Perspectives of Patients With Chronic Diseases on Future Acceptance of AI–Based Home Care Systems: Cross-Sectional Web-Based Survey Study
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Abstract
Background: Artificial intelligence (AI)–based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients’ motivation and intention to adopt such systems. Objective: This study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses. Methods: We conducted a cross-sectional web-based survey with 222 patients with chronic diseases based on a hypothetical scenario. Results: The results indicated that patients have an overall positive perception of AI-based home care systems. Their attitudes toward the technology, perceived usefulness, and comfortability were found to be significant factors encouraging adoption, with a clear understanding of accountability being a particularly influential factor in shaping patients’ attitudes toward their motivation to use these systems. However, privacy concerns persist as an indirect factor, affecting the perceived usefulness and comfortability, hence influencing patients’ attitudes. Conclusions: This study is one of the first to examine the motivation of patients with chronic diseases to adopt AI-based home care systems, offering practical insights for policy makers, care or technology providers, and patients. This understanding can facilitate effective policy formulation, product design, and informed patient decision-making, potentially improving the overall health status of patients with chronic diseases.
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In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole.
“In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole [39,42,52-55].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [55]
The Discussion’s Analysis of Results (page unavailable) reports that perceived usefulness influenced patients’ motivation to adopt AI-based home care systems. The Methods’ Accountability and Security section mentions concerns about reliability, but the paper does not report patients assessing output quality or accuracy before use, or questioning the value of AI as a whole. (LLM, conf=0.84)
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Barriers to and facilitators of artificial intelligence adoption in health care: scoping review
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- Authors
- Wang, B., Asan, O., & Mansouri, M.
- Affiliations
- Florida Polytechnic University (US)
- Stevens Institute of Technology (US)
- Corresponding author
- Asan, Onur
- Venue
- JMIR Human Factors
- Publisher
- JMIR Publications Inc.
- Published
- 2023-11-06
- Volume
- 10
- Pages
- e49788
- ISSN
- 2292-9495 (ISSN-L)
- PMC
- PMC10660233
- OpenAlex
- W4387359174
- Cited by
- 23
- References
- 50
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 4
- FWCI
- 5.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Human Factors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Social Sciences › Demography › Technology Use by Older Adults
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, home care system, chronic disease, technology adoption, patient motivation, perceived usefulness, comfort, privacy concerns, accountability, patient attitudes, acceptance of AI
- MeSH terms
- Artificial Intelligence, Chronic Disease, Cross-Sectional Studies, Home Care Services, Humans, Internet
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