The EMHI Project Course is designed to provide students with experience in identifying important problems in the health care system or their workplace and to design a project to solve that problem. Students learn about strategic thinking, goal setting, planning, and pitching their ideas to key stakeholders and funders. Students also learn how to develop a business case to ensure that their project will be financially sustainable. Students then apply the learnings from their course work to engage stakeholders in co-designing a solution to the problem they identified earlier. At the end of the course, the students generate recommendations for how organizations or the health care system can solve the problem using a digital approach that meets the needs of all stakeholders. At the end of the program, students are expected to be able to develop projects independently and oversee project managers who will run those projects.

This Project Extension supports students' ongoing learning and contribution at employer sites. The course is designed to build on work and reflection to date, as achieved via the MHI2015Y HI Project course. Note: there is one final evaluation due from supervisor/employer, and no interim report is required. There are no group workshops for this course; however, students are invited to communicate with Julia Zarb, course instructor, via live or phone meetings. Ideally, students will set one meeting with the instructor for at least 30 minutes over the course of the term.

There are numerous ways in which information technology can be used in any particular setting, with very different results. IT can be used to reduce costs and improve efficiency simply by taking advantage of the power of automation. But the increasingly diverse capabilities of IT systems can also stimulate innovative rethinking of business processes, reorganizing, and simplifying work relationships and roles.

Even more radically, strategic use of IT can lead to transformations in entire industries, changing the rules and business models within which customers, suppliers, partners, and other stakeholders operate. In the information systems world, the systems analyst acts as the intermediary between technical system developers on the one hand, and business managers and users on the other. Techniques have been developed to enable them to analyze business situations and communicate requirements to technical developers. With the rapidly changing role of IT in today's organizations, there is also need to rethink the methods and techniques used in systems analysis. This course will cover conventional systems analysis methods as well as recent developments. Modelling approaches considered will include process modelling, data modelling, object modelling, strategic modelling, and value network modelling. Strengths and limitations of various techniques will be examined.

Objectives: at the end of this course, students will be able to: 1) Describe and explain the activities and contexts of systems analysis. 2) Describe the changing nature of systems analysis, where information systems can be used to achieve varying degrees of change to existing processes. 3) Approach an organization to study its activities and processes from the perspective of systems analysis. 4) Map processes using modelling techniques for analysis. 5) Analyze the processes and data in an organization, and to explore alternative options for redesigning or improving processes, taking advantage of information technology systems. 6) Use modelling techniques to explore more fundamental changes, including those involving reconfigurations of relationships among stakeholders inside and outside the organization. 7) Discuss the strengths and limitations of various techniques for systems analysis.

Health informatics professionals are increasingly called upon to help manage knowledge in organizations, beyond conventional information processing. A wide range of information technologies, such as collaboration and social software, enterprise repositories, knowledge-based or expert systems, software agents, as well as traditional information systems, are being used to support work in organizations. This course examines knowledge management from a health system perspective. Notions of knowledge in the management literature and in the information systems area are reviewed. Modelling techniques that can be used during systems analysis in the context of organizational knowledge management are examined.

The course aims to expose students to the issues of knowledge management in health organization and across health systems, and to provide opportunities to learn and apply modelling and analytical techniques to understand the use of various types of information technologies in meeting organizational knowledge management needs.

Objectives: at the end of this course, students will be able to: 1) Analyze and identify knowledge management needs in health settings. 2) Apply modeling techniques to analyze organizational processes from a knowledge management perspective as well as information systems perspective. 3) Analyze and identify potential IT systems solutions to address knowledge management needs. 4) Explain and illustrate potential application of ontologies in the context of knowledge management. 5) Describe and explain knowledge management concepts in relation to the application of information technologies in the health system. 6) Apply an integrated framework to analyze knowledge management across policy, interoperability, and technology domains. 7) Identify key stakeholders in the system and describe their unique and common knowledge management needs.

Information systems permeate seemingly all aspects of both work and play. One of the greatest drawbacks in the use of such technologies, however, is poor technological literacy among system owners and users alike: a tendency to unbridled enthusiasm for what information systems can do, but without concomitant reflection on their limitations, and critical implications of how they are designed and why they are used. This course will orient students to fundamental perspectives necessary for sound technical judgement about the place of information and communication technologies in contemporary society. A balance of theory and practical perspectives is sought. On the theoretical side, three interrelated themes are developed: the structure of information systems, the design of information systems, and the social implications of information systems. The practical side is developed through assignments (data modelling, information systems assessment, and systems development planning).

