Evaluating Chatbots as Nursing Assistants in Palliative Care: A Narrative Review
AI Summary
AI chatbots can enhance patient support, symptom monitoring and personalised communication in palliative care, improving accessibility and routine mental health check ins.
Accuracy gaps, poor electronic health record integration, data privacy and regulatory shortcomings threaten safety and risk undermining empathetic, human centred care.
Further research must evaluate long term outcomes, cost benefit, funding models and implementation strategies to ensure safe, effective and equitable deployment.
Indian J Palliat Care. 2026 Apr-Jun;32(2):129-136. doi: 10.25259/IJPC_144_2025. Epub 2026 May 12.
ABSTRACT
Artificial intelligence (AI) powered chatbots are emerging tools to support healthcare providers, especially in palliative care, by delivering information, emotional support and practical assistance to patients and caregivers. This study aimed to perform a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of chatbots acting as nursing assistants in palliative care, to elucidate their potential benefits, challenges and future directions. We conducted a narrative review of articles published between January 2020 and March 2025. Two reviewers independently searched relevant databases and screened studies according to predefined inclusion criteria. Extracted data were synthesised using the SWOT framework, and findings were reported in a narrative format. AI-powered chatbots in palliative care offer strengths such as enhanced patient support, symptom monitoring and mental health check-ins, but suffer from weaknesses such as inaccurate information and poor integration with health records. Opportunities include educational potential and personalised care, while threats include privacy concerns, regulatory gaps and resistance from healthcare professionals. Over-reliance on technology could also undermine empathetic, human-centred care. This review shows that AI-powered chatbots in palliative care can improve communication and personalised monitoring. However, challenges such as accuracy, data security and funding need to be addressed. Future studies should focus on long-term outcomes and cost-benefit analyses.
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