Patient Experiences with AI in Healthcare Settings

Authors

DOI:

https://doi.org/10.61838/kman.aitech.1.3.3

Abstract

This study aims to explore patient experiences with AI in healthcare settings, identifying their perceptions, concerns, and the perceived impact of AI on their care. The objective is to provide insights into patient attitudes towards AI, which can inform the development, implementation, and policy-making of AI technologies in healthcare. We conducted a qualitative study using semi-structured interviews with 26 participants who have interacted with AI-driven healthcare services. Participants were selected to represent a diverse range of ages, backgrounds, and experiences. Thematic analysis was employed to identify major themes and categories from the interview data, focusing on patient perceptions, experiences, and expectations of AI in healthcare. Five major themes emerged from the data: Understanding and Perceptions; Quality and Accessibility; Integration and Adaptation; Operational Challenges; and Patient Empowerment and Engagement. These themes encompassed various categories, including AI literacy, privacy concerns, care personalization, technical issues, and patient rights, among others. The findings highlight a general optimism about the potential of AI to improve healthcare outcomes, coupled with significant concerns about data privacy, ethical use, and the need for greater patient education and involvement in AI development. Patients perceive AI as a potentially transformative force in healthcare but underscore the importance of addressing ethical considerations, ensuring transparency, and enhancing patient engagement in the deployment of AI technologies. The study advocates for a patient-centered approach in the development and implementation of AI in healthcare to maximize its benefits while mitigating challenges.

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Published

2023-07-10

How to Cite

Hoseini, M. (2023). Patient Experiences with AI in Healthcare Settings. AI and Tech in Behavioral and Social Sciences, 1(3), 12-18. https://doi.org/10.61838/kman.aitech.1.3.3