CUPE Local 3902 (Unit 3) Job Posting Sessional Lecturer Position

Posting Date: September 5, 2024

Program: Masters of Health Informatics (MHI)

Sessional dates of appointment: Winter 2025, January – April

Course title: MHI2002H: Emergent Topics in Health Informatics: Intelligent Medicine, Machine Learning and Knowledge Representation

Course Description:

This course is designed for students to understand the issues associated with the use of data management technology and analytics solutions in the healthcare system. This includes systems and technologies used to generate, harvest and store clinical data and methods used to create predictive models (including but not limited to methods associated with machine learning). Furthermore, issues related to delivery of predictive analytics and implementation of algorithms in care settings along with clinical, business and ethical challenges will be explored. In addition, an overview of the issues within the health industry that are driving the use of data, will be reviewed, including population health management, clinical decision support, and advanced research. The goal is for students to be able to gain experience in the description, architecture and implementation planning of data infrastructure in the healthcare system along with providing a strong foundation in regard to analytics lifecycle and methods.

Objectives:

Students will enhance abilities to:

• Describe and conceptualize data infrastructure used in the healthcare system, including classical and non-classical sources of data and the technologies and methods used to harvest and store clinical data.
• Utilize statistical and machine learning tools to create and validate predictive models and present analytics results using visualization tools.
• Identify and problem-solve the organizational, clinical and ethical implementation challenges associated with predictive algorithms in healthcare.
• Gain the ability to position data management and advanced analytics in the context of health system challenges and business models.

Qualifications:

• A PhD or Masters level education with recent experience in clinical and health informatics, preferably in the areas of AI and ML design, development and implementation.
• Knowledge and experience with AI governance; use of generative AI.
• Experience with ICT adoption, implementation, and evaluation;
• A robust understanding of clinical/clinician work processes, as influenced by health informatics and related technology;
• Past teaching experience related to health informatics, preferably at the graduate level;
• Prior experience in curriculum development and adult teaching-learning methods;
• Comfortable with electronic teaching tools such as Learning Management Systems (e.g., Blackboard), PowerPoint, as well as on-line collaboration tools (Blogs, Wikkis, Discussion Boards, Webinars, or Video-conferencing).

Class schedule: Weekly

Estimated enrolment: 60

Estimated TA support: based on enrolment – None

Duties:

• Course instructor for a professional graduate course using competency-based learning and assessment methods.
• Responsible for course design and assessment of student outcomes. Must be accessible to students outside of classroom hours.

Salary: Commensurate with experience

How to submit an application: please send your CV and cover letter via e-mail to ihpme.mhi.program@utoronto.ca and ihpme.appointments@utoronto.ca.

Closing date: September 25, 2024

This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.

It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.

Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.

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