Evidence map›Paper›PMID 41676898›Full record

ArticleNursing open2026

Information Needs for Artificial Intelligence Chatbot on Prenatal Care Among Women of Advanced Maternal Age: A Thematic Analysis.

Uri Bang, Hyun Kyoung Kim, Geum Hee Jeong

Abstract read
In one paragraph

Article in Nursing open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Uri BangKongju National University, Gongju, South Korea.ORCID https://orcid.org/0000-0002-5919-9988
Hyun Kyoung KimKongju National University, Gongju, South Korea.ORCID https://orcid.org/0000-0003-2782-108X
Geum Hee JeongHallym University, Chuncheon, South Korea.ORCID https://orcid.org/0000-0002-2254-1962

Funding

National Research Foundation RS-2023-00239284
6 · The paper itself

Abstract

aimThis study aimed to investigate the information needs for artificial intelligence chatbot-based interventions on prenatal care among pregnant women of advanced maternal age.

designThis study employed a qualitative research method using thematic analysis.

methodsParticipants were pregnant women of advanced maternal age aged 35 and older who attended childbirth classes or used midwifery services in three cities. Participants were recruited through purposive sampling from September 23, 2023, to February 24, 2024, until saturation was reached. Semi-structured interviews were conducted with a total of 13 participants. The analysis was conducted in accordance with Braun and Clarke's six phases of thematic analysis: familiarisation with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report.

resultsFour central themes were identified that described prenatal care information needs for AI chatbots, as follows: (1) Information needs for an AI chatbot supporting advanced maternal age pregnancy; (2) childcare; (3) postpartum recovery; and (4) breastfeeding promotion.

conclusionThese findings highlight the importance of developing an accessible, interactive, and empathetic AI chatbot-based prenatal care application that can provide reliable information and support to improve maternal and fetal health among women of advanced maternal age. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The themes identified in this study offer concrete guidance for developing an AI chatbot that delivers relevant support for postpartum recovery, breastfeeding, newborn emergencies, and child health management among pregnant women of advanced maternal age. REPORTING

methodThe study follows the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Indexed as

Artificial IntelligenceNeeds AssessmentPrenatal CareAdultAdvanced Maternal AgeFemaleHumansPregnancyQualitative Researchadvanced maternal ageartificial intelligenceneed assessmentprenatal carequalitative study

Identifiers

PMID41676898
PMCPMC12895358

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.