Artificial Intelligence in Health Care Certificate Program

Artificial Intelligence in Health Care Certificate Program:
at a glance

Credential(s) Earned

Certificate of Achievement - Successfully complete all 4 courses

Program Length

15 months

Start Date

February 2021

Tuition

$5,900 total (domestic students). Please see below for international tuition.

Application Deadline

December 24, 2020

Program Code

AI

Description

Due to the technological revolution nowadays, more people expect new technologies to replace older ones to make processes more efficient and to reduce errors. Artificial Intelligence’s (AI) primary aim in a health-related environment is to provide clinical decision and diagnostic support by analyzing relationships between treatment options and patient outcomes. AI has also been developed for patient monitoring and care, drug development and disease prevention. This AI certificate program will introduce students to the discipline of AI and how it is applied in the healthcare environment. Students will acquire data science and analytic skills, learn how to implement AI solutions and participate in creating an AI solution.

Have questions about the Artificial Intelligence in Health Care Certificate Program? Check our Frequently Asked Questions page!

Why AI in Health Care?

Artificial Intelligence‘s (AI) primary aim in a health-related environment is to analyze the relationships between treatment or prevention techniques and the patients overall outcome and provide information to improve clinical decisions and diagnosis. AI has been developed and applied to practices such as patient monitoring and care, drug development, treatment protocols and even diagnosis processes. Since AI is being incorporated into health care delivery, there is a need for professionals that have clinical knowledge and AI knowledge to support AI enabled health care systems. AI solutions require users to know how AI works so that applications run optimally and provide accurate recommendations and predictions.

Who is this Program Intended for?

This program is intended for all certified/regulated health care providers, this includes all disciplines and managers/leaders. IT professionals interested in getting into health care as well, health care informatics professionals.

Have a question? Email ce@michener.ca

Program Overview

The primary aim of AI in healthcare is to assist healthcare providers by providing them the information needed to make decisions. AI:

  • Identifies disease management and prevention guidelines
  • Provides relationships between treatment and outcomes
  • Prioritizes patients in need of care
  • Compiles relevant information and presents it to clinicians
  • Highlights observations of interest and assists healthcare providers when making a diagnosis
  • Generates preliminary reports

Program Layout

This program consists of 4 required courses.

Course 1 - AI Fundamentals

This mandatory course will focus on introducing students to the discipline of artificial intelligence and will lay the groundwork for understanding AI. It will start with a brief history and discuss concepts and models required for learning how to apply AI. Specific references will be made to the health care environment.

Topics that will be covered:

  • 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
  • AI and Legislation and Ethics
  • AI Limitations and the Future

Course Timeline: February 4, 2020 – April 24, 2020

Course Delivery: Online – Facilitated 

Course 2 - AI and Data Science

This mandatory course will focus on data science and data analytics skills needed to work with health data. It will familiarize students with databases, working with data and managing data. Statistical concepts related to AI will also be covered.

Topics that will be covered:

  • Introduction to Data Science and it’s Relation to AI
  • Operational Systems: Relational and Non-Relational Databases
  • Analytical Systems: Data Warehousing
  • Analytical Systems: Data Mining
  • Data Sources in health Care Environment
  • Standards used in Defining Data in the Health Care Environment
  • Managing Data
  • Cloud Computing and Data Science
  • Data Governance

Course Timeline: May 4, 2020 – July 24, 2020

Course Delivery: Online – Facilitated 

Course Prerequisites: Must successfully complete Course 1: AI Fundamentals 

Design your pathway (Choose one of the following):

Course 3a - AI and Developing in Health Care Environment

This course will introduce students to developing using Python. It will familiarize students with developmental concepts and highlight the challenges encountered when developing for the healthcare environment.

Topics that will be covered:

  • Developing using Python (8 weeks)
  • Python for AI
  • Issues with Developing 1 & 2 in a Health Care Environment

Course Timeline: September 8, 2020 – November 27, 2020

Course Delivery: Online – Facilitated 

Course Prerequisites: Must successfully complete Course 1: AI Fundamentals & Course 2: AI and Data Science

Course 3b - Managing AI Implementations

This course will focus on managing AI implementations in the healthcare environment. Students will explore the benefits of AI, identify opportunities where AI can be employed and develop an understanding of how to design solutions that will be adapted by healthcare providers, patients and other stakeholders.

