Clinical and Translational Research: Digital Health Innovation (Ph.D.)
Offered by: Medicine (Faculty of Medicine and Health Sciences)
Degree: Doctor of Philosophy
Program Description
The PhD in Clinical and Translational Research; Digital Health Innovation focuses on advanced concepts in clinical epidemiology, medical artificial intelligence (AI), clinical innovation, and applied data science, including the use and generation of digitized health and social data using specialized software. Fundamentals of current AI applications in medicine, methods to employ big data in clinical tool development, mathematical principals underpinning digital health and big data, and design thinking methodology in clinical innovation. Three areas of focus include: i) software and service innovation, ii) device innovation, and iii) innovation using digital data. The thesis must focus on digital health innovation.
Required Courses (6-9 credits)1
| Course | Title | Credits |
|---|---|---|
| EXMD 601 | Real World Applications of Data Science and Informatics. | 3 |
Real World Applications of Data Science and Informatics. Terms offered: Winter 2027 Training in practical applications of health care data science. | ||
| EXMD 634 | Quantitative Research Methods. | 3 |
Quantitative Research Methods. Terms offered: Fall 2026 Topics covered include: 1) An overview of common research designs based on examples from research currently undertaken in the Division of Experimental Medicine; 2) Types of data arising from these designs; 3) Basic methods for data analysis; and 4) Application of these methods to student research projects. | ||
| EXMD 701 | Comprehensive Oral Examination. | 0 |
Comprehensive Oral Examination. Terms offered: Fall 2026, Winter 2027 An examination that must be passed by all doctoral candidates in order to continue in the doctoral program. | ||
PhD 1 students must also take:
| Course | Title | Credits |
|---|---|---|
| EXMD 650 | Health Informatics | 3 |
Health Informatics Terms offered: Fall 2026 Basic concepts in bio and health informatics, health informatics architecture and standards, ethics, health information infrastructure and management of health information systems, electronic health record systems, patient and consumer-oriented health information systems, telehealth, clinical decision-support systems, computers in health education, clinical research informatics and frameworks for evaluation. | ||
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6 credits if admitted as PhD 2, 9 credits if admitted as PhD 1.
Complementary Courses (19-21 credits)
3 credits from the following list of methods courses:
| Course | Title | Credits |
|---|---|---|
| EXMD 510 | Bioanalytical Separation Methods. | 3 |
Bioanalytical Separation Methods. Terms offered: Fall 2026 The student will be taught the capabilities and limitations of modern separation methods (gas and high-performance liquid chromatography, capillary electrophoresis, hyphenated techniques). Application of these techniques to solve analytical problems relevant to biomedical research will be emphasized, with special attention being paid to the processing of biological samples. | ||
| EXMD 521 | Computational Methods Single-Cell Analytics. | 3 |
Computational Methods Single-Cell Analytics. Terms offered: Fall 2026 Computational methods (particularly machine learning methods) for various single-cell data analysis, modelling, and visualization tasks that could drive novel biomedical discoveries, focusing on “real-world” biomedical applications. | ||
| EXMD 600 | Principles of Clinical Research. | 3 |
Principles of Clinical Research. Terms offered: Fall 2026 Foundations for conducting clinical research including the principles underlying clinical studies, an overview of key methods in clinical research and the critical interpretation of peer-reviewed literature. | ||
| EXMD 602 | Techniques in Molecular Genetics. | 3 |
Techniques in Molecular Genetics. Terms offered: Winter 2027 Precise description of available methods in molecular genetics, and rationales for choosing particular techniques to answer questions posed in research proposals for targeting genes in the mammalian genome. Emphasis placed on analysis of regulation of gene expression and mapping, strategies for gene cloning. Course divided between lectures and student seminars. | ||
| EXMD 604 | Recent Advances in Cellular and Molecular Biology 1. | 3 |
Recent Advances in Cellular and Molecular Biology 1. Terms offered: Fall 2026 Recent aspects of cell and molecular biology, including cellular organelle structure and function, molecular genetics, gene expression regulation, DNA replication and protein trafficking. | ||
| EXMD 605 | Recent Advances in Cellular and Molecular Biology 2. | 3 |
Recent Advances in Cellular and Molecular Biology 2. Terms offered: Winter 2027 Recent aspects of cell and molecular biology, includin signal transduction, cell growth and development, and immunology. | ||
| EXMD 609 | Cellular Methods in Medical Research. | 3 |
Cellular Methods in Medical Research. Terms offered: Fall 2026 Different cellular methods used in biomedical research, including spectroscopic, microscopic and immunological techniques as well as statistics. Lectures, some demonstrations by faculty as well as short seminars given by the students. | ||
| EXMD 610 | Molecular Methods in Medical Research. | 3 |
Molecular Methods in Medical Research. Terms offered: Winter 2027 Different molecular methods used in biomedical research, including chromatography, purification and analysis of proteins and nucleic acids, various techniques in molecular biology, transgenic technology, and stem cells. Lectures, some demonstrations, and short seminars given by the students. | ||
| EXMD 616 | Molecular and Cell Biology Topics. | 3 |
Molecular and Cell Biology Topics. Terms offered: Fall 2026 Structured and instructor-directed student presentations and discussions of recent advances in molecular and cellular biology. The course will reinforce the students' knowledge of currently major areas of investigation, with a focus on human disease and medical applications. Important recent publications will extend material from textbook and review articles. | ||
