Digital Health & Data Analytics Curriculum

Semester 1

The Healthcare Ecosystem: Behind the Scenes

Digital health professionals operate within a complex, multi-faceted and ever evolving health ecosystem, that encompasses everything from the clinic to the legislature. Every decision can have immense impact.  We will take you behind the scenes of healthcare and you will expand your knowledge of how healthcare works, exploring several models with a keen focus on how Canada’s healthcare system provides care and supports digitally enabled systems in comparison to other systems. You will learn the fundamental vocabulary and concepts that are foundational to understanding the national and international healthcare landscape.

Key topics include:

  • Structure and governance of Canada’s healthcare system: local, provincial, and national
  • Comparative healthcare systems
  • Population health, including social determinants of health
  • Indigenous healthcare
  • Public and private organizations
  • Health economics: funding sources, key performance indicators, and digital tools

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Digital Health I: Empowering People

Digital Health I explores the role of e-health and digital health within the healthcare system. You will examine the critical importance of digital health and its emerging and dominant role in building a healthier world for all.  According to HIMSS, digital health connects and empowers people to manage health and wellness (HIMSS 2020) and through this course, you will understand the key concepts and the practical and pragmatic knowledge and experience required to work within digital healthcare ecosystems. You will acquire key knowledge, skills and attitudes critical for implementing and optimizing flexible and interoperable digital tools, technologies and services that empower patients, providers and the community to manage health and build a healthier world for all.

Key topics include:

  • Definitions and frameworks for digital health
  • Health Information Systems
  • Interoperability and Collaborative Tools
  • Patient Safety and Digital Health
  • Mobile Health and Applications
  • Transformative capabilities of Digital Health
  • TeleHealth and Virtual Care at the Webside
  • Consumer and Public Digital Health
  • Human Factors

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Data Science and Analytics I

Digital health professionals require high quality, timely, relevant, clean data to support effective clinical decision making and diagnostics. You will learn the fundamentals of effective data collection, data definitions, data cleaning, and management, using current technologies and data standard practices. The course will explore a variety of structured and unstructured data types, including both clinical and patient-provided that are required to improve health and personalize health care. You will also become familiar with the basics of databases, managing data and building modern and safe data storage systems. The course will introduce concepts related to big data, machine learning, and predictive analytics.  You will be able to put into practice essential concepts of data collection, management and processing including visualization, as well as effective data communication tools and techniques to support a data driven and data enabled healthcare environment.

Key topics include:

  • Introduction to data science and data analytics
  • Introduction to types of data: big, small and patient generated
  • Data Collection methods, tools and processes
  • Operational systems: Relational and non-relational databases
  • Standards used in defining data in the healthcare environment
  • Data Management best practices and real-life examples
  • Cloud computing and data science
  • Data Use: Visualization Tools and Practices
  • Data governance

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Semester 2

Digital Health II: Building Systems

Digital Health relies on dynamic, robust and adaptive systems to provide high quality care. You will build on the key fundamental knowledge from Digital Health I, examining the social, ethical, financial, and systems issues that shape the experience of digital health. Using contemporary case studies to highlight the potentials, perils, and pitfalls of digital health transformation, you will explore issues related to population health, privacy, cybersecurity, and governance. You will also examine issues related using digital health to address social and ethical issues, such as equity, diversity and access to care for all.  You will also examine issues related to building responsive and adaptive digital health systems that connect communities together for integrated and coordinated care. In addition, students will hear from a diverse selection of experts in health policy, research, administration, industry, and evaluation, and will be exposed to potential areas for future study, work placement, and capstone projects.
Key topics and case studies include:

  • Big Data and Population Health
  • Privacy, Cybersecurity and Governance
  • Inclusion, Diversity, Equity and Accessibility in Digital Health
  • Canada Health Infoway and eHealth Ontario: One Record to Rule Them All?
  • COVID-19 and the Digital Transformation
  • Community Care and the Digital Divide
  • Transforming Quality and Safety with Digital Image Peer Review
  • The Robot Will See You Shortly: Surgery at a Distance
  • AI in the NICU
  • Telementoring to Teach the World

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Artificial Intelligence and Machine Learning I

AI and Machine learning are rapidly changing every aspect and dimension of the health system and, in fact, some say, will change healthcare as we know it. You will explore the power and limits of artificial intelligence and will provide practical skills and experience with AI applications that are currently enhancing healthcare. Building from concepts around clinical decision making and decision support, you will work with a wide range AI applications from robots to Chatbots to machine learning techniques and data-driven tools that predict and change health and wellness. You will understand the evolution of AI and explore concepts and models required for learning how to apply AI. The course will also consider the social and economic implications of AI looking critically at bias, ethics, as well as the impact on diverse communities and the accessibility of healthcare for even the most vulnerable populations.