Topics that will be covered:

  • Ways by which AI Improves Productivity
  • Identifying Opportunities for Applying AI
  • Product and Development Management
  • Lessons from early EHR Implementations
  • Human Factors Engineering
  • Patient Care in a Digitally enabled Environment
  • Validating, Piloting and Scaling Up Solutions
  • Preparing for Implementing AI Solutions and Challenges
  • Technological Solutions and Tools for Implementing AI Applications
  • Jobs and Skills in a AI enabled World
  • Preparing your Staff for AI Tools

Course Timeline: September 8, 2020 – November 27, 2020

Course Delivery: Online – Facilitated 

Course Prerequisites: Must successfully complete Course 1: AI Fundamentals & Course 2: AI and Data Science

Final Project:

Course 4 - AI Project

The fourth mandatory course (fifth total) will tie everything together and highlight what is required to successfully complete a project. Students will gather requirements and develop AI enabled solutions. Topics include how to identify and document requirements, develop solutions, test and then implement them using JIRA.

Course Timeline: TBD

Course Delivery: TBD

Course Prerequisites: Must successfully complete Course 1, Course 2 and Course 3a or 3b.

 

Instructors and Reviewers

Faculty Names and Bios 

Instructors:

Name

Bio

Hamid Semeralul

 

 

Hamid Semeralul received his undergraduate degree in Electrical Engineering from the University of Ottawa and graduate degree in Mechanical Engineering from Ontario Tech University. His research topics during graduate studies included optimizing the quality of novel manufacturing systems – such as 3D printed medical implants – and using data science and AI tools to explore massive amounts of data to solve production problems.

 

His university teaching experience includes teaching Business Math, Statistics, and Business Forecasting Techniques courses at Ontario Tech University. These courses included teaching AI topics such as algebra, calculus, probability, regression, classification, clustering, Artificial Neural Networks (ANN) and other related topics.

 

Hamid’s experience includes working as a consultant where he championed the creation of Management Information Systems (MIS) that helped manage a nuclear facility that produces isotopes used for medical scans and diagnosis. His tasks included automating documents mining activities (including structured and unstructured data) to extract intelligence, loading various data into databases and producing visual and written reports for clients and regulators. Hamid also worked as a team lead managing modification projects at a nuclear power facility.

Shaimaa Ali

 

 

Shaimaa is an assistant professor at the department of electrical and computer engineering at the university of western Ontario.

Shaimaa’s passion for data science started while studying her double major undergraduate degree in computer science and statistics. She then pursued a master’s degree specializing in data mining, in which she created an outlier detection algorithm. While working on her master’s degree she worked as a software developer on various projects and experienced the significant effort and resources required to debug software first hand, so she pursued a PhD in which she created a data mining technique for software debugging.

After completing her PhD, Shaimaa thrived as a data-scientist in the department of Physics and Astronomy where she helped astronomers extract knowledge and better understand the history of galaxy formation from massive amounts of simulation data. Shaimaa also worked on a big-data development project at the department of Pathology and Laboratory Medicine, in which her role was to scale a medical image processing system to be able to handle large amounts of images of blood samples.

Almas Naseem

 

 

Almas Naseem completed her masters of science from Trent University in 2007.  Her MSc thesis topic was: Forced and mixed convection heat transfer from an unsteady shear-flow past a circular cylinder. She then completed her PhD from the University of Western Ontario in 2013. The focus of her PhD studies was Financial Mathematics and her thesis topic was: Analysis of Re-advanceable Mortgages.

During her MSc and PhD studies Almas provided teaching assistantship for courses which include Applied Statistics, Calculus for engineering, Algebra and Matlab lab.

Justus Lam

 

 

Justus is a Senior Application Developer with the Joint Department of Medical Imaging at the University Health Network. He graduated with a bachelor’s of Software Engineering from the University of Western Ontario. Justus began his career by working in the steel industry writing software to interface with 3D point-cloud scanning lasers. For the past 5 years he has worked at UHN developing RIS and PACS software, specializing in custom HL7 interfacing, DICOM communication and UI design.
Huy Tran

 

 

Huy is an Application Developer with the University Health Network’s informatics team.  He started out studying Neuroscience and Physiology at the University of Toronto then went on to pursue a postgraduate degree in the field of Neuroscience researching Learning and Memory.  After landing a job in the telecommunications industry, he acquired a Computer Science education at Sheridan College.  Since then, he has applied his computer science knowledge and experience to the healthcare industry taking on software projects that include, work on a voice recognition module for radiologist and medical practice management software for healthcare practitioners.
Pran Piru

 

 

