ArticleDigital health
Patient-facing chatbots: Enhancing healthcare accessibility while navigating digital literacy challenges and isolation risks-a mixed-methods study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Engagement with a Chatbot-based Intervention for the delivery of mailed at-home COVID-19 Testing: a Descriptive Log Analysis Study from the SCALE-UP II Trial.medRxiv : the preprint server for health sciences · 2026Article
- Role-based evaluation of artificial intelligence chatbot responses in orthodontic emergency scenarios: accuracy, readability, understandability, and patient-oriented communication.BMC oral health · 2026Article
- Article
- Prevalence of patients with limited health literacy in rotator cuff tears and their experiences with care: a mixed-methods study.JSES international · 2026Article
- Comparative Performance of agentic AI and Physicians in Taking Clinical History across Leading Large Language Models (LLMs).medRxiv : the preprint server for health sciences · 2026Article
- Mental health chatbots and their technical features: A systematic review of reviews and a thematic analysis.Global mental health (Cambridge, England) · 2026Review
- What patients want from healthcare chatbots: insights from a mixed-methods study.Journal of the American Medical Informatics Association : JAMIA · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Digital communication between patients and healthcare teams is increasing. Most patients find this effective, yet many patients remain digitally isolated, a social determinant of health. This study investigates patient attitudes toward healthcare's newest digital assistant, the chatbot, and perceptions regarding healthcare access. Methods: We conducted a mixed methods study among patient users of a large healthcare system's chatbot integrated within an electronic health record. We purposively oversampled by race and ethnicity to survey 617/3089 (response rate 20%) patient users online using de novo and validated items. In addition, we conducted semi-structured interviews with users (n = 46) purposively sampled based on diversity, age, or select survey responses between November 2022 and May 2024. Results: In surveys, 213/609 (35.0%) felt they could not understand the chatbot completely, and 376/614 (61.2%) felt the chatbot did not completely understand them. Of 238 users who felt completely understood by the chatbot, 178 (74.8%) believed the chatbot was intended to help them access healthcare; in comparison, of 376 users who felt not completely understood, 155 (41%) believed the chatbot was intended to help access ( Conclusion: Patients' experience with the chatbot appears to affect their perception of the intent of the chatbot's implementation; those adept at chatbot communication or within historically less trusting groups may prefer a quick, non-judgmental answer to questions via the chatbot rather than human interaction. Although our findings are limited to one health system's existing chatbot users, as patient-facing chatbots expand, attention to these factors can support healthcare systems' efforts to design chatbots that meet the unique communication needs of all patients, expressly those at risk of digital isolation.
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