Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care

BMC Health Services Research

Research

Samira Adus¹  Jillian Macklin²  Andrew Pinto³ 

[1] Faculty of Medicine, University of Toronto, Toronto, ON, Canada;Faculty of Medicine, University of Toronto, Toronto, ON, Canada;Dalla Lana School of Public Health, Institute of Health Policy, Management, and Evaluation, Toronto, ON, Canada;Faculty of Medicine, University of Toronto, Toronto, ON, Canada;Dalla Lana School of Public Health, Institute of Health Policy, Management, and Evaluation, Toronto, ON, Canada;MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada;

关键词: Patient engagement;    Artificial intelligence;    Qualitative research;    Patient perspective;    Application development;   

DOI  :  10.1186/s12913-023-10098-2

 received in 2023-05-23, accepted in 2023-10-01,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundArtificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients’ needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care.MethodsWe conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis.ResultsWe found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development.ConclusionGiven the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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