Artificial Intelligence in Hospice and Palliative Care: A Systematic Review of Its Role in Interprofessional Shared Decision-Making Care

Abstract

Background

Artificial intelligence (AI) is increasingly integrated into hospice and palliative care to support clinical decision-making across clinicians, patients, and caregivers. Applications include risk prediction, symptom monitoring, needs identification, and communication support, yet synthesis of its role in interprofessional shared decision-making (IP-SDM) across care levels remains limited.

Aim

To synthesize evidence on AI in hospice and palliative care, focusing on its role across IP-SDM levels and its influence on decision processes and outcomes.

Design

Systematic review guided by the IP-SDM framework. Methodological quality was assessed using the Joanna Briggs Institute hierarchy. The review was registered with PROSPERO.

Data Source

Six databases (CINAHL Complete, MEDLINE via ProQuest, PubMed, Scopus, PsycInfo, and IEEE Xplore) were searched to January 2026 for English-language empirical studies of AI in hospice and palliative care.

Results

Fourteen studies were included. AI applications primarily functioned as decision-support tools supporting early palliative care identification, advanced care planning (ACP), serious illness communication, and palliative referral processes. Most interventions operated at meso level of the IP-SDM framework through predictive models, automated prompts, and communication tools integrated into workflows. Although AI was associated with improved process measures, including ACP documentation and clinician behaviors, these improvements did consistently translate into downstream patient-centered outcomes. AI demonstrated limited involvement in deliberative aspects of shared decision-making.

Conclusion

AI currently functions primarily as a facilitator of early identification, communication, and workflow coordination within hospice and palliative care. Future research should prioritize multi-site implementation studies evaluating whether AI-supported processes translate into sustained patient-centered outcomes and equitable end-of-life care.