Other Titles
Exploring AI in Hospice and Palliative Care: An Integrative Review of Technological and Clinical Approaches [Poster Title]
Abstract
Background: Artificial Intelligence (AI) is increasingly transforming the healthcare industry by integrating various algorithmic models into electronic health records and wearable health technologies. These advancements support healthcare providers in making evidence-based decisions and predicting patient outcomes, ultimately enhancing the quality of care. This integrative literature review synthesizes peer-reviewed research on the application of AI in palliative and hospice care, aiming to identify both the implications and barriers to practice and research in this vital area.
Methods: We conducted a comprehensive search across nine databases—Academic Search Complete, Cumulative Index to Nursing and Allied Health Literature, Cochrane, PubMed, Medline, Web of Science, Scopus, PsycINFO, and ProQuest Dissertations & Theses Global—for studies published in English between January 2010 and June 2024. The review included qualitative, quantitative, and mixed-method studies exploring AI's impact on palliative or hospice care. Data on clinical settings, AI model types, validation methods, measurement outcomes, and key findings were extracted, and methodological quality was assessed using the Mixed Methods Appraisal Tool.
Results: The review included 47 studies published between 2018 and 2024, primarily quantitative descriptive studies using retrospective EHR analyses, revealing a shift from early mortality prediction models to more sophisticated machine learning and explainable AI (XAI) technologies. Key challenges identified include biases in training data, the need for external validation, and ethical concerns surrounding implementation.
Conclusion: AI has significant potential to enhance palliative care utilization and decision-making; however, limitations such as reliance on retrospective datasets, small study populations, and lack of external validation hinder generalizability. Future research should prioritize multicenter studies with diverse cohorts, focus on external validation, and ensure transparency in AI algorithms. Collaborative efforts among AI developers, clinicians, and regulators are crucial for the ethical integration of AI in clinical practice.
Sigma Membership
Non-member
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Hospice, Palliative, or End-of-Life Care, Instrument Tool Development, Implementation Science, Artificial Intelligence
Recommended Citation
Xu, Tuzhen; Rose, Gloria M.; Liu, Caiyi; Li, Lin; and Zhu, Sen, "Exploring AI in Hospice and Palliative Care: An Integrative Review" (2025). International Nursing Research Congress (INRC). 10.
https://www.sigmarepository.org/inrc/2025/posters_2025/10
Conference Name
36th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Seattle, Washington, USA
Conference Year
2025
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Exploring AI in Hospice and Palliative Care: An Integrative Review
Seattle, Washington, USA
Background: Artificial Intelligence (AI) is increasingly transforming the healthcare industry by integrating various algorithmic models into electronic health records and wearable health technologies. These advancements support healthcare providers in making evidence-based decisions and predicting patient outcomes, ultimately enhancing the quality of care. This integrative literature review synthesizes peer-reviewed research on the application of AI in palliative and hospice care, aiming to identify both the implications and barriers to practice and research in this vital area.
Methods: We conducted a comprehensive search across nine databases—Academic Search Complete, Cumulative Index to Nursing and Allied Health Literature, Cochrane, PubMed, Medline, Web of Science, Scopus, PsycINFO, and ProQuest Dissertations & Theses Global—for studies published in English between January 2010 and June 2024. The review included qualitative, quantitative, and mixed-method studies exploring AI's impact on palliative or hospice care. Data on clinical settings, AI model types, validation methods, measurement outcomes, and key findings were extracted, and methodological quality was assessed using the Mixed Methods Appraisal Tool.
Results: The review included 47 studies published between 2018 and 2024, primarily quantitative descriptive studies using retrospective EHR analyses, revealing a shift from early mortality prediction models to more sophisticated machine learning and explainable AI (XAI) technologies. Key challenges identified include biases in training data, the need for external validation, and ethical concerns surrounding implementation.
Conclusion: AI has significant potential to enhance palliative care utilization and decision-making; however, limitations such as reliance on retrospective datasets, small study populations, and lack of external validation hinder generalizability. Future research should prioritize multicenter studies with diverse cohorts, focus on external validation, and ensure transparency in AI algorithms. Collaborative efforts among AI developers, clinicians, and regulators are crucial for the ethical integration of AI in clinical practice.
Description
This presentation explores the role of Artificial Intelligence (AI) in palliative and hospice care, summarizing a review of 47 studies. It highlights AI's effectiveness in predicting mortality and enhancing decision-making while increasing palliative consultations. Despite high accuracy, challenges related to data integration and model reliability are discussed.