Certificate in Health Informatics

The Certificate in Health Informatics aims to train students in the world of health informatics, giving you the skills to develop and use information technology to create a better healthcare system

Health informaticians work in an interdisciplinary environment incorporating computer science, medicine, health, nursing and business knowledge to improve healthcare and health outcomes.

Program overview

Term 1 2 foundation courses
1 elective or certificate course
Term 2 1 required course
2 elective or certificate courses
Term 3 DGIN 7000: Internship (internship students)
DGIN 9000: Master’s Thesis (thesis students) 
Term 4  DGIN 5001: Capstone (internship students)
DGIN 5002: Research Methods (thesis students)
2 elective or certificate courses

Customize your degree

Although all MDI students follow a required program outline, you can create your own degree with a wide variety of elective options that support the Certificate in Health Informatics.

Two of the following courses:

Assigned by the graduate committee based on academic background and goals.

DGIN 5100 Foundations in Web Technologies

This hands-on course examines the technologies and infrastructure required to support digital innovation.  The course examines the major components of the information technology infrastructure, such as networks, databases and data warehouses, electronic payment, security, and human-computer interfaces.  The course covers key web concepts and skills for designing, creating and maintaining websites, such as Grid Theory, HTML5, CSS, JavaScript, AJAX theory, PHP, SQL and NoSQL databases.  Other principles such as Web Accessibility, Usability and User eXperience, as well as best security practices, are explored in detail through a combination of lectures, in-class examples, individual lab work and assignments, and a final group project.

DGIN 5200 Foundations in Business

The overall aim of this course is to develop a high-level understanding of the dynamics of innovation, the distribution and outcomes of the strategic management of innovation and the relationships that are important in developing high-impact organizations. 

DGIN 5300 Law, Policy, and Ethics in Emerging Technologies

Emerging technologies—such as digital media, the “internet of things”, artificial intelligence (AI), and financial tech—are playing an increasingly central role in how individuals live and interact with each other; how businesses innovate and create new opportunities; and how governments function and serve their populations. But the unrestrained development and use of these technologies can raise complex legal, policy, and ethical challenges. This course offers students an introduction to foundational legal, policy, and ethical issues raised by emerging technologies in a variety of contexts, with special consideration for digital innovation and commerce. On completion, students will be able to better identify, understand, and critically assess these issues and also more effectively manage and resolve them in the course of the professional pursuits.

DGIN 5400 Statistics for Health Informatics

This course covers essential statistical methods for medical research. Topics include descriptive analysis techniques and basic principles of statistical inference for comparison of means, proportions and investigation of relationships between variables using regression mod-eling techniques. Students will also become familiar with nonparametric tests and power and sample size calculations.

The following core course:

DGIN 5201 Digital Transformation

This core digital innovation course focuses on the design and management of digital innovation projects for both public sector and private sector organizations. Specifically, this course provides students with knowledge and skills to initiate and execute digital innovation and transformation projects in existing organizations or new start-ups.

One of the following core courses:

DGIN 5001 – Capstone in Digital Innovation

This course requires students to apply principles of Digital Innovation (DI) holistically to a concrete problem. In the context of a multidisciplinary team, students are expected to apply Di processes, develop negotiation and collaborative skills.

DGIN 5002 – Research Methods

This class will provide Master of Digital Innovation thesis students with an understanding of the principles of empirical science as they relate to computer science related research. The goal is for the student to determine the research methods most appropriate for their research area and to be able to design simple to moderately complicated research studies. The course covers both quantitative and qualitative research issues and will provide a practical introduction to the statistics through hand-on tutorials. In addition, this course will provide the basis for critical reading of research findings in the literature and students will gain experience with scientific writing. This course will teach students how to assess the validity of other researchers’ articles, and at the same time, enable students to validate their own research.

All health informatics students 
complete the following electives:

HINF 6101 Health Information Flow and Use

This course tracks the flow and use of health information in relation to population and individual health needs, including its generation, collection, movement, storage and use in various settings. The course includes a discussion of health and health information, and of the measurement of health and health services processes.

HINF 6110 Health Information Systems and Issues

A course about health infostructures and their strengths and weaknesses. Students will learn about how such structures operate, the issues they generate, their impact on the health of populations and their impact on the flow and use of information. Particular attention will be paid to ethical and practical health informatics issues.

HINF 6230 Knowledge Management for Health Informatics

The goal of this course is to characterize healthcare knowledge and to examine the technical issues related to the development and deployment of knowledge management solutions for managing healthcare knowledge to support three main activities: Clinical decision support, practitioner and patient education, and health administration. At the conclusion of the course, students will be able to (a) identify the presence (or lack) of healthcare knowledge within a healthcare enterprise; (b) capture it using various knowledge representation formalisms; and (c) utilize it via new or existing knowledge management infrastructures to impact the delivery the healthcare.


Your choice of two elective courses from the following:

HINF 6020 Research Methods

This course explores the logic and principles of research design, measurement, and data collection. The course offers a range of methodological issues and methods, including experimental and quasi-experimental designs, survey research and sampling, measurement, and qualitative methods.

HINF 6102 Health Information Standards and Use

This seminar course discusses technical and philosophical issues related to the capture and use of information. Issues include nomenclature; the reliability and accuracy of coding schema; interoperability; and, ISO/CEN, HL7 and Infoway standards development. Student projects will track the flow and use of information for hospital, community and public health purposes.

HINF 6210 Databases and Data Mining for Health Informatics

Health organizations collect massive amount of data to support clinical decision-making, outcome measurement, policy setting, administration and research. This course provides a conceptual understanding of various data mining algorithms and introduces healthcare-related data mining strategies to facilitate the mining of real-life healthcare data to provide data-driven healthcare decision-support services.

DGIN 5401 Operationalized Machine Learning in Healthcare

This course provides a broad overview of machine learning and machine learning operations in healthcare contexts. We begin by studying how healthcare data is unique, and how machine learning methods have been applied to clinical and medical tasks. We focus on various graphical, deep learning, time-series, and transfer learning models and unique aspects of their application in healthcare. We cover concepts of fairness, privacy, trust, explainability, and other human factors. We discuss implementation techniques, including ‘MLOps’ for healthcare, and opportunities for real-world deployment. Much of the course will be seminar-based, including guest lectures and descriptions of research papers. Students will choose and complete a commensurate research project. The course expects and requires a familiarity with programming and core concepts in data mining or data science. It is strongly recommended that Master of Digital Innovation students take this in their final semester.