Evidence map›Paper›PMID 42390917›Full record

ArticleJournal of medical Internet research2026

Attitudes and Needs of Health Care Providers Toward Artificial Intelligence-Assisted Pediatric Palliative Care: Mixed Methods Study.

Siyu Cai, Qiaohong Guo, Zishen Wang, Ruixin Wang, Xuan Zhou, Xiaoxia Peng

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Siyu CaiCenter for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.ORCID https://orcid.org/0009-0003-4186-2504
Qiaohong GuoSchool of Nursing, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0001-8734-8138
Zishen WangSchool of Nursing, Capital Medical University, Beijing, China.ORCID https://orcid.org/0009-0006-9292-843X
Ruixin WangHematology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.ORCID https://orcid.org/0009-0009-0683-8393
Xuan Zhou *Hematology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.ORCID https://orcid.org/0000-0002-0628-5866
Xiaoxia Peng *Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.ORCID https://orcid.org/0000-0001-7660-4618

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile artificial intelligence's (AI's) transformative potential in health care is widely acknowledged, its application in highly sensitive, humanistic domains like pediatric palliative care (PPC) remains largely unexplored.

objectiveThis study aims to explore the attitudes and needs of health care providers on the PPC assisted by AI, with the goal of informing future development and implementation of AI systems in this field.

methodsThis was an explanatory sequential mixed methods study consisting of a nationwide cross-sectional questionnaire survey (March-April 2025) followed by qualitative semistructured interviews (August-October 2025). The quantitative study aimed to investigate PPC health care providers' experiences, attitudes, and needs for the application of AI. Participants included team members of all recognized PPC teams in mainland China. The qualitative study aimed to explore in greater depth the potential future roles of AI in this field, as well as the features of an ideal AI-assisted tool for PPC. Potential interviewees were recruited from the pool of quantitative survey respondents.

resultsAmong 352 survey respondents, most (n=205, 58.24%) reported moderate familiarity with AI, with large language models being the most commonly used (n=280, 79.55%). Among large language model users, over half (161/280, 57.50%) reported using them for clinical purposes. Attitudes were generally positive: 67.05% (236/352) believed AI's benefits would outweigh drawbacks, and 75% (264/352) considered its implementation feasible. The most desired applications were patient and family education (276/352, 78.41%) and symptom management (257/352, 73.01%). Interviews with 17 providers revealed three themes: (1) clinical roles and boundaries, (2) elements for clinical integration, and (3) challenges in development and deployment.

conclusionsThis study reveals that PPC providers express positive attitudes and strong demand for AI-assisted clinical work. Furthermore, the research clarifies appropriate roles for AI, outlines elements for clinical integration, and highlights potential challenges in development and integration. This study provides evidence for the feasibility of AI application in PPC and offers guidance for the future development and deployment of AI tools.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelHealth PersonnelPalliative CarePediatricsAdultChildChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and Questionnairesartificial intelligenceattitudesmixed methods studyneedspediatric palliative care

Identifiers

PMID42390917
PMCPMC13376846

What Socratic holds

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LicenceCC BY
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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 Socratic graph.