16-18 credits from the following list of complementary courses2, organized by specific areas of focus:
Software and Service Innovation
| Course | Title | Credits |
|---|---|---|
| EXMD 630 | Developing Digital Innovations for Health Impact. | 3 |
Developing Digital Innovations for Health Impact. Terms offered: Winter 2027 Advanced innovative thinking, knowledge and skills that will aid in the development of innovative digital health solutions. It will integrate mind mapping strategies, design thinking, and usability evaluations that form the core of developing and refining an innovative digital health solution aimed to solve a well-defined clinical/public health problem. | ||
| EXMD 674 | Health Technology Assessment | 3 |
Health Technology Assessment Terms offered: Winter 2027 Introduction to health technology assessment, including its purpose, producers, components, and impact. | ||
| FMED 612 | Evaluation Research and Implementation Science. | 1 |
Evaluation Research and Implementation Science. Terms offered: this course is not currently offered. An introduction of how to critically appraise available evidence to become familiar with methods specific to intervention implementation and program evaluation in the context of family medicine practice with an opportunity to develop an evaluation protocol for a specific program that responds to the expectations of clinicians-managers in primary care. | ||
Device Innovation
| Course | Title | Credits |
|---|---|---|
| BMDE 503 | Biomedical Instrumentation. | 3 |
Biomedical Instrumentation. Terms offered: Fall 2026 The principles and practice of making biological measurements in the laboratory, including theory of linear systems, data sampling, computer interfaces and electronic circuit design. | ||
| BMDE 508 | Introduction to Micro and Nano-Bioengineering. | 3 |
Introduction to Micro and Nano-Bioengineering. Terms offered: Fall 2026 The micro and nanotechnologies that drive and support the miniaturization and parallelization of techniques for life sciences research, including different inventions, designs and engineering approaches that lead to new tools and methods for the life sciences - while transforming them - and help advance our knowledge of life. | ||
| BMDE 517 | Electromagnetic Technologies for Biomedicine | 3 |
Electromagnetic Technologies for Biomedicine Terms offered: Fall 2026 Introduction into concepts and characteristics of electromagnetic (EM) fields interacting with the body, from theory through practice. Electromagnetic properties of biological tissues, safety considerations, electrode- and antenna-based measurements, and existing and emerging medical technologies. | ||
| BMDE 655 | Biomedical Clinical Trials - Medical Devices. | 3 |
Biomedical Clinical Trials - Medical Devices. Terms offered: Winter 2027 This course will train biomedical engineers to understand the clinical and business aspects of transferring a medical device idea into a commercial product. This course provides an overview of the pre‐clinical and clinical testing of medical devices, clinical trials, reimbursement systems, market analysis, sales models, and business models, as pertaining to medical devices. This course will also cover the design of randomized trials, including statistical principles, hypothesis postulating, bias minimization, and randomization methods. | ||
Innovation Using Digital Data
| Course | Title | Credits |
|---|---|---|
| BMDE 520 | Machine Learning for Biomedical Data. | 3 |
Machine Learning for Biomedical Data. Terms offered: Fall 2026 Theoretical and practical course in machine learning applied to the expanding richness of biomedical data, including multidimensional biomedical measurements centring on high-resolution body imaging and whole-genome common variant genetics. | ||
| COMP 550 | Natural Language Processing. | 3 |
Natural Language Processing. Terms offered: Winter 2027 An introduction to the computational modelling of natural language, including algorithms, formalisms, and applications. Computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. Selected applications such as automatic summarization, machine translation, and speech processing. Machine learning techniques for natural language processing. | ||
| COMP 551 | Applied Machine Learning. | 4 |
Applied Machine Learning. Terms offered: Fall 2026, Winter 2027 Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems. | ||
| ECSE 551 | Machine Learning for Engineers. | 4 |
Machine Learning for Engineers. Terms offered: Fall 2026, Winter 2027 Introduction to machine learning: challenges and fundamental concepts. Supervised learning: Regression and Classification. Unsupervised learning. Curse of dimensionality: dimension reduction and feature selection. Error estimation and empirical validation. Emphasis on good methods and practices for deployment of real systems. | ||
| EXMD 651 | Statistical Models for Health Research. | 3 |
Statistical Models for Health Research. Terms offered: this course is not currently offered. Introduction to the concepts necessary for construction of statistical models relevant to clinical research, biomedical research and other areas of health research. Modern methods and software for estimating the parameters of these models and for drawing inferences and predictions from them. The models will be applied to data drawn from health research studies. The value and limitations of statistical models vs. machine learning models. | ||
| EXSU 500 | Fundamentals of AI in Medicine | 3 |
Fundamentals of AI in Medicine Terms offered: Fall 2026 Introduction to artificial intelligence (AI) applied to issues in medical diagnosis, therapy selection and learning from health data. Various AI methods, electronic medical records, and ethical/security concerns. Machine learning approaches including deep learning and reinforcement learning without delving too deeply into the technical details. | ||
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Other courses at the 500 level or higher may also be selected in consultation and with approval of the program director.