Evidence map›Paper›PMID 37115527›Full record

ArticleJAMA internal medicine2023

Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.

John W Ayers, Adam Poliak, Mark Dredze, Eric C Leas, Zechariah Zhu, Jessica B Kelley, Dennis J Faix, Aaron M Goodman, Christopher A Longhurst, Michael Hogarth and 1 more

Registry-linked trialOpen access · greenAbstract read
In one paragraph

Article in JAMA internal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06108037 (Duke Health Listens Survey on Generative Artificial Intelligence), which is not on this map. Cited by 1,151 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,151citing papers in PubMed, 3 pooled it
574.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT06108037 nacompletednot on this map

Duke Health Listens Survey on Generative Artificial Intelligence (AI) for MyChart Messaging

TypeinterventionalSponsorDuke UniversityRan2023 to 2023Enrolled1,454ConditionsCommunicationArmsGenerative AI for electronic communication and disclosure
3 · Its place in the literature

Who cites it

1,151 citing papers in PubMed, 3 syntheses or guidelines pooled it, 2,093 citations in OpenAlex.

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1,091 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 6 institutions in 1 country.

John W AyersQualcomm Institute, University of California San Diego, La Jolla.
Adam PoliakDepartment of Computer Science, Bryn Mawr College, Bryn Mawr, Pennsylvania.
Mark DredzeDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland.
Eric C LeasQualcomm Institute, University of California San Diego, La Jolla.
Zechariah ZhuQualcomm Institute, University of California San Diego, La Jolla.
Jessica B KelleyHuman Longevity, La Jolla, California.
Dennis J FaixNaval Health Research Center, Navy, San Diego, California.
Aaron M GoodmanDivision of Blood and Marrow Transplantation, Department of Medicine, University of California San Diego, La Jolla.
Christopher A LonghurstDepartment of Biomedical Informatics, University of California San Diego, La Jolla.
Michael HogarthDepartment of Biomedical Informatics, University of California San Diego, La Jolla.
Davey M SmithDivision of Infectious Diseases and Global Public Health, Department of Medicine, University of California San Diego, La Jolla.
University of California San Diego · USBryn Mawr College · USHuman Longevity (United States) · USJohns Hopkins University · USNaval Health Research Center · USQualcomm (United States) · US

Funding

Characterizing the Epidemiology of Cannabidiol (CBD) Use Among US adultsK01DA054303 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Eric Leas · 2022 to 2026
$810k
NIDA NIH HHS K01 DA054303
6 · The paper itself

Abstract

Importance: The rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians. Objective: To evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions. Design, Setting, and Participants: In this cross-sectional study, a public and nonidentifiable database of questions from a public social media forum (Reddit's r/AskDocs) was used to randomly draw 195 exchanges from October 2022 where a verified physician responded to a public question. Chatbot responses were generated by entering the original question into a fresh session (without prior questions having been asked in the session) on December 22 and 23, 2022. The original question along with anonymized and randomly ordered physician and chatbot responses were evaluated in triplicate by a team of licensed health care professionals. Evaluators chose "which response was better" and judged both "the quality of information provided" (very poor, poor, acceptable, good, or very good) and "the empathy or bedside manner provided" (not empathetic, slightly empathetic, moderately empathetic, empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale and compared between chatbot and physicians. Results: Of the 195 questions and responses, evaluators preferred chatbot responses to physician responses in 78.6% (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were significantly shorter than chatbot responses (52 [17-62] words vs 211 [168-245] words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher quality than physician responses (t = 13.3; P < .001). The proportion of responses rated as good or very good quality (≥ 4), for instance, was higher for chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or very good quality responses for the chatbot. Chatbot responses were also rated significantly more empathetic than physician responses (t = 18.9; P < .001). The proportion of responses rated empathetic or very empathetic (≥4) was higher for chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 times higher prevalence of empathetic or very empathetic responses for the chatbot. Conclusions: In this cross-sectional study, a chatbot generated quality and empathetic responses to patient questions posed in an online forum. Further exploration of this technology is warranted in clinical settings, such as using chatbot to draft responses that physicians could then edit. Randomized trials could assess further if using AI assistants might improve responses, lower clinician burnout, and improve patient outcomes.

Indexed as

PhysiciansSocial MediaArtificial IntelligenceCross-Sectional StudiesHumansLanguage

Identifiers

PMID37115527
PMCPMC10148230
OpenAlexW4367310920

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.