The Challenge
I was hired as an Instructional Design Specialist to work on a project titled “Accelerating the Adoption of Artificial Intelligence (AI) in Healthcare”. The project was a collaboration between the Michener Institute, the University Health Network, and the Vector Institute. It was funded by the Government of Canada’s Future Skills Centre and focused on preparing the healthcare community for AI-enabled care by transforming their mindset, skillset, and toolset through research projects, symposia, roundtables, certificate programs, and a mentorship component.
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As an Instructional Design Specialist, my main role was to lead the deliverables relating to certificate programs and a mentorship program. Secondary to this, I help with the engagement activities, including symposia and partnership round tables, and with the overall research component that aims to better understand what the healthcare community needs to accelerate AI.
The Solution
When I started my position, I connected with the research team to discuss their plans and timelines for the different research activities. They shared drafts of their scoping review and environmental scan that provided an overview of what AI programs currently exist for healthcare workers and what content and instructional strategies programs should include based on existing literature. They also shared their interview guides for instructors/program developers and students from partner AI-related educational programs that aimed to discover best practices and lessons learned from existing programs. They were open to my input and involvement in the interviews and data analysis so I could ensure program priorities were included and have a deeper understanding of their findings.
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I started summarizing the scooping review and environmental scan into design recommendations per topic and created a map of existing programs and their relation to our intended programming. I also tracked down notes from previous team project brainstorming sessions and received access to student feedback for the other AI-related courses offered at Michener. I then led additional team brainstorming sessions with updated needs assessment information and asked the research team about being involved in the interviews as it would help with a more agile and flexible approach to program planning.
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In my initial action plan draft, I had listed a target student survey or interview as a needs assessment activity. I asked the research team if we could include a similar one to better understand our target students’ needs. Although the team was interviewing students from partner programs, those programs were generally longer and at a higher level than the programs our team intended to develop. As well, the partner programs were all provided by organizations in Ontario. The project was intended to be a Canada-wide initiative and inclusive through the grant.
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In the initial brainstorming session notes, there were comments from patient partners expressing disappointment in their level of involvement with AI-related initiatives. Our project included two Patient Partners, and I thought it would be helpful to include patient voices in the research, and ultimately gain content for the programs. I was able to align the script for these interviews with my tentative program objectives and included a compassionate care focus to align with other research the team was completing.
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To organize and increase the utility of the information the research team was collecting and the team brainstorming sessions, I created a design guide that summarized student needs and recommendations for each AI-related topic the scoping review identified. That way, when we decided what topics would be included in each program, I could create design guides for each module of the programs. I included key findings and recommendations from a Royal College report that details findings and recommendations from a series of roundtables, stakeholder engagements, and a survey for residents and fellows in Canada. Recommendations from other literature sources were included, such as a survey of medical students in Ontario. I also used the report to start outlining educational resources and experiences we might need to be prepared to offer for the mentorship component.
References
Bilimoria, K., Harish, V., McCoy, L., Mehta, N., Morgado, F., Magaraj, S., Wang, C., and Zheng, J. (2019, February). Training for the future: Preparing medical students for the impact of Artificial Intelligence. Ontario Medical Students Association. https://omsa.ca/en/position-papers/preparing-medical-students-impact-artificial-intelligence
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Reznick, R. K., Harris, K., Horsley, T., & Hassani, M. S. (2020, February). Task force report on artificial intelligence and emerging digital technologies. Royal College of Physicians and Surgeons of Canada. https://www.royalcollege.ca/rcsite/health-policy/initiatives/ai-task-force-e
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McCradden, M. D., Baba, A., Saha, A., Ahmad, S., Boparai, K., Fadaiefard, P., & Cusimano, M. D. (2020). Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: A qualitative study. CMAJ open, 8(1). DOI:10.9778/cmajo.20190151
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McCradden, M. D., Sarker, T., & Paprica, P. A. (2020). Conditionally positive: A qualitative study of public perceptions about using health data for artificial intelligence research. BMJ open, 10(10). Doi:10.1136/bmjopen-2020-039798
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Wiljer, D., Salhia, M., Dolatabadi, E., Dhalla, A., Gillan, C., Al-Mouaswas, D., Jackson, E., Waldorf, J., Mattson, J., Clare, M., Lalani, N., Charow, R., Balakumar, S., Younus, S., Jeyakumar, T., Peteanu, W., & Tavares, W. (2021). Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach. JMIR Research Protocols, 10(10). https://doi.org/10.2196/30940
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Youssef, A., Wiljer, D., Mylopoulos, M., Maunder, R., & Sockalingam, S. (2020). “Caring About ME”: A pilot framework to understand patient-centered care experience in integrated care – a qualitative study. BMJ Open, 10. https://doi.org/10.1136/bmjopen-2019-034970