International journal of palliative nursing · 2025
Artificial intelligence in symptom management and clinical decision support for palliative care.
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Narvaez Roison Andro, Ferrer Marilane, Peco Ralph Antonio, Mejilla Joylyn
Аннотация
BACKGROUND: Artificial intelligence (AI) is increasingly applied to palliative care to enhance symptom management and decision support. However, the breadth and implementation strategies of such technologies remain underexplored.
AIM/OBJECTIVES: This scoping review aimed to map empirical studies from 2015 to 2025 that used AI for symptom assessment, mortality prediction and care planning in palliative settings.
METHODS: The review followed Arksey and O'Malley's five-stage framework for scoping reviews and was reported according to PRISMA-ScR guidelines. Included studies were appraised using the Mixed Methods Appraisal Tool.
RESULTS: A total of 12 peer-reviewed studies were included, revealing five major themes: (1) Predictive modeling for mortality and referral, enabling early identification of high-risk patients; (2) Automated symptom detection, improving distress surveillance via NLP and decision trees; (3) Wearable and time-series forecasting, allowing real-time physiologic tracking; (4) Workflow integration, demonstrating seamless adoption of AI tools in clinical systems; and (5) Explainability and trust, where interpretable outputs enhanced clinician confidence. These studies showed improved symptom control, timely referrals and interdisciplinary coordination.
CONCLUSION: AI offers promising solutions to enhance palliative nursing through proactive, data-driven care. Ethical implementation, training, and validation are key to sustainable adoption.
AIM/OBJECTIVES: This scoping review aimed to map empirical studies from 2015 to 2025 that used AI for symptom assessment, mortality prediction and care planning in palliative settings.
METHODS: The review followed Arksey and O'Malley's five-stage framework for scoping reviews and was reported according to PRISMA-ScR guidelines. Included studies were appraised using the Mixed Methods Appraisal Tool.
RESULTS: A total of 12 peer-reviewed studies were included, revealing five major themes: (1) Predictive modeling for mortality and referral, enabling early identification of high-risk patients; (2) Automated symptom detection, improving distress surveillance via NLP and decision trees; (3) Wearable and time-series forecasting, allowing real-time physiologic tracking; (4) Workflow integration, demonstrating seamless adoption of AI tools in clinical systems; and (5) Explainability and trust, where interpretable outputs enhanced clinician confidence. These studies showed improved symptom control, timely referrals and interdisciplinary coordination.
CONCLUSION: AI offers promising solutions to enhance palliative nursing through proactive, data-driven care. Ethical implementation, training, and validation are key to sustainable adoption.