Pran Piru is an experienced Business Systems Analyst with a demonstrated history working in the healthcare and insurance industries. He is skilled in a variety of BSA activities including Waterfall/Agile methodology, Manual Testing, Business Analysis and Stakeholder Engagement. He began his career at eHealth Ontario where he was responsible for vetting and improving documentation by refining their SharePoint information system. He graduated with a degree in Commerce and specialized in Business Systems.
Stan Loi Stanley Loi is an accomplished Business Systems Analyst possessing a wide variety of skills, experiences and in-depth knowledge of business processes and technologies used in healthcare settings. He began his career in the healthcare industry supporting a variety of end users and hospital information systems such as RIS and PACS. He designed and improved workflows as well as managed custom HL7 and DICOM interfaces for hospitals and clinics of all sizes across Canada and the United States during his time at Agfa Healthcare.
Phillip Chong

 

 

Phillip has over 15 years of software testing experience with a wide variety of applications and companies in health care, security and portfolio management.  His educational background includes a diploma in Computer Programming and an Honours Bachelor Degree in Mathematics.

 

 

Reviewers:

Name

Bio

Ethe Luo

 

 

Ethe has over 4 years of experience as a Health Informatics Engineering Analyst at Artificial Intelligence in Medicine Inc., where she develops AI solutions for cancer registries and hospitals. She holds a BSc in Life Sciences and Biotechnology, a MSc in Health Informatics and is currently working on another Master degree in Computer Sciences.
Rachel Ho

 

 

 

Rachel currently works for Inspirata as a project manager, implementing AI solutions for data extraction using Natural Language Processing (NLP) in Canada, United States and Europe. She has 10 years of clinical experience as an Medical Radiation Technologist (MRT) and was involved in an Electronic Medical Record (EMR) implementation. She has software solutions interface and implementation knowledge and continues to be fascinated by the power of data and its ability to provide healthcare professionals with the knowledge they need to make informed decisions.
Marina MacPherson

 

 

 

Marina is a registered medical radiation and imaging technologist, and works in a large community hospital in Toronto.  She has a BSc in Biology from McMaster University, and has completed graduate level studies in Gerontology at the University of Toronto.  She holds a certificate in Adult Education from Seneca College.  Her career has spanned multiple imaging modalities, including General Radiography, Mammography, CT and Interventional Radiography.  She currently works as a senior PACS analyst, and is involved in a number of enterprise-wide Imaging projects.

Marina has implemented an imaging-focused analytics program at the Imaging Department level, and has collaborated with data scientists in the development of an AI tool which facilitates the detection of pneumothorax.  She is interested in patient-centric approaches to sharing large imaging data sets, and how emerging AI algorithms will impact both healthcare practitioners and patients.

 

What are the Admission Requirements?

Admission requirements to the Certificate Program are:

  1. Regulated and non-regulated healthcare providers – all disciplines and including leaders/managers (college or university graduates); or
  2. IT professionals interested in getting into healthcare (college or university graduates); or
  3. Healthcare informatics professionals (college or university graduates)

Please Note: Applicants who do not meet the above requirements but who have current, relevant work experience in an appropriate fields will be assessed on an individual basis. Submission of two letters of support will be required; one from a direct supervisor and one from your human resources department. A current resume describing your background and experience will also be required.

Students must have access to a computer that meets the minimum system requirements, access to the Internet and an e-mail account.

Students must meet Michener’s English Language Requirements. Please view the English Language Requirements for further information. If you require an English language assessment, please review our English Language Assessment page for specific requirements.

If you are in international applicant, please click here.

How do I Apply?

Please submit the following to Michener:

Please submit your application to:

Office of the Registrar
The Michener Institute
222 St. Patrick Street
Toronto, ON M5T 1V4
Canada

Application Process for International Applicants

All International students (on a study permit/work permit in Canada, or residing overseas) have a separate application process. Please apply directly to The Michener Institute. For more information please refer to the International Admissions page.

Tuition

Tuition is payable on a course-by-course basis and includes some program materials, tutor support and evaluation.

Course Tuition
Timelines
AI Fundamentals (AINT111) $1475 Feb 2020 – April 2020
AI and Data Science (AINT120) $1475 May 2020 – July 2020
AI and Developing in Health Care Environments (AINT130)  OR Managing AI Implementations (AINT131) $1475 Sept 2020 – Nov 2020
AI Final Project (AINT140) $1475 TBD

*Prices are subject to change

The tuition for International Students is $7,670 total ($1,918 per course). Please visit the International Tuition Fees page for more information.