Evidence map›Paper›PMID 38300696›Full record

Observational studyJMIR medical education2024

Increasing Realism and Variety of Virtual Patient Dialogues for Prenatal Counseling Education Through a Novel Application of ChatGPT: Exploratory Observational Study.

Megan Gray, Austin Baird, Taylor Sawyer, Jasmine James, Thea DeBroux, Michelle Bartlett, Jeanne Krick, Rachel Umoren

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 16% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Megan GrayDivision of Neonatology, University of Washington, Seattle, WA, United States.ORCID 0000-0002-3272-9432
Austin BairdDivision of Healthcare Simulation Sciences, Department of Surgery, University of Washington, Seattle, WA, United States.ORCID 0000-0002-4711-3016
Taylor SawyerDivision of Neonatology, University of Washington, Seattle, WA, United States.ORCID 0000-0002-3908-8957
Jasmine JamesDepartment of Family Medicine, Providence St Peter, Olympia, WA, United States.ORCID 0000-0001-9907-906X
Thea DeBrouxDivision of Neonatology, University of Washington, Seattle, WA, United States.ORCID 0009-0005-5475-8233
Michelle BartlettDepartment of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.ORCID 0000-0002-9119-6779
Jeanne KrickDepartment of Pediatrics, San Antonio Uniformed Services Health Education Consortium, San Antonio, TX, United States.ORCID 0000-0002-6461-3776
Rachel UmorenDivision of Neonatology, University of Washington, Seattle, WA, United States.ORCID 0000-0003-2356-9278
University of Washington · USChildren's Hospital of Philadelphia · USProvidence St. Peter Hospital · USSan Antonio Uniformed Services Health Education Consortium · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUsing virtual patients, facilitated by natural language processing, provides a valuable educational experience for learners. Generating a large, varied sample of realistic and appropriate responses for virtual patients is challenging. Artificial intelligence (AI) programs can be a viable source for these responses, but their utility for this purpose has not been explored.

objectiveIn this study, we explored the effectiveness of generative AI (ChatGPT) in developing realistic virtual standardized patient dialogues to teach prenatal counseling skills.

methodsChatGPT was prompted to generate a list of common areas of concern and questions that families expecting preterm delivery at 24 weeks gestation might ask during prenatal counseling. ChatGPT was then prompted to generate 2 role-plays with dialogues between a parent expecting a potential preterm delivery at 24 weeks and their counseling physician using each of the example questions. The prompt was repeated for 2 unique role-plays: one parent was characterized as anxious and the other as having low trust in the medical system. Role-play scripts were exported verbatim and independently reviewed by 2 neonatologists with experience in prenatal counseling, using a scale of 1-5 on realism, appropriateness, and utility for virtual standardized patient responses.

resultsChatGPT generated 7 areas of concern, with 35 example questions used to generate role-plays. The 35 role-play transcripts generated 176 unique parent responses (median 5, IQR 4-6, per role-play) with 268 unique sentences. Expert review identified 117 (65%) of the 176 responses as indicating an emotion, either directly or indirectly. Approximately half (98/176, 56%) of the responses had 2 or more sentences, and half (88/176, 50%) included at least 1 question. More than half (104/176, 58%) of the responses from role-played parent characters described a feeling, such as being scared, worried, or concerned. The role-plays of parents with low trust in the medical system generated many unique sentences (n=50). Most of the sentences in the responses were found to be reasonably realistic (214/268, 80%), appropriate for variable prenatal counseling conversation paths (233/268, 87%), and usable without more than a minimal modification in a virtual patient program (169/268, 63%).

conclusionsGenerative AI programs, such as ChatGPT, may provide a viable source of training materials to expand virtual patient programs, with careful attention to the concerns and questions of patients and families. Given the potential for unrealistic or inappropriate statements and questions, an expert should review AI chat outputs before deploying them in an educational program.

Indexed as

Premature BirthPrenatal EducationArtificial IntelligenceCounselingEducational StatusFemaleHumansInfant, NewbornPregnancyAIartificial intelligenceChatGPTneonatologyprenatal counselingsimulationvirtual healthvirtual patient

Identifiers

PMID38300696
PMCPMC10870212
OpenAlexW4389577325

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.