Key topics include:

  • Mathematical foundations 1 & 2
  • Data analysis
  • Learning methods
  • Computational models and training
  • Machine and deep learning
  • AI and personalized medicine and the EHR
  • AI bias, legislation and ethics
  • Putting AI into Practice and the future

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Data Science and Analytics II

You will continue to build and advance your knowledge around data analytics and data science by learning new concepts and working on a data science project. Through labs and practical projects working with relevant data and scenarios, you will rapidly advance your ability to put your knowledge of data analytics and science into practice. You will explore how data is used in clinical and healthcare operational decision making, data modelling and basic concepts of coding that will being to harness the immense amount of data produced along the health care journey. In particular, we will look at the impact of large data sets on fighting real problems such as health equities and the spread of pandemics such as COVID-19. You will work on practical case studies on how a data science project is structured, including how to frame a data science question within the content of clinical environment, sourcing available data, and applying mechanisms of translating a data science inquiry into a project. You will also acquire the knowledge how data science fits within the existing software development cycle and considerations how to productize an idea/project.

Key topics include:

  • Data Science Projects: Inquiry, Framing and Data Science Solution
  • Management of data: Data warehousing
  • Data Analysis: Purpose and Techniques around Data mining
  • Data Sources and Use: Risks and Limitations
  • Coding, modelling techniques and key vendor platforms
  • High level overview of a data science project cycle and role of data science

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Semester 3

Fundamentals of Implementation Science

Change and helping people change is incredibly difficult and this is particularity true in healthcare where it often takes and decade or more for good science and practice to reach our patients and our communities. Fortunately, there is a discipline to help us: implementation science. You will learn the fundamentals of implementation beginning with clearly identifying the problem, putting together a clear plan based on the best available evidence, managing the change with appropriate tool and processes, and working with key stakeholders to make that change a reality. Making the change is hard, but maintaining and sustaining change is even more challenging. You will acquire the skills to ensure that change is measured and sustained over a long period of time to ensure the best care is in practice.

Key topics include:

  • Understanding Critical and Wicked Problems
  • Engaging Stakeholders and Building Effective Relationships
  • Using Evidence to Inform Practice
  • Assessing the Environment
  • Managing Projects Effectively
  • Delivering on Time and on Budget

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Design Thinking and Quality Improvement

Designing change and continually improving that change on a human scale in healthcare is complex. There are practical and pragmatic approaches to achieving this change that work. You will learn to find solutions that are desired by your stakeholders, viable within the context that you are working in and feasible with the resources at hand. You will learn to deploy quickly, change continuously and evolve practice rapidly. You will learn key concepts, tools and processes related to design thinking, human factors and quality improvement, including human centred design and lean methodologies.

Key topics include:

  • Engaging and Involving Stakeholders in Framing the Problem
  • Inspiring through Clear Vision
  • Co-Creation and Co-Design with End Users
  • Human Factors and User-Centred Design
  • Generating Impactful Ideas
  • Rapid Prototyping for Progress
  • PDSA and Rapid testing cycles
  • Improving with Data
  • Sharing Success for Continuous Change

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Artificial Intelligence and Machine Learning II

You will learn advanced topics in artificial intelligence and machine learning, including successes and failures when implemented in clinical environments. You will learn how data, images and sounds are processed, along with their practical applications. Further, you gain an appreciation for the risks of AI and machine learning, such as if medical decision making is based solely on data that can be rife with inherent bias, or using data in a manner that does not adapt to new examples. Personalized medicine will be heavily reliant on data and good clinical judgment, but also on systems that are free of false data and that provide a transparent view on machine learning predictions. Students will learn the ethical considerations, risks, and major platforms in use by working on a data science and/or machine learning project.

Key topics include:

  • Supervised and unsupervised learning
  • Deep Learning – how it is and what it is used for
  • Text data, natural language processing and languages models (GPT-3 et al)
  • Computer vision and image processing (segmentation et al)
  • Audio processing (e.g., cough detection, sleep quality assessment, fall/motion alerts)
  • Risks & Limitations (overfitting, bias, transparency)
  • Examples from MI or partners (surgical, wound, radiology, etc.)
  • Ethics, privacy and “explainability”
  • Personalized medicine
  • Integration and usage of data from patient devices (smartwatches et al)

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Semester 4

Advanced Topics in Implementation Science

Making the change in a complex system like healthcare is hard, but maintaining and sustaining change is even more challenging. Building on the critical skills and knowledge you have acquired through Implementation Science, you will acquire the skills to ensure that change is measured and sustained over a long period of time to ensure the best care is realized in practice.

