Attitudes and Needs of Health Care Providers Toward Artificial Intelligence-Assisted Pediatric Palliative Care: Mixed Methods Study
Clinical Snapshot
PICO Framework
| P — Population | Health care providers (physicians, nurses, and allied health professionals) working within recognised pediatric palliative care (PPC) teams across mainland China |
| I — Intervention | Artificial intelligence-assisted tools and systems applied to pediatric palliative care clinical practice (including large language models, symptom management support, patient/family education platforms) |
| C — Comparator | No formal comparator — exploratory study of attitudes and needs; implicitly compared to current AI-naïve PPC practice |
| O — Outcomes | Provider familiarity with AI, attitudes toward AI implementation (perceived benefits vs. drawbacks, feasibility), desired AI application domains, qualitative themes regarding clinical roles, integration elements, and deployment challenges |
Bottom Line
This explanatory sequential mixed methods study is the first to systematically examine PPC health care provider attitudes toward AI across a national sample in China. Among 352 providers, two-thirds expressed positive attitudes and three-quarters considered AI implementation feasible, with patient/family education and symptom management identified as priority applications. Qualitative interviews with 17 providers elaborated on appropriate clinical roles, integration requirements, and deployment challenges. The study addresses a genuine evidence gap and its mixed methods design is methodologically appropriate. However, several quality concerns warrant caution: the absence of a reflexivity statement, unreported ethics approval, unconfirmed thematic saturation, and lack of inferential analysis limit the strength of conclusions. Social desirability and sampling biases may inflate positive attitudes. The findings are specific to China's centralised healthcare system and should be interpreted cautiously before application to Australian PPC practice. Nonetheless, the study provides a useful foundation for co-designing AI tools with PPC clinicians, and its emphasis on provider-defined boundaries and integration requirements offers practical guidance for developers and health system planners considering AI in this sensitive clinical domain.
Key Findings
P Value: Not reported
Effect Size: Not applicable — descriptive cross-sectional and qualitative study; no effect sizes calculated
Primary Outcome: 67.05% (236/352) of PPC health care providers believed AI's benefits would outweigh its drawbacks; 75% (264/352) considered AI implementation in PPC feasible. Most desired applications were patient and family education (78.41%, 276/352) and symptom management (73.01%, 257/352). Three qualitative themes identified: (1) clinical roles and boundaries of AI, (2) elements required for clinical integration, and (3) challenges in development and deployment.
Nnt Or Sensitivity: Not applicable — attitudinal survey and qualitative study; no diagnostic, therapeutic, or prognostic metrics reported
Confidence Interval: Not reported
Clinical Application
The study suggests strong provider-level demand and perceived feasibility for AI-assisted tools in PPC, particularly for patient/family education and symptom management support. However, clinical implementation feasibility depends on factors not fully addressed: regulatory approval of AI tools, integration with existing electronic medical record systems, staff training requirements, and ongoing governance frameworks. The qualitative theme of 'challenges in development and deployment' signals that provider enthusiasm does not automatically translate to implementation readiness. Australia's PPC landscape differs substantially from mainland China's. Australia has a smaller, geographically dispersed PPC workforce supported by state-based children's hospitals (e.g., The Royal Children's Hospital Melbourne, Sydney Children's Hospital Network) and community-based services such as Very Special Kids and Bear Cottage. The RACGP and Australian and New Zealand Society of Palliative Medicine (ANZSPM) have not yet issued specific guidance on AI in palliative care. The TGA's evolving Software as a Medical Device (SaMD) regulatory framework would apply to any AI clinical decision support tools deployed in this context. PBS considerations are not directly relevant to AI tools but would apply to any pharmacological symptom management protocols informed by AI recommendations. The study's finding that patient/family education is the most desired AI application has direct relevance to Australia's culturally and linguistically diverse PPC population, where AI-assisted multilingual communication tools could address documented equity gaps. Health care professionals working in specialist or generalist pediatric palliative care settings, including paediatric oncologists, palliative care nurses, allied health professionals, and clinical educators involved in PPC team-based care
Abstract
BACKGROUND: While 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. OBJECTIVE: This 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. METHODS: This 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. RESULTS: Among 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. CONCLUSIONS: This 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.
References
- 1.Cai, S., Guo, Q., Wang, Z., Wang, R., Zhou, X., & Peng, X. (2026). Attitudes and needs of health care providers toward artificial intelligence-assisted pediatric palliative care: Mixed methods study. Journal of Medical Internet Research. https://doi.org/10.2196/93400
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