Currently submitted to: Transfer Hub (manuscript eXchange)
Date Submitted: May 3, 2026
Open Peer Review Period: May 3, 2026 - Jun 28, 2026
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
PCP-Bot: A Voice-Based Large Language Model–Driven Chatbot for Pre-Visit Planning in Primary Care- A Prospective Feasibility Study
ABSTRACT
Background:
Primary care encounters are increasingly constrained by limited time and documentation burden, reducing opportunities for meaningful patient–physician communication. Pre-visit planning tools can improve efficiency but are often rigid, burdensome, and inconsistently adopted. Patient-facing applications using large language models (LLMs) offer the potential for more flexible, conversational approaches to eliciting patient information prior to clinical encounters, though concerns remain regarding safety, hallucination, and workflow integration.
Objective:
To evaluate the feasibility of an LLM-based conversational assistant (PCP-Bot) for pre-visit planning in primary care, focusing on the quality, usability, and perceived clinical utility of generated pre-visit summaries.
Methods:
We conducted a prospective feasibility study using simulated primary care scenarios. PCP-Bot, implemented using ChatGPT-4o, engaged users via a voice-based conversational interface and generated structured pre-visit summaries using a schema-constrained output. Ten synthetic cases were enacted by trained non-medical researchers acting as patients, producing 30 complete dialogues and corresponding summaries. Practicing physicians (N=10) independently rated summaries across six domains (usefulness, readability, relevance, coherence, comprehensiveness, and factual accuracy) using 5-point Likert scales. Perceived usefulness (TAM-PU) was assessed among physicians, and perceived ease of use (TAM-PEU) was assessed among participants simulating patient interactions. Quantitative analyses examined interaction characteristics and their associations with summary quality.
Results:
PCP-Bot generated concise conversations (median 28 exchanges, IQR 26.25–31) and summaries (median 148 words, IQR 132.75–162). Clinician ratings were favorable across domains, including usefulness (mean 3.99, SD 0.25), relevance (mean 4.07, SD 0.21), readability (mean 4.07, SD 0.26), coherence (mean 3.94, SD 0.27), and comprehensiveness (mean 3.88, SD 0.22), with a low hallucination rate (mean 0.51, SD 0.25). Simulated patients reported high perceived ease of use (mean TAM-PEU 97.2, SD 2.48), while physicians reported moderate perceived usefulness (mean TAM-PU 61.3, SD 15.9). Longer summaries were associated with higher ratings of usefulness (r=0.39, P=.033) and comprehensiveness (r=0.39, P=.031), whereas longer patient dialogue was negatively associated with perceived relevance (r=−0.39, P=.035).
Conclusions:
In simulated primary care scenarios, an LLM-based conversational assistant produced concise, structured pre-visit summaries that clinicians rated favorably, supporting the feasibility of conversational pre-visit workflows. Summary quality appears sensitive to the balance between detail and conciseness. Real-world evaluation is needed to assess clinical impact, safety, equity, and integration into routine care.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.