Evidence map›Paper›PMID 40474032›Full record

ArticleHealth care management science2025

Optimal quality oversight in kidney transplantation and its impact on transplant centers' waitlist management.

Zahra Gharibi, Hung T Do, Michael Hahsler, Mehmet U S Ayvaci

Abstract read
In one paragraph

Article in Health care management science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Zahra GharibiDepartment of Operations, Supply Chain, and Business Analytics, College of Business Administration, California State University San Marcos, San Marcos, CA, 92096, USA. zgharibi@csusm.edu.ORCID https://orcid.org/0000-0001-7317-1247
Hung T DoGrossman School of Business, University of Vermont, Burlington, VT, 05405, USA.
Michael HahslerDepartment of Computer Science, Southern Methodist University, Dallas, TX, 75205, USA.
Mehmet U S AyvaciJindal School of Management, University of Texas at Dallas, Richardson, TX, 75080, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper studies the effects of quality oversight in the context of assessing kidney transplantation-related outcomes and possible unintended consequences (e.g., cherry-picking of organs and selection of healthier transplant candidates). In this context, we propose a stochastic economic model that identifies socially optimal kidney transplant choices given the inherent trade-off between the expected wait time and the quality of the received donor kidney for a given patient. Socially optimal decisions seek to maximize the utilitarian welfare function defined as the sum of all patients' post-transplant expected utilities. To determine the social loss, we compare the socially optimal decisions to those taken by a transplant program that maximizes its utility. We derive the optimal quality oversight policy that minimizes social loss and examine how decisions are impacted due to the changes introduced by the new Kidney Allocation System. Our empirical analysis using data from the Scientific Registry of Transplant Recipients and United States Renal Data System indicates that current quality oversight imposed through Conditions of Participation results in inefficient transplant decisions for 56% of recipients, and the performance is inconsistent across different regions and parameters. We propose that the risk-adjusted post-transplant performance assessment policy considers the factors impacting demand-supply parameters such as organ availability in the 11 US transplant regions, candidates' blood type, and the newly introduced Kidney Allocation System. Policymakers and providers can utilize insights from our findings to design effective oversight mechanisms and make informed decisions regarding transplant and waitlist management that yield desired outcomes.

Indexed as

Kidney TransplantationQuality of Health CareWaiting ListsHumansModels, EconomicTissue and Organ ProcurementUnited StatesKidney transplantationNew kidney allocation systemOperations managementOperations researchQuality oversightQueuing model in healthcareStochastic model

Identifiers

PMID40474032
PMCPMC12535526

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.