Professionalism/Ethics in Organ Allocation

Background The Process In the U.S., organ donation starts with finding a willing donor. Donors can either be alive or deceased if registered with the U.S. Organ Procurement and Transplantation Network (OPTN)About the OPTN - OPTN https://optn.transplant.hrsa.gov/about/ 2025-05-05 optn.transplant.hrsa.gov. After the viability of the organ(s) is determined, an algorithm matches the donor's organ(s) to recipients based on traits like blood type or donor healthThe Organ Transplant Process organdonor.gov https://www.organdonor.gov/learn/process/transplant-process 2025-05-05 www.organdonor.gov. After matching is complete, the transplant doctors will decide to accept the organ, and quickly make arrangements for the organ delivery and setup surgeryOrgan Donation & Transplantation https://my.clevelandclinic.org/health/treatments/11750-organ-donation-and-transplantation 2025-05-05 Cleveland Clinic en. Complications For living donors, recovery or medical issues from surgery could make returning to work or life previously difficultLiving Organ Donation organdonor.gov https://www.organdonor.gov/learn/process/living-donation 2025-05-05 www.organdonor.gov. For recipients, medical complications such as blood loss, organ rejection, and infection are all potential risks. One of the major challenges in organ donation currently is a difficulty in balancing the risk of waitlist mortality with the chances of graft and patient survival post transplant. Traditional allocation systems dont have the complexity needed to efficiently consider all the variables that go into a transplant. As a result, some candidates may be under prioritized despite having outcomes that are more favorable, while others may receive organs despite a high-risk of post transplant complications. This inefficiency highlights the need for more adaptive and data-driven tools that can personalize transplant decisions based on a broader set of patient data. Ethical Issues Skipping the LineRosenthal Brian M. Hansen Mark White Jeremy 2025-02-26 Organ Transplant System ‘in Chaos’ as Waiting Lists Are Ignored en-US The New York Times https://www.nytimes.com/interactive/2025/02/26/us/organ-transplants-waiting-list-skipped-patients.html 2025-05-06 0362-4331 Generally, organ allocation is done according to a ranking system. The ranking system takes into account the biological characteristics of the organ, and then ranks potential recipients by factors such as severity, how long they have been waiting, and likelihood of success. Usually, the offers will be made to the candidates in the order they appear on the ranking, and each doctor will have up to 1 hour to accept or reject the organ. For a long time, skipping the list was considered a last resort and resulted in an investigation by the United Network for Organ Sharing (UNOS). In recent years, however, "line-skipping" has become far more common, nearly 10 times as common as a decade ago. Rather than an offer made to a specific patient, open offers may be made to hospitals that can then choose which patient the organ goes to. The organ may still go to someone close to the top of the list, or it may go to someone thousands of spots down. Organ allocation organizations justified this decision as a way to reduce organ waste by matching organs more quickly; however, there has not been any decrease in the number of discarded organs since this practice became more widespread. Many times patients are skipped well before the organ is in danger of being discarded. Doctors and allocation workers also allege that line-skipping may be used to increase profits for these organizations. Organs are paid for at a flat rate, so saving time and labor costs financially benefits the allocation organization. The organization with the highest line-skipping rate in the country, Lifebanc, skips the line nearly a third of the time. Most of those organs have gone to a hospital that Lifebanc hires medical advisers from, the Cleveland Clinic. In a recent investigation, the New York Times found that at least 1,200 people had died after being skipped on the organ transplant list. Skipping the transplant list also favors wealthy white patients, exacerbating existing racial and socioeconomic disparities. UNOS has almost entirely ceased investigating these cases, only taking action in 0.5% of cases. This means organ allocation organizations are functioning with minimal oversight, and patients are experiencing harm as a result. Instances of Discrimination In 1999, faulty research claimed that the estimated glomerular filtration rate (eGFR), a metric for measuring kidney health, was on average higher for Black patients2024-04-01 A biased test kept thousands of Black people from getting a kidney transplant. It's finally changing https://apnews.com/article/kidney-transplant-race-black-inequity-bias-d4fabf2f3a47aab2fe8e18b2a5432135 2025-05-05 AP News en. Due to this discrepancy, Black patients at bad eGFR levels who needed to be put on the transplant list had to wait for their condition to get worse. The National Kidney Foundation (NKF) and American Society of Nephrology (ASN) established rules for race-neutral eGFR tests in 2021Lewis Diane C. Woods Karima Watkins Tamara Kofman Mila Kempf Purvee Winn Joseph Addressing The Harmfully Slow Uptake Of Race-Neutral Kidney Function Tests https://www.healthaffairs.org/content/forefront/addressing-harmfully-slow-uptake-race-neutral-kidney-function-tests Health Affairs Forefront en 10.1377/forefront.20250205.468098. A year later, OPTN forced transplant centers to adhere to similar standards. In 2023, over 14,000 Black potential transplant patients were moved up the kidney wait list. The risk metrics of organ transplant candidates, which help place them on the waitlist, are an area of concern. The Model for End-Stage Liver Disease (MELD) adds 1.33 points to female patients' scores, and the Kidney Donor Risk Index (KDRI) still factors in raceDale Reid Cheng Maggie Pines Katharine Casselman Currie Maria Elizabeth 2024-10-17 Inconsistent values and algorithmic fairness: a review of organ allocation priority systems in the United States https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-024-01116-x BMC Medical Ethics en 25 1 10.1186/s12910-024-01116-x 1472-6939 11483980 39420378. The lung allocation score has a