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

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

  • By

  • Siyu Cai

  • Qiaohong Guo

  • Zishen Wang

  • Ruixin Wang

  • Xuan Zhou

  • Xiaoxia Peng

  • July 2, 2026

  • 0 min

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Clinical Report: Perspectives and Requirements of Healthcare Professionals Regarding AI-Enhanced Pediatric Palliative Care

Overview

This study investigates healthcare providers' attitudes towards AI applications in pediatric palliative care (PPC). It emphasizes the need for tailored AI tools that align with the unique requirements of this field.

Background

Pediatric palliative care is essential for improving the quality of life for children with serious illnesses and their families. The integration of artificial intelligence in healthcare presents opportunities to enhance symptom management and decision-making in PPC. However, the unique ethical and contextual challenges in PPC necessitate careful consideration of AI's role in this sensitive area.

Data Highlights

This study utilized a mixed methods approach, combining quantitative data from a cross-sectional questionnaire with qualitative insights from semi-structured interviews among PPC healthcare providers in China.

Key Findings

  • Healthcare providers expressed a need for AI tools that enhance patient needs assessment and symptom management in PPC.
  • AI applications must align with the ethical and contextual complexities inherent in pediatric palliative care decision-making.
  • Providers highlighted the importance of end-user acceptance and trust in the successful implementation of AI technologies.
  • Current AI research predominantly focuses on diagnosis and treatment, lacking emphasis on improving quality of life in PPC.
  • There is a significant gap in understanding the specific needs of PPC professionals regarding AI integration.

Clinical Implications

Healthcare providers in PPC should be involved in the development of AI tools to ensure they meet the specific needs of the field.

Conclusion

The study highlights the necessity for AI tools tailored to the complexities of pediatric palliative care.

Related Resources & Content

  1. Frontiers in Digital Health, 2026 -- Perspectives on healthcare artificial intelligence policy from health equity professionals: findings from an interview study
  2. npj Digital Medicine, 2026 -- Systematic Review and Meta-Analysis of Automated Algorithms for Detecting Patients in Need of Palliative Care
  3. npj Digital Medicine, 2026 -- A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems
  4. End of life care for infants, children and young people with life-limiting conditions: planning and management, NICE guideline NG61
  5. EAPC CYPRG Charter full doc eapc -- European Charter on Palliative Care for Children and Young People
  6. npj Digital Medicine — Toward governance of artificial intelligence in pediatric healthcare
  7. Ethical considerations in AI for child health and recommendations for child-centered medical AI
  8. Toward governance of artificial intelligence in pediatric healthcare
  9. End of life care for infants, children and young people with life-limiting conditions: planning and management
  10. EAPC CYPRG Charter full doc eapc
  11. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare - PMC
  12. AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN - PubMed
  13. AI in Palliative Care: A Scoping Review of Foundational Gaps and Future Directions for Responsible Innovation - ScienceDirect
  14. Systematic literature review on the application of explainable artificial intelligence in palliative care studies - ScienceDirect
  15. Using a Machine-Learning Algorithm to Identify Palliative Care Needs in a Primary Care Population: A Pilot Study
  16. Large language models perpetuate bias in palliative care: development and analysis of the Palliative Care Adversarial Dataset (PCAD)
  17. Exploring the application of Artificial Intelligence in palliative care and its practical, technical and ethical considerations: a scoping review | BMC Palliative Care | Springer Nature Link

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