Learner objectives: students will develop an understanding of how information systems work, to be able to appreciate their capabilities and limitations. Theoretical objectives: 1) Know the origins and evolution of IS. 2) Understand the function and structure of networks and databases. 3) Describe systems development methods. 4) Discuss how to measure IS quality. 5) Appreciate multiple ethical issues in the deployment of IS, both in and out of workplaces. 6) Articulate the challenges and limitations of electronic support of group activities. Practical objectives: 1) Demonstrate data modelling skills in constructing entity-relationship diagrams and data flow diagrams. 2) Describe how to systematically evaluate an existing information system. 3) Demonstrate an ability to author a Request for Proposals document. 4) Participate meaningfully in the planning process for an IS design and implementation.

This course will explore the art (practical exercise of leadership) with the science (contemporary theory and concepts of leadership) of leadership of digital health transformation. The course is divided into four sections:

1) The Foundations of Leadership (Introduction Session) — explores various models and theories about the skills, competencies, and mindsets of leadership and how "management" differs from "leadership." Focus will be on leadership frameworks that are values and influence-based and relevant to digital health transformation.

2) The Leadership of Change (3 sessions) — explores the evolution of change theory in health and some contemporary change models that are used in the IT/Digital Health and health fields. We'll also look at the "change cycle" and what leadership practices can be brought to bear to create successful change projects. We will explore the theory and root cause of common failures of digital health change projects. Examples from health will be examined (e.g., the intersectionality of leadership across the various health sectors). A group change/transformation project and paper will form the basis of the culmination of learning for this section to give learners practical insight in applying theory to leading change/transformation in the field.

3) Beyond Change: Leading Crises, Innovation and Disruptive Change (2 sessions) — explores crisis and resilience leadership and models for disruptive change. Examines how leadership can enable innovation and transformational change. Examples from health informatics, including COVID-19, and health system recovery will be examined.

4) Culmination of Learning (2 sessions) — Translating Learning about Leadership Into One's Personal Practice: Learners enter into a final phase of self-reflection on key learnings from the course and create a personal leadership development plan. Proven leadership self assessment tools including the Leadership Practice Inventory, resilience and influencer tests will inform the self-reflection process. Learners will also interview a digital health leader in the field to examine how these leadership competencies manifest in practice within digital health and receive the reflections of a career coach in digital health leadership. Peer and small group dialogue as well as the final Self Reflection Paper will embed learning into one's leadership practice now and in future.

Learner objectives: upon successful completion of this course, students will be able to integrate leadership concepts into their personal leadership practice in the field of digital health transformation: 1) Indicate their leadership development needs and construct a set of actions that will improve their ability to lead and practice leadership in the field of digital health. 2) Exhibit insight and self-awareness for their own leadership practice. 3) As a leader of digital health transformation, learners will be able to: describe their vision of the future and demonstrate their ability to inspire others to a common vision, particularly in times of crisis and change. 4) To recognize opportunities to challenge the status quo and improve system performance. 5) Understand the conditions and climate in which people are willing to innovate and bring about disruptive change. 6) To identify leadership actions that will allow others within a team to trust, collaborate, and work as a team toward results. 7) To give constructive feedback, recognize, and appreciate the accomplishments of others in ways that are meaningful to them. 8) To listen actively to diverse points of view and lead with empathy. 9) To be able to articulate a vision for change, to plan a change project, and monitor achievement of progress toward the future.

Health care remains a top policy priority in Canada and a key defining characteristic of Canadian identity. Under Canada's universal, publicly funded health insurance plan (Medicare), all Canadians have access to medically necessary hospital and doctor care regardless of the ability to pay. Yet, like health systems across the industrialized world, Canada's faces growing challenges. An aging and increasingly diverse population, global pandemics, emerging and more costly medical technologies and drugs, and rising public expectations about timely access to care, put additional demands on already stretched health care resources. The site of care is shifting as more care moves out of hospitals and into home and community, as well as online. Individuals and communities are demanding a greater role in decision-making and greater choice in where and how they receive care. There are increasing pressures to harmonize domestic health care policies with global "benchmarks" and to take advantage of the potential for digital technologies to transform care. In spite of billions of new health care dollars, public concerns about wait times for non‐emergency care continue to fuel debate about health system sustainability and the need for private pay care options.

This course will develop and apply a policy analysis "tool kit" to critically analyze key issues and trends in Canada's health care system and digital health policy, with a particular focus on understanding the ways in which digital technologies can help to address long-standing Canadian health system challenges and how individuals both within and outside government can shape this future. Course sections examine the current state of health care in Canada, the public-private mix, the influence of powerful interest groups, and the determinants of health, paying particular attention to the ideas, interests, and institutions which have shaped the Canadian health care system in the past and which now shape its future. This graduate course is designed for health professionals and students of health policy who need to "make sense" of and meaningfully influence a rapidly changing and increasingly politicized health care environment in which "evidence" is often only one factor driving the pace and direction of change.