strong bias towards candidates in closer distances, but the composite allocation score (CAS) replaced it in 2023. CAS got rid of the strict geographic constraints, but had issues with blood type bias against type OAlderete Isaac S. Hartwig Matthew G. 2025-02-13 From flawed to fairer: reducing blood type bias in lung transplant allocation https://www.amjtransplant.org/article/S1600-6135(25)00079-6/abstract American Journal of Transplantation English 0 0 10.1016/j.ajt.2025.02.003 1600-6135 39954812. After a 3-month study, it was found that the simulation used to create CAS had an error marking type O candidates as universal recipients. The policy changes to CAS caused the waiting list mortality rate and median time to transplant to be maintained or reduced for all blood typesLung transplants for blood type O increase after modified blood type rating scale - OPTN https://optn.transplant.hrsa.gov/news/lung-transplants-for-blood-type-o-increase-after-modified-blood-type-rating-scale/ 2025-05-06 optn.transplant.hrsa.gov. Future How AI Can Help Machine learning models are trained from large datasets and have the ability to consider numerous features in the set. This allows the model to create complex relationships between data from donors and recipients that are not readily available. Artificial intelligence is changing how transplant care is delivered by making it more accurate and personalized. Before a transplant, AI can improve donor-recipient matching by analyzing health and immune factors that might be too complex for traditional methods. This can reduce the chances of rejection and help ensure better outcomes. Algorithms also improve how organs are allocated by predicting which patients are most likely to benefit, not just based on urgency but on expected long-term success. AI tools can also support mental health evaluations by assessing a patient’s emotional readiness and ability to follow care plans after surgery. After the transplant, AI helps create follow-up plans tailored to each patient’s condition. It can also predict how well someone might do based on both past and current health data. By tracking patterns and spotting problems early, AI can alert doctors to signs of graft failure before symptoms show up, allowing them to act quickly and prevent complications.Bhat Mamatha Rabindranath Madhumitha Chara Beatriz Sordi Simonetto Douglas A. 2023-06-01 Artificial intelligence, machine learning, and deep learning in liver transplantation https://www.sciencedirect.com/science/article/pii/S016882782300017X#sec2 Journal of Hepatology Liver Transplantation 78 6 1216–1233 10.1016/j.jhep.2023.01.006 0168-8278 Pros and Cons of AI Usage AI helps improve organ transplantation by improving matching between donors and recipients and helps to align characteristics for the most successful outcome. Current allocation systems lack the ability to prioritize in real time and continuously require updates.Deshpande Rajkiran 2024 Smart match: revolutionizing organ allocation through artificial intelligence https://pmc.ncbi.nlm.nih.gov/articles/PMC10933088/ Frontiers in Artificial Intelligence 7 1364149 10.3389/frai.2024.1364149 2624-8212 10933088 38481822 Using AI helps combat this by ensuring the best decisions are made when they are needed. Despite its benefits, AI in organ transplantation comes with several significant concerns. One major issue is algorithmic bias. AI models can amplify existing biases in the data they are trained on. If the training data includes disparities in race or socioeconomic status for example, the algorithm may unintentionally favor or disadvantage certain groups, leading to unfair treatment decisions. Another concern is the potential for AI to compromise human decision-making. If clinicians rely too heavily on algorithm-generated recommendations, they may overlook important context or ethical considerations that AI cannot fully address. Accountability is an issue when a doctor follows the decision-making of AI systems. There is a line between using AI as a tool and relying on it for medically significant decisions. Data privacy is also a major challenge. AI systems often need access to large amounts of sensitive patient information, and without strong protections, there is a risk of misuse that could violate patient confidentiality. Additionally, many AI models, particularly deep learning systems, operate as "black boxes,"Drezga-Kleiminger Max Demaree-Cotton Joanna Koplin Julian Savulescu Julian Wilkinson Dominic 2023-11-27 Should AI allocate livers for transplant? Public attitudes and ethical considerations https://pmc.ncbi.nlm.nih.gov/articles/PMC10683249/ BMC medical ethics 24 1 102 10.1186/s12910-023-00983-0 1472-6939 10683249 38012660 meaning their internal decision-making processes are not easily interpretable. This lack of transparency can make it difficult for clinicians to understand or trust the rationale behind a recommendation and it can also make it difficult for a patient to understand what is being recommended to them as well. If patients do not fully grasp how AI is used in their care, their ability to give informed consent on life-altering procedures is negatively affectedSalybekov Amankeldi A. Yerkos Ainur Sedlmayr Martin Wolfien Markus 2025-04-17 Ethics and Algorithms to Navigate AI’s Emerging Role in Organ Transplantation https://www.mdpi.com/2077-0383/14/8/2775 Journal of Clinical Medicine en 14 8 2775 10.3390/jcm14082775 2077-0383 12027807 40283605 MDPI. Conclusions Organ allocation is a good case study for professional ethics, as it features exaggerated versions of the pressures that exist in most fields. To complete organ allocation ethically, employees must be able to prioritize patient good while under both time pressure and pressure from executives at hospitals and organizations. Avoiding the pitfalls of line-skipping will require employees of hospitals and organizations to work together and prioritize patient welfare, rather than financial incentives. The New York Times investigation itself relied in part on conversations with both current employees of these organizations and employees who had left due to ethical concerns. This case also highlights the need for effective oversight organizations, and the responsibility that regulators have to investigate thoroughly and respond with appropriate action. References