Health insurance and kidney transplantation outcomes in the United States: a systematic review and AI-driven analysis of disparities in access and survival.
Garcia Valencia, O. A., et al.Ren Fail. 2025 Dec;47(1):2513007.
Aims
This study aimed to assess how the type of health insurance affects access to kidney transplantation and post-transplant outcomes in the United States, while also identifying disparities using AI-assisted analysis to inform equity-focused policy recommendations.
Interventions
MEDLINE, EMBASE, and the Cochrane Database were searched for relevant literature. Titles, abstract and full-text were screened and data were extracted by two independent reviewers. The Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias.
Participants
14 studies were included in the review.
Outcomes
The primary outcomes were access to kidney transplantation, patient survival and graft survival. The secondary outcomes were medication adherence, time to transplantation, waitlist removal (rate and causes) by insurance type, socioeconomic and demographic modifying factors of insurance-associated transplant outcomes.
Follow-up
N/A
CET Conclusions
This systematic review aimed to describe the association between health insurance coverage and transplant waitlisting, access and outcomes in the United States. A total of 14 observational (13 cohort and 1 cross-sectional) studies were included in the review. The authors found that race, socioeconomic status and type of insurance had a significant impact on access and outcomes of kidney transplantation, with lower referral rates, higher rate of transplant waitlist rejection and poorer posttransplant outcomes observed in publicly insured patients as well as patients belonging to racial and ethnic minorities and poorer socioeconomic backgrounds. With the use of AI-assissted analytics, the paper also provides structured, actionable policy recommendations to help improve equity in the field of transplantation. Overall, the methodology of the review is sound, and the manuscript is clearly written. The paper provides a good demonstration of how AI can be used in research to recognise patterns, identify gaps and offer policy recommendations.
Trial registration
N/A