Learner objectives: upon successful completion of this course, students will be able to: 1) Identify major elements of Canada's health care system. 2) Explain current digital health policy issues and trends in Canada and internationally. 3) Apply a conceptual policy analysis tool kit to "make sense" of a volatile digital health policy environment. 4) Better understand how to navigate and shape the digital health policy landscape. 5) Write short, concise briefing notes which synthesize academic articles, policy papers, and reports as the basis for evaluating and recommending policy options. 6) Value the need for a policy analytic approach.

The course will be run in seminar format with a maximum of 12 students. Students are expected to come prepared to actively participate in class sessions, based on readings and pre-recorded lectures.

Data are increasingly being collected digitally throughout our health care system. New sources and uses of data could help improve patient outcomes, lower health system costs, and improve health care provider productivity. They also present challenges: 1) New types of data, (e.g., from DNA testing) may increase risk to privacy. 2) Modern data analytics methods commonly link multiple datasets, sometimes from multiple jurisdictions, to achieve sample sizes appropriate for analytics needs. This creates challenges with data security and data flows (due to legislative restrictions). Further, data science approaches to analyses challenge current data minimization principles. 3) Many entities would like to monetize health data or use them for commercial purposes. The public are uneasy over this. Commercial interests must be balanced with public interests. 4) Governments and payers want access to data to develop better policies and allocate resources more effectively. 5) Health care organizations will need to share data more frequently to provide care to complex patients whose needs cannot be met by a single entity. 6) Patients are increasingly asking for access to their data for personal use. 7) Current policies and mechanisms for data governance struggle to meet these challenges, as there are no simple solutions.

The fundamental theme of this course is how the health care system can optimize and leverage the information collected to meet our evolving need for data to be used (and re-used) by and across organizations while meeting our legal and privacy obligations. We will consider both theoretical/conceptual and operational aspects of data quality, responsible use of data, and outcomes assessment using real-world applications. We will also provide case examples of initiatives leading the way in this new data environment. The course will identify emerging frameworks and technological solutions for solving key data governance issues.

Variational principles and Lagrange's Equations, Hamilton's principle. Kinematics of rigid body motion, Euler angles, rigid body equations of motion. Hamilton's equations, cyclic coordinates, Legendre transformations. Canonical transformations, Hamilton-Jacobi theory.

Multi-degree of freedom systems, using both analytical and approximate methods. Vibrations of continuous systems, including strings, bars and membranes. Natural modes of plate vibration — approximate methods such as Rayleigh's Energy Methods, Rayleigh-Ritz Method, Galerkin's Method, and assumed mode method. Introduction to finite element analysis.

The purpose of the course is to introduce the theory and practical application of acoustics noise and vibration control. While the emphasis of the study will be on the built environment, both indoor and outdoor, the methods taught can also apply to other industries, e.g. the automotive industry. Both the physics and perception of sound will be discussed covering such wide ranging topics as concert hall design, speech intelligibility, HVAC noise control design, and building isolation from rail noise, to name a few. The course combines theoretical introductions to the subjects of acoustics, noise and vibration and follows them up with case studies from industry.

This course will provide students with practical knowledge on sensor network design including sensor selection, calibration, digitization, and digital signal processing. Students will be introduced to theory and operation of various sensor technologies and their applications. Commonly used transducers such as chemical, mechanical, and magnetic as well as the more advanced organic and nuclear transducers are discussed. This course will also cover linear and non-linear multi-parameter calibration. Digitization, and a survey of digital signal processing techniques will be discussed with practical application of commonly used digital filters. Special focus will be placed on optimal design of sensor networks and multi-sensor data fusion. There will be a design project to enforce the lessons learned in class on sensor calibration and digital signal processing.

Linear systems and signal sampling, Fourier transforms and frequency analysis, Laplace transforms, FFT and inverse FFT algorithms, convolution/de-convolution, impulse response, random signals, noise characterization, auto- and cross-correlation, power spectra, adaptive filters, detection and clustering. These topics will be covered with extensive coverage on their applications to various topics in mechanical or biomedical engineering. In mechanical engineering such topics include vibrations, signal timing, spectral/phase analysis, signature analysis, thermal waves, acoustic emission, engine performance analysis, resonant acoustic spectroscopy (RAS), crack detection and location with ultrasound, flow measurements, condition-based monitoring and maintenance, fracture mechanics, etc. In biomedical engineering these topics include modeling of biomedical control systems, analysis of evoked potentials, analysis of electroencephalograms and electrocardiograms.