Key topics include:

  • Building Change and Programs to Last
  • Understanding the Logic and Theory of Change
  • Developing Key Performance Indicators
  • Assessing and Evaluating Change
  • Communicating the Impact of Change
  • Disseminating New Knowledge

This course will be delivered online using a combination of lectures, independent readings, and seminars.

Applied Project

You will develop a project that will showcase the advanced knowledge and skills that you have acquired during the program. Your project will form an invaluable piece of your professional portfolio that you can use to demonstrate to potential employers in the field that you are ready to be part of a high performing digital health team. You will focus on one of the primary program streams of artificial intelligence, machine learning, robotics, or data science. You will apply your skills in design thinking and implementation science to maximize potential impact to the healthcare system, while ensuring feasibility and facilitating robust evaluation.

Sample projects include:

  • Development of a dashboard to track patient and staff vaccination rates
  • Integration of data from various health information databases to support clinical decision making
  • Development of an evaluation strategy for a new technology, process, or policy
  • Application of machine learning to predict patterns in community disease spread
  • Development of an AI chat bot to support essential service triage

You will receive mentorship and feedback from experts in your chosen subject area.

Semester 5

Practicum I

You will be placed within a healthcare organization that has an active machine learning, artificial intelligence, robotics, or data science project. You will apply the knowledge, skills, and judgement that you have acquired through the program to date to embed yourself within a team of digital health professionals. This first of two practicums will be an opportunity for you to not only demonstrate what you have learned, but also to get feedback from industry professionals on how you can improve and eventually become employed in your chosen field.

If you are already employed at an organization that has a digital health project that you could contribute to, you may choose to complete your practicum at your workplace. We will work with your organization to ensure that you receive the challenge and feedback that you need to advance your skills.

This practicum will be taken full-time, 35 hours per week over 15 weeks.

Special Topics in Professional Development

During your practicum you will connect on a weekly basis with your Michener faculty and the rest of your class cohort who are in placements to reflect on your progress, set goals for the rest of the placement, and discuss topics relevant to professional growth in the field of AI, machine learning, data science and robotics. You will participate in a professional learning community that will help you constructively integrate the feedback you receive from your practicum leaders, as well as giving you an opportunity to refine your skills in teamwork and communication.

Finally, guest lectures from industry professionals will give you valuable insights into the emerging trends and workplace dynamics of your chosen field. Guest lectures will be tailored as much as possible to the development needs of your cohort.

Sample guest lecture topics include:

  • Workplace culture and the “hidden” skills of career success
  • Professional communication and relationship management
  • Leadership and followership
  • Conflict management
  • Emerging trends in AI, machine learning, data science and robotics
  • Equity, diversity and inclusion in the workplace

Semester 6

Practicum II

For your second practicum you will have the choice of either continuing with your first placement and becoming more central to the team’s projects, or switching focus and beginning a new placement. Regardless of your path, this practicum will be an opportunity to further develop your skills and demonstrate your readiness for employment in your chosen stream. You will continue to receive feedback from leaders on your placement team as you work together to refine your professional development goals.

As with the first practicum, if you are already employed at an organization that has a digital health project that you could contribute to, you may choose to complete your practicum at your workplace.

This practicum will be taken full-time, 35 hours per week over 15 weeks.

Special Topics in Professional Development (cont.)

During your practicum you will connect on a weekly basis with your Michener faculty and the rest of your class cohort who are in placements to reflect on your progress, set goals for the rest of the placement, and discuss topics relevant to professional growth in the field of AI, machine learning, data science and robotics. You will participate in a professional learning community that will help you constructively integrate the feedback you receive from your practicum leaders, as well as giving you an opportunity to refine your skills in teamwork and communication.

Finally, guest lectures from industry professionals will give you valuable insights into the emerging trends and workplace dynamics of your chosen field. Guest lectures will be tailored as much as possible to the development needs of your cohort.

Sample guest lecture topics include:

  • Workplace culture and the “hidden” skills of career success
  • Professional communication and relationship management
  • Leadership and followership
  • Conflict management
  • Emerging trends in AI, machine learning, data science and robotics
  • Equity, diversity and inclusion in the workplace

This course will be delivered online using a seminar format.