ArticleJournal of the American Medical Informatics Association : JAMIA2025
What patients want from healthcare chatbots: insights from a mixed-methods study.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- [Artificial intelligence and disinformation in health: The need for re-education from primary care].Atencion primaria · 2026Review
- AI Privacy and Security in Healthcare: A Systematic Literature Review.International journal of telerehabilitation · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectivesTo understand whether patients prefer chatbots for certain tasks in healthcare, and their motivations for doing so, recognizing that chatbots are already assisting patients with various healthcare tasks. MATERIALS AND
methodsWe conducted a mixed-methods study with patient-users of a healthcare system multi-task chatbot integrated in an electronic health record. We purposively oversampled by race or ethnicity to survey 617/3089 (response rate, 20.0%) chatbot users using de novo and validated survey items. We conducted semi-structured interviews with 46 patient-users and 2 chatbot developers between November 2022 and May 2024. We used modified grounded theory to analyze interviews, descriptive statistics and Chi-square tests to compare survey results, and mixed-methods techniques to integrate findings.
resultsPatient-users preferred chatbots for administrative tasks to save providers' time, because of the chatbot availability, and to avoid unpleasant interactions. Some preferred to discuss sensitive tasks (such as mental health or gender-affirming care) with chatbots due to more privacy or anonymity and less embarrassment or judgment. Developer interviews corroborated this finding. Avoiding bias and using a preferred means of communication applied to all tasks. In surveys, patient-users were less likely to worry about being judged based on chatbot interactions (153/608, 25.2%) compared to interactions with a doctor (219/606, 36.1%) (P < .001). Patient-users preferred human clinicians for diagnostic tasks. DISCUSSION: Patient-users appear to simultaneously prefer chatbots for simple tasks or sensitive ones, with diverse motivations. Whether chatbots best meet patient needs while balancing ethical tensions regarding access, privacy, judgment, and bias is unclear.
conclusionFuture chatbot design must accommodate different and diverse patient preferences.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.