TDNet Discover

The quantitative study aimed to examine the relationship between exposure to health-related artificial intelligence (AI) technologies, engagement with digital health technologies, and patient trust and satisfaction among U.S. adults. The research addressed the critical issue of low patient trust in AI-enabled healthcare, which poses barriers to the effective integration and utilization of AI technologies in healthcare settings. The study utilized data from the Health Information National Trends Survey (HINTS), a nationally representative sample of adult patients in the United States. The research employed descriptive analysis, one-way ANOVA, and independent samples t-tests to investigate how different levels of exposure to AI technologies and engagement with digital health tools impact patient perceptions of online health information, satisfaction with healthcare providers, and health information-seeking behaviors. Data analysis involved secondary analysis of the HINTS dataset, focusing on variables related to AI exposure, digital health engagement, trust in online health information, patient satisfaction, and confidence in health self-management. The statistical analyses identified significant associations between levels of AI exposure and trust in online health information, as well as between digital health engagement and patient satisfaction scores. Additionally, the study examined the relationship between confidence in health self-management and frequency of internet use for health information seeking. The findings provide empirical evidence to guide healthcare providers, policymakers, and AI developers in implementing AI technologies that align with patient expectations and values. The results highlight the importance of responsible AI implementation strategies and effective patient education to build trust and improve outcomes in the era of AI-enabled healthcare. Recommendations for further research include exploring the long-term effects of AI exposure and digital health engagement on patient outcomes and investigating the potential impact of AI on healthcare disparities across diverse patient populations.