ArticleInquiry : a journal of medical care organization, provision and financing
Navigating the Adoption Maze: Evolutionary Dynamics of Stakeholder Behavior in AI-Driven Elderly Care Solutions.
Article in Inquiry : a journal of medical care organization, provision and financing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A study on collaborative governance of excessive medical care based on three-way evolutionary game and simulation.Frontiers in public health · 2025Article
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Authors and funding
2 authors.
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Abstract
In the face of a rapidly aging population and the increasing demand for elderly care, the adoption of artificial intelligence (AI) in healthcare products has emerged as a promising solution to enhance service delivery. This paper investigates the behavioral evolution of multiple stakeholders-namely, government entities, AI healthcare enterprises, and medical professionals-in the adoption process of AI-enabled elderly care products. By employing an evolutionary game theory model, we analyze the stability strategies of these stakeholders under varying initial conditions. Our findings reveal that government subsidies and regulatory measures play a crucial role in promoting the adoption of these technologies, while the attitudes of enterprises and medical professionals are significantly influenced by perceived costs and benefits. Simulation analyses were conducted using MATLAB 2019a to validate the model, providing insights into optimizing stakeholder engagement and enhancing the adoption of AI in elderly care. We propose actionable recommendations for policymakers and industry leaders to foster the integration of AI into elderly care services, addressing critical challenges and leveraging opportunities in this evolving landscape.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.