Who Gets the Organ?
In June 2009, Steve Jobs received a liver transplant at Methodist University Hospital in Memphis, Tennessee. He lived in Palo Alto, California. The transplant center closest to his home was at Stanford, where median wait times for a liver at the time exceeded three years. In Memphis, the wait was a fraction of that.
Jobs flew to Tennessee on a private jet, listed himself at the Memphis center, and received an organ within weeks of being placed on the waitlist. Nothing he did was illegal. He followed every rule. He simply knew something most patients on the waitlist didn't: the system has geography-shaped holes in it, and if you can move through those holes, you survive.
This article is about the system that made that possible, why most patients never learn how it works, and what it would take to change that.
The system everyone trusts and nobody understands
The United States organ transplant system is managed by UNOS, the United Network for Organ Sharing, under a federal contract from the Health Resources and Services Administration. The policy arm is OPTN, the Organ Procurement and Transplantation Network. For most purposes, UNOS and OPTN are the same entity operating the same system: a national infrastructure for matching donated organs with patients who need them.
Here is what most people assume: there is a national list. You get on it. You wait your turn. The sickest person gets the next organ.
Here is what actually happens: the list is not one list. It is dozens of overlapping local lists, governed by geography, managed by 56 separate Organ Procurement Organizations (OPOs), filtered through individual hospital acceptance criteria that vary enormously, and navigable primarily by people with resources.
How organs actually get allocated
When someone dies and their organs become available, the local OPO coordinates recovery. That OPO covers a specific geographic territory. Under the allocation framework that governed transplant for decades (and still shapes much of how the system operates in practice), the organ was offered first to patients listed at transplant centers within the local OPO territory. If no suitable local match was found, the offer expanded to a regional circle, then to a national pool.
This is the concentric circles model. Local first. Regional second. National last.
The rationale was logistical: organs deteriorate outside the body. A kidney can survive about 24 to 36 hours on ice (cold ischemia time). A heart, four to six hours. A liver, roughly 12. The shorter the distance between donor and recipient, the better the organ quality on arrival. Local priority was, in theory, a quality-of-care decision.
But there's a second-order effect the logistical argument doesn't address: donor supply and waitlist size are not evenly distributed across the country. Some OPO territories have high organ donation rates and relatively small waitlists. Others have massive waitlists and limited donor supply. The result is that median wait time for the same organ, at the same medical acuity, varies enormously depending on where you are listed.
The number that should bother you
For kidneys, median wait time in the United States ranges from under two years in some regions to over seven years in others. The same organ. The same clinical need. A fivefold difference determined almost entirely by geography.
For livers, the variation has historically been even more dramatic. Before the 2020 acuity circles policy change for liver allocation, some OPOs had median wait times under 90 days while others exceeded two years. A patient in rural Tennessee and a patient in New York City, both with identical MELD scores (the severity metric for liver disease), faced radically different odds of survival based on which transplant center they happened to walk into first.
This is not a secret. The data is published annually by the Scientific Registry of Transplant Recipients (SRTR). But it is buried in program-specific reports, formatted for administrators and researchers, not for patients who need to make a decision about where to list.
Each center plays by its own rules
Wait time variation is only part of the story. Each transplant center independently decides what organs it will accept for its patients.
When an OPO offers an organ, it goes out to transplant centers in sequence based on the match run (the ranked list of potential recipients). A center can accept or decline the offer. Conservative centers decline organs they consider suboptimal: older donors, donors with certain risk factors, organs with longer cold ischemia time. Aggressive centers accept a wider range of organs, reasoning that an imperfect organ now beats waiting for a perfect organ later.
From the patient's perspective, this means two things. First, your effective wait time depends not just on the supply in your OPO territory but on how aggressively your transplant center accepts offers. A patient listed at a conservative center in a supply-rich OPO might wait longer than a patient at an aggressive center in a supply-poor OPO, because the conservative center keeps declining offers that the aggressive center would take.
Second, these acceptance criteria are not standardized, not well-publicized, and not easy for patients to compare. The SRTR publishes center-level offer acceptance rates, but the data requires statistical fluency to interpret, and most patients are never told it exists.
Double listing: the loophole that isn't a loophole
UNOS does not prohibit patients from listing at multiple transplant centers. If you can travel to a second city for the required medical evaluation, you can add yourself to a second waitlist. You are now eligible for organs in two OPO territories simultaneously. Your effective odds improve immediately.
This is called double listing (or multiple listing), and it is perfectly legal. It is also expensive. Each transplant center requires its own evaluation: imaging, bloodwork, consultations, sometimes a full week of appointments. Travel, lodging, and the evaluation itself are rarely covered by insurance. Follow-up care may require returning to the distant center, or negotiating a transfer of care arrangement.
The result is that double listing is functionally available only to patients with the money to travel, the insurance to cover out-of-network evaluations (or the cash to self-pay), and the knowledge that multiple listing is even an option. A 2018 analysis in the American Journal of Transplantation found that multiply-listed patients had significantly higher household incomes and were more likely to be white. The mechanism is