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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.

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.

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.

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.

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.

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.

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.