The main purpose of this course is to introduce a series of distinct topics in control to students who have not seen control system design beyond a first course in control, which includes classical methods such as root locus, and Bode design, for example. The topics discussed in this course are selected to give students a broad overview of a variety of control design methods and concepts in stability.

This course introduces the design of intelligent robots- focusing on the principles and algorithms needed for robots to function in real world environments with people. Topics that will be covered include autonomy, social and rational intelligence, multi-modal sensing, biologically inspired and anthropomorphic robots, and human-robot interaction. Class discussions will centre on the interactive, personal assistive and service robotics fields.

AI-embedded Robotics: applications in Service and Personal Robots. Development of a prototype home-assistance robot. Applications of the robot in the home environment.

This course builds on the concepts of AI Applications in Robotics I.

This course will cover the development of AI-Embedded Robotics with Applications to Home and Institutional Care. Image Processing for Object Detection and Identification; Convoluted; Neural Networks (CNN); Computer Vision (CV); Artificial Neural Networks (ANN); Autonomous Navigation; Mapping, Localization, Motion Planning; Collision Avoidance; Grasping and Manipulation; Imitation Teaching/Learning; Reinforcement Learning (RL); Policy Improvement with Path Integrals (PI2); Control; Model-Free Control; Advanced Control via DMP and Potential Functions; Closed Loop Control; Applications: Projects: Dressing, Bathing, Ironing, Laundry folding, Sewing, Other.

This course provides students with knowledge on healthcare robotics including surgical, assistive, and rehabilitation robots. Specific topics include medical imaging-guided surgery; minimally invasive surgery through miniaturization, novel actuation and sensing; robotic surgery at tissue and cell levels; autonomous robotic systems to assist with daily living activities; multi-modal robot interfaces; robotics-based rehabilitation technologies; upper limb rehabilitation robots; wearable exoskeletons and sensors; implanted neural interfaces. Students are provided with state-of-the-art advances in healthcare robotics.

A course in which the postulatory approach is used to develop the theory of thermodynamics. The postulates are stated in terms of a variational principle that allows them to be applied to systems subjected to fields, to phase transitions, and to systems in which surface effects are dominant. The thermodynamic stability of systems is examined and examples of stable, metastable, and unstable systems are discussed.

In this course you will learn about the phenomena that control phase change of pure substances. Most of the course will be devoted to studying liquid-vapour phase change, with an emphasis on boiling. We will study the thermodynamics of phase change, vapour bubble nucleation and growth, heat transfer during boiling, and fluid mechanics during the flow of a liquid-vapour mixture. All students are expected to have done undergraduate courses in thermodynamics, fluid mechanics, and heat transfer.

This course covers the basic principles of how global energy is currently supplied, by primary source. The aim is to provide an energy literacy that can inform research, technology development, and effective policy in this area. The course content will be roughly divided according to the current global energy mix (i.e., 31% oil, 27% coal, 25% gas, 6.9% hydro, 4.3% nuclear, 2.5% wind, 1.4% solar, and 1.8% geothermal/biomass/biofuels). In each case background reading and critical analyses will be applied to: a) the characteristics of the resource; b) the infrastructure for extraction/development of the resource; c) the usage of the resulting energy; and d) the implications of usage. Assignments and exams will assess both background knowledge and the ability to apply fluid flow, thermodynamic, and heat transfer analyses to energy supply systems.

This course will deal with the basic theory of combustion in the steady state, with consideration of theories of flame propagation, flame stabilization, limits of inflammability, ignition, quenching, etc., and discussion will include both laminar and premixed flames, diffusion flames, flames and detonation.

The primary emphasis of the course is materials properties relevant for some clean energy conversion technologies. More specifically, some materials such as inorganic solids and semi-conductors that play key roles in clean electricity production technologies such as fuel cells, gas turbines, and solar cells will be the primary focus, with their ionic and electronic conduction mechanisms and their relevance being the major part of the technical content of the course. That information will be combined with some overview-level information of a few different technologies on a broad level.

A first course in nuclear reactor theory, which introduces students to the scientific principles of nuclear fission chain reactions and lays a foundation for the application of these principles to the nuclear design and analysis of reactor cores. Topics covered include basic nuclear concepts, atomic fission, neutron propagation and interaction with matter, neutron thermalization, diffusion model of a nuclear reactor, criticality, nuclear reactor kinetics, and reactivity effects.