AI in Practice for Nurses Weekend Mini-Course (Deadline: Jan. 17, 2027)

T-CAIREM's **AI in Practice for Nurses Weekend Mini-Course** offers practical, hands-on exposure to the tools, opportunities, and implications of AI in your nursing practice. You’ll leave with strategies to improve productivity and insights into the role AI is playing in your specialty. No coding experience is required. This is not a course designed to develop an AI tool. Instead, it focuses on current practical applications of AI in healthcare and their clinical implications.

**COURSE TIMES & DATES:** 10am to 1:30pm ET on January 23 & 24, 2027

**FORMAT:** Virtual (Zoom link will be provided to registered participants)

**COST:** CAD $650 + tax

**REGISTRATION DEADLINE:** January 17, 2027 at 11:30pm ET

NOTE: A certificate of course completion will be provided for participants who successfully complete the final quiz.

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**About the Instructors**

• **Dr. Winnie Sun, RN, PhD**
• **Associate Professor, Faculty of Health Sciences, Ontario Tech University**

Dr. Winnie Sun is an Associate Professor at the Faculty of Health Sciences in Ontario Tech University, and she is holding a research appointment as the Co-Research Director of the Advancement for Dementia Care Centre (ADCC), partnership between Ontario Tech University and Ontario Shores Centre for Mental Health Sciences in Canada. She was awarded a Post-Doctoral Fellowship from AGE-WELL (Canada’s Technology and Aging Network) to support her training and development of expertise in technology co-design, ethics, accessibility, usability test, evaluation and implementation, with focus on assistive technology for older adults. In 2024, Dr. Sun received the Research Excellence Chair Award in Healthy Aging and Dementia Care, where she is leading the development of technology-enabled innovations using virtual reality (VR) and artificial intelligence (AI) to promote aging in place and brain health. In 2025, Dr. Sun became as Co-Principal Scientific Directors to help shape the future of aging through the Sienna Senior Living Research Fund, to co-create the establishment of Sienna Senior Living Research Centre for Healthy Aging and Happiness, with the aim of advancing research and innovation in healthy aging. Additionally, she recently received TD Ready Commitment Funds to advance dementia care through technological innovations using virtual reality (VR) and artificial intelligence (AI). This initiative focuses on the goal of improving early brain health interventions and leveraging advanced technologies including the use of multi-sensory VR and AI-powered innovations to drive better health outcomes.

**Dr. Nihal Haque, MD, FRCPC**
**Chief Medical Information Officer and Geriatrician, Scarborough Health Network**

Dr. Nihal Haque is the Chief Medical Information Officer (CMIO) and a practicing geriatrician at Scarborough Health Network. He is also an Assistant Professor at the Temerty Faculty of Medicine. He serves as a faculty affiliate with the T-CAIREM education committee on AI in medical education. He has obtained certification in Artificial Intelligence (AI) in Healthcare from the Michener Institute and Harvard T.H. Chan School of Public Health. He is actively involved in AI‑driven research and innovation, with ongoing projects that include AI applications in delirium care, AI chatbots for medical resident training and AI-assisted discharge summary creation for complex, frail older inpatients. Dr. Haque has also provided strategic guidance to AI start‑ups, advising them on privacy and regulatory pathways, research design, clinical validation and commercialization. He was also part of an interdisciplinary academic team that received federal funding from Canada Health Infoway to develop AI solutions to reduce burnout among healthcare workers. He also teaches about practical applications of AI to healthcare executives through the Rotman School at the University of Toronto. He is currently a fellow in the AMS-Fitzgerald Fellowship in AI & Human-Centred Leadership at the Dalla Lana School of Public Health.

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**Course Outline**

**Day 1: January 23 • 10am to 11:30am ET**

• Introduction to AI (Algorithms, how AI is different from a non-AI computer program, what machine learning is, AI capabilities and limitations).

• Introduction to generative AI and Large Language Models (LLM): Why are they in the news, and how can they be used practically for day-to-day healthcare applications?

• Q&A group discussion about practical, real-life examples and scenarios.

**Break: 11:30am to 12:00pm ET**

**Day 1: 12:00pm to 1:30pm ET**

• Discuss the types of AI algorithms to know for healthcare practice and terminology needed to understand medical AI.

• Discuss CNN, predictive algorithms, classification algorithms, supervised versus unsupervised learning, and model training metrics.

• Q&A group discussion with peers and the instructor.

**Day 2 • January 24 • 10am to 11:30 am ET**

• Medicolegal/data privacy session: Medicolegal risks when using AI in nursing practice, including discussion of current College of Nurses guidelines; critical evaluation of AI bias, equity, reliability, accuracy and safety; impact of AI in nursing care delivery for quality and outcomes.

• Data privacy session: Privacy and professional considerations when using AI in nursing practice and clinical decision support, including trustworthiness and ethical considerations of AI tools.

• Q&A group discussion.

**Break: 11:30 am to 12:00 noon ET**

**Day 2: 12:00 pm to 1:30 pm ET**

• How do I evaluate an AI product for use in my practice? Approach to implementing AI in your clinical practice, focusing on data privacy considerations, how to make a proper consent form for AI applications, how to get an AI product approved for use and strategies to support interprofessional collaboration in AI innovations.

• How can I incorporate AI into my day-to-day practice - review of examples of applications ready to use now (AI scribes, AI chatbots, AI apps for CME, AI apps for finding healthcare information).

• What resources can I use to stay up to date on healthcare AI from a nursing perspective? Barriers and facilitators to AI adoption; Advancing an interprofessional approach to AI integrated care for clinical decision support and person-centred care.

• Q&A group discussion: Self-reflection: Reflection on AI understanding, identification of areas and needs for further learning, action plan for collaborating and integrating AI tools into practice.

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• **Learning Objectives**

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After active engagement in this program, participants will be able to understand and evaluate an AI technology for incorporation into their day-to-day practice through the following learning objectives:

1. To describe the fundamental concepts of artificial intelligence, including the distinction between AI and traditional computer programs, and explain the practical applications of generative AI and Large Language Models (LLMs) in clinical practice.

1. To identify key AI algorithms relevant to nursing practice and how they are used to solve day-to-day clinical problems.

1. To analyze the ethical issues associated with AI use in clinical practice, incorporating College of Nurses of Ontario and data privacy guidelines.

1. To develop a structured approach to assessing AI tools for clinical use in a hospital setting, including considerations for data privacy, patient consent, and workflow integration, while identifying readily available AI applications that can enhance day-to-day nursing practice.

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**Mapping of Nursing Competencies (College of Nurses of Ontario)**

**For Objective #1 and 2**

**Clinician Role:** Registered nurses are clinicians who provide safe, competent, ethical, compassionate, and evidence-informed care across the lifespan in response to client needs. Registered nurses integrate knowledge, skills, judgment and professional values from nursing and other diverse sources into their practice.

**Communicator Role: **Registered nurses are communicators who use a variety of strategies and relevant technologies to create and maintain professional relationships, share information, and foster therapeutic environments.

**For Objectives #3 and 4**

**Clinician Role: **Registered nurses are clinicians who provide safe, competent, ethical, compassionate, and evidence-informed care across the lifespan in response to client needs. Registered nurses integrate knowledge, skills, judgment and professional values from nursing and other diverse sources into their practice.

**Professional Role: **Registered nurses are professionals who are committed to the health and well-being of clients. Registered nurses uphold the profession’s practice standards and ethics and are accountable to the public and the profession.