ArticleCost effectiveness and resource allocation : C/E2025
Cost-effectiveness analysis of home-based palliative care for end-stage cancer patients.
Article in Cost effectiveness and resource allocation : C/E, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Assessing the Value for Money of AI-Assisted Technologies for Older Adults: Scoping Review of Economic Evaluations.Journal of medical Internet research · 2026Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGlobal increases in cancer prevalence and the associated financial burden necessitate the development of highly cost-effective care models. Optimally managing scarce healthcare resources is crucial to balance the goals of maximizing survival for curable patients and quality of life for those with terminal illness. This study evaluated the cost-effectiveness of a novel home-based palliative care model for end-stage cancer patients that utilizes Artificial Intelligence (AI) for operational optimization, compared to usual care.
methodsThis case-control study compared costs and outcomes for two groups of end-stage cancer patients. The intervention group consisted of 94 patients receiving home-based palliative care from an Iranian NGO, Ala, which uses an AI-driven management system for resource planning. The control group included 113 patients receiving usual care, comprising outpatient services and hospitalization as needed. The groups were matched for cancer type, stage, age, and gender. We calculated the costs of inpatient and outpatient clinical and para-clinical services over a 6-month period. Outcome measures were hospital length of stay (LOS) and the proportion of deaths occurring at home.
resultsHome-based model demonstrated significant advantages. A higher proportion of deaths occurred at home in the intervention group compared to the control group (45.7% vs. 23.9%; p = 0.001). The mean LOS was lower for the intervention group (14.8 days vs. 20.5 days; p = 0.052). The mean total costs were also significantly lower for the intervention group (\$8,860 vs. \$13,172; p = 0.019). Incremental analysis confirmed home-based care as a dominant strategy, providing better outcomes at a lower cost.
conclusionHome-based palliative care model proved to be a dominant and cost-effective strategy for end-stage cancer patients, resulting in more home deaths, reduced hospital stays, and lower costs. We recommend the inclusion of such optimized home-based palliative care in health insurance benefits packages and suggest that the integration of AI for demand forecasting and resource planning is a key component for its scalability and efficiency.
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