ArticleJMIR human factors2023
The Effects of a Health Care Chatbot's Complexity and Persona on User Trust, Perceived Usability, and Effectiveness: Mixed Methods Study.
Article in JMIR human factors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Usability and User Experience Assessment of Health Care Conversational Agents Using Validated Subjective Instruments: Systematic Review and Comparative Analysis.JMIR human factors · 2026Pooled it
- User and Provider Experiences With Health Education Chatbots: Qualitative Systematic Review.JMIR human factors · 2025Pooled it
- "But it sounded confident": the role of accuracy, tone, and disclaimers in users' medical decision-making.Frontiers in artificial intelligence · 2026Article
- A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study.JMIR human factors · 2024Article
- GPT-4 as a Source of Patient Information for Anterior Cervical Discectomy and Fusion: A Comparative Analysis Against Google Web Search.Global spine journal · 2024Review
- ChatGPT Virtual Assistant for Breast Reconstruction: Assessing Preferences for a Traditional Chatbot versus a Human AI VideoBot.Plastic and reconstructive surgery. Global open · 2024Article
- Ethical Implications of Chatbot Utilization in Nephrology.Journal of personalized medicine · 2023Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rising adoption of telehealth provides new opportunities for more effective and equitable health care information mediums. The ability of chatbots to provide a conversational, personal, and comprehendible avenue for learning about health care information make them a promising tool for addressing health care inequity as health care trends continue toward web-based and remote processes. Although chatbots have been studied in the health care domain for their efficacy for smoking cessation, diet recommendation, and other assistive applications, few studies have examined how specific design characteristics influence the effectiveness of chatbots in providing health information.
objectiveOur objective was to investigate the influence of different design considerations on the effectiveness of an educational health care chatbot.
methodsA 2×3 between-subjects study was performed with 2 independent variables: a chatbot's complexity of responses (eg, technical or nontechnical language) and the presented qualifications of the chatbot's persona (eg, doctor, nurse, or nursing student). Regression models were used to evaluate the impact of these variables on 3 outcome measures: effectiveness, usability, and trust. A qualitative transcript review was also done to review how participants engaged with the chatbot.
resultsAnalysis of 71 participants found that participants who received technical language responses were significantly more likely to be in the high effectiveness group, which had higher improvements in test scores (odds ratio [OR] 2.73, 95% CI 1.05-7.41; P=.04). Participants with higher health literacy (OR 2.04, 95% CI 1.11-4.00, P=.03) were significantly more likely to trust the chatbot. The participants engaged with the chatbot in a variety of ways, with some taking a conversational approach and others treating the chatbot more like a search engine.
conclusionsGiven their increasing popularity, it is vital that we consider how chatbots are designed and implemented. This study showed that factors such as chatbots' persona and language complexity are two design considerations that influence the ability of chatbots to successfully provide health care information.
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Registered trials
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