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We show that kidney exchange markets suffer from market failures whose remedy could increase transplants by 30 to 63 percent. First, we document that the market is fragmented and inefficient; most transplants are arranged by hospitals instead of national platforms. Second, we propose a model to show two sources of inefficiency: hospitals only partly internalize their patients’ benefits from exchange, and current platforms suboptimally reward hospitals for submitting patients and donors. Third, we calibrate a production function and show that individual hospitals operate below efficient scale. Eliminating this inefficiency requires either a mandate or a combination of new mechanisms and reimbursement reforms. (JEL D24, D47, I11)
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What Matters for the Productivity of Kidney Exchange?
Kidney exchange platforms serve patients who need a kidney transplant and who have a willing, but incompatible, donor. These platforms match patients and donors to produce transplants. This paper documents operational details of the three largest platforms in the United States. It then uses the framework developed in Agarwal et al. (2017) to examine how practical details influence platform productivity. The results show that reducing frictions in accepting proposed matches, frequent matching, and encouraging altruistic donors are important ways in which a platform can increase its productivity.
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- Award ID(s):
- 1729090
- PAR ID:
- 10074938
- Date Published:
- Journal Name:
- AEA Papers and Proceedings
- Volume:
- 108
- ISSN:
- 2574-0768
- Page Range / eLocation ID:
- 334 to 40
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Kidney exchange, where candidates with organ failure trade incompatible but willing donors, is a life-saving alternative to the deceased donor waitlist, which has inadequate supply to meet demand. While fielded kidney exchanges see huge benefit from altruistic kidney donors (who give an organ without a paired needy candidate), a significantly higher medical risk to the donor deters similar altruism with livers. In this paper, we begin by exploring the idea of large-scale liver exchange, and show on demographically accurate data that vetted kidney exchange algorithms can be adapted to clear such an exchange at the nationwide level. We then propose cross-organ donation where kidneys and livers can be bartered for each other. We show theoretically that this multi-organ exchange provides linearly more transplants than running separate kidney and liver exchanges. This linear gain is a product of altruistic kidney donors creating chains that thread through the liver pool; it exists even when only a small but constant portion of the donors on the kidney side of the pool are willing to donate a liver lobe. We support this result experimentally on demographically accurate multi-organ exchanges. We conclude with thoughts regarding the fielding of a nationwide liver or joint liver-kidney exchange from a legal and computational point of view.more » « less
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Purpose: AI models for kidney transplant acceptance must be rigorously evaluated for bias to ensure equitable healthcare access. This study investigates demographic and clinical biases in the Final Acceptance Model (FAM), a donor-recipient matching deep learning model that complements surgeons’ decision-making process in predicting whether to accept available kidneys for their patients with end of stage renal disorder. Methods: AI models for kidney transplant acceptance must be rigorously evaluated for bias to ensure equitable healthcare access. This study investigates demographic and clinical biases in the Final Acceptance Model (FAM), a donor-recipient matching deep learning model that complements surgeons’ decision-making process in predicting whether to accept available kidneys for their patients with end of stage renal disorder. Results: There is no significant racial bias in the model’s predictions (p=1.0), indicating consistent outcome across all racial combinations between donors and recipients. Gender-related effects as shown in Figure 1, while statistically significant (p=0.008), showed minimal practical impact with mean differences below 1% in prediction probabilities. Significant difference Clinical factors involving diabetes and hypertension showed significant difference (p=4.21e-19). The combined presence of diabetes and hypertension in donors showed the largest effect on predictions (mean difference up to -0.0173, p<0.05), followed by diabetes-only conditions in donors (mean difference up to -0.0166, p<0.05). These variations in clinical factor predictions showed bias against groups with comorbidities. Conclusions: The biases observed in the model highlight the need to improve the algorithm to ensure absolute fairness in prediction.more » « less
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The objective of this study was to investigate factors influencing one’s decision to become a live kidney donor under the framework of sociotechnical systems, by expanding the focus to include larger organizational influences and technological considerations. Semi-structured interviews were conducted with live kidney donors who donated through University of Louisville Health, Trager Transplant Center, a mid-scale transplant program, in the years 2017 through 2019. The interview transcripts were analyzed for barriers and facilitators to live kidney donation within a sociotechnical system. The most salient facilitators included: having an informative, caring, and available care team; the absence of any negative external pressure toward donating; donating to a family or friend; and the ability to take extra time off work for recovery. The most recurrent barriers included: short/medium-term (<1 year) negative health impacts because of donation; the need to make minor lifestyle changes (e.g., less alcohol consumption) after donation; and mental health deterioration stemming from the donation process. The sociotechnical systems framework promotes a balanced system comprised of social, technical, and environmental subsystems. Assessing the facilitators and barriers from the sociotechnical system perspective revealed the importance of and opportunities for developing strategies to promote integration of technical subsystem, such as social media apps and interactive AI platforms, with social and environmental subsystems to enable facilitators and reduce barriers effectively.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1257/pandp.20181077
