Course

Artificial Intelligence in Healthcare

Faculty
Health Sciences
Department
Health Information Management
Course code
HIMP 4510
Credits
3.00
Semester length
15 Weeks
Max class size
35
Method(s) of instruction
Hybrid
Lecture
Course designation
None
Industry designation
None
Typically offered
To be determined

Overview

Course description
Students will begin to understand and critique applications of artificial intelligence (AI) in healthcare. In addition, students will gain knowledge of the AI framework and how this aligns with health data management principles and practices.
Course content
  • 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
Learning activities

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.  

 

Means of assessment

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.

Learning outcomes

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.
Textbook materials

A list of required and optional textbooks, materials and electronic applications is provided for students at the beginning of each semester.

Requisites

Prerequisites

HIMP 4430 and HIMP 4440 

and

(CSIS 3290 

or

BUSN 3630)

Students in the BScHIM program are required to maintain a passing grade of 65% (C+) in all courses in order to progress in the program.

Corequisites

HIMP 4502

Equivalencies

No equivalent courses.

Course Guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.

(Current)

Course Transfers

These are for current course guidelines only. For a full list of archived courses please see https://www.bctransferguide.ca

Institution Transfer details for HIMP 4510
There are no applicable transfer credits for this course.

Course Offerings

Winter 2025