Lecture: 4 hours/week
or
Hybrid: 2 hours/week in class and 2 hours/week online
In this course, students engage in a variety of learning activities such as lectures, case study analysis, independent research, exercises, training on data classification technology, participant presentations, classroom discussions and guest speakers.
- Exploration of the potential advantages and disadvantages of AI in healthcare
- Examination of the AI decision framework and application to current and future trends in healthcare
- Examination of ethical issues pertaining to AI in healthcare including inclusivity, equity and accountability
- Examination of governance issues related to AI in healthcare
- Exploration of equitable access and application of AI in healthcare
- Exploration of the role of big data in the development of AI systems and application of data ethics principles and practices
- Exploration of methods to ensure AI is responsive and sustainable
At the end of the course, the successful student will be able to:
- demonstrate an understanding of AI and machine learning applications and foundations;
- apply AI to monitor health outcomes and trends in healthcare;
- apply big data analytics in healthcare;
- analyze the benefits and challenges of AI and machine learning;
- apply patient risk stratification strategies to assess clinical workflows;
- demonstrate an understanding of the integrated approach to hospital management and systems optimization using AI.
The course evaluation is consistent with the Douglas College Evaluation Policy. An evaluation schedule is presented at the beginning of the course. This is a graded course. All assignments must be completed to pass the course.
A list of required and optional textbooks, materials and electronic applications is provided for students at the beginning of each semester.