. Part-time enrollment is not permitted.
Degree Requirements
The Health Informatics concentration consists of a total of fourteen courses: eight required courses, four electives, and satisfactory completion and presentation of a yearlong, two-course capstone project. Students demonstrating a mastery of topics covered by the required courses may replace them with more advanced courses but must receive written permission from the DGS and their adviser prior to enrolling in the substitute courses.
The graduate school requires an overall grade average of High Pass, including grades of Honors in at least two full-term graduate courses for students enrolled in a two-year program. In order to maintain the minimum average of High Pass, each grade of Pass on the student’s transcript must be balanced by one grade of Honors. Each grade of Fail must be balanced by two grades of Honors. If a student retakes a course in which the student has received a failing grade, only the newer grade will be considered in calculating this average. The initial grade of Fail, however, will remain on the student’s transcript. A grade awarded at the conclusion of a full-year course in which no grade is awarded at the end of the first term would be counted twice in calculating this average.
Curriculum
Required Courses
| BIS 550 | Topics in Biomedical Informatics and Data Science | 1 |
| BIS 560 | Introduction to Health Informatics | 1 |
| BIS 562 | Clinical Decision Support | 1 |
| or BIS 640 | User-Centered Design of Digital Health Tools | |
| BIS 633 | Population and Public Health Informatics | 1 |
| BIS 634 | Computational Methods for Informatics | 1 |
| BIS 638 | Clinical Database Management Systems and Ontologies | 1 |
| BIS 685 | Capstone in Health Informatics | 1 |
| BIS 686 | Capstone in Health Informatics | 1 |
| EPH 508 | Foundations of Epidemiology and Public Health | 1 |
| or EPH 509 | Fundamentals of Epidemiology | |
| EPH 608 | Frontiers of Public Health 1 | 1 |
| 1 | Students entering the program with an M.P.H. or relevant graduate degree may be exempt. |
| MS Suggested Electives in Informatics, Statistics and Data Science (4 course units) | ||
| BIS 540 | Fundamentals of Clinical Trials | 1 |
| BIS 567 | Bayesian Statistics | 1 |
| BIS 568 | Applied Artificial Intelligence in Healthcare | 1 |
| BIS 620 | Data Science Software Systems | 1 |
| BIS 621 | Regression Models for Public Health | 1 |
| BIS 623 | Advanced Regression Models | 1 |
| BIS 628 | Longitudinal and Multilevel Data Analysis | 1 |
| BIS 630 | Applied Survival Analysis | 1 |
| BIS 662 | Computational Statistics | 1 |
| BIS 691 | Theory of Generalized Linear Models | 1 |
| BIS 692 | Statistical Methods in Computational Biology | 1 |
| CB&B 555 | Unsupervised Learning for Big Data 1 | 1 |
| CB&B 567 | Topics in Deep Learning: Methods and Biomedical Applications 1 | 1 |
| CB&B 663 | Deep Learning Theory and Applications 1 | 1 |
| CDE 534 | Applied Analytic Methods in Epidemiology | 1 |
| CDE 566 | Causal Inference Methods in Public Health Research | 1 |
| CPSC 540 | Database Design and Implementation 1 | 1 |
| CPSC 546 | Data and Information Visualization 1 | 1 |
| CPSC 564 | Algorithms and their Societal Implications 1 | 1 |
| CPSC 577 | Natural Language Processing 1 | 1 |
| CPSC 581 | Introduction to Machine Learning 1 | 1 |
| CPSC 582 | Current Topics in Applied Machine Learning 1 | 1 |
| CPSC 583 | Deep Learning on Graph-Structured Data 1 | 1 |
| CPSC 670 | Topics in Natural Language Processing 1 | 1 |
| EMD 533 | Implementation Science | 1 |
| EMD 553 | Transmission Dynamic Models for Understanding Infectious Diseases | 1 |
| ENAS 544 | Fundamentals of Medical Imaging | 1 |
| EPH 510 | Health Policy and Health Care Systems | 1 |
| HPM 559 | Big Data, Privacy, and Public Health Ethics | 1 |
| HPM 560 | Health Economics and U.S. Health Policy | 1 |
| HPM 570 | Cost-Effectiveness Analysis and Decision-Making | 1 |
| HPM 573 | Advanced Topics in Modeling Health Care Decisions | 1 |
| IMED 625 | Principles of Clinical Research 1 | 1 |
| INP 560 | R Stats for Neuroscience | 1 |
| MGT 534 | Personal Leadership 2 | 4 |
| MGT 612 | Introduction to Social Entrepreneurship 2 | 4 |
| MGT 656 | Management of Software Development 2 | 4 |
| S&DS 517 | Applied Machine Learning and Causal Inference 1 | 1 |
| S&DS 530 | Data Exploration and Analysis 1 | 1 |
| S&DS 562 | Computational Tools for Data Science 1 | 1 |
| S&DS 563 | Multivariate Statistical Methods for the Social Sciences 1 | 1 |
| S&DS 565 | Introductory Machine Learning 1 | 1 |
| S&DS 583 | Time Series with R/Python 1 | 1 |
| S&DS 610 | Statistical Inference 1 | 1 |
| S&DS 663 | Computational Mathematics Situational Awareness and Survival Skills 1 | 1 |
| S&DS 664 | Information Theory 1 | 1 |
| 1 | Course offered in the Graduate School of Arts and Sciences. |
| 2 | Course offered in the School of Management. |
In addition, in the second year of the program, students are required to complete an independent capstone project (BIS 685/BIS 686) under the direction of a faculty member. This project may fall into one of the main areas—clinical informatics; clinical research informatics; population health informatics; and implementation of new methods and technology—and may include elements from several of these areas. Students are required to prepare a carefully written report and make an oral presentation of the work to the faculty and students. A capstone committee consisting of two faculty members and one outside reader will provide guidance to the candidate as to the suitability of the project and will monitor its progress.
Competencies
Upon receiving an M.S. in the Health Informatics concentration of Public Health, the student will be able to:
- Select informatics methods appropriate for a given public health context
- Compare the health information system structure and function across regional, national, and international settings
- Assess population informatics needs, assets, and capacities that affect communities’ health
- Propose strategies to identify stakeholders and build coalitions and partnerships for influencing public health informatics
- Communicate audience-appropriate public health content, both in writing and through oral presentation
- Apply systems thinking tools to a public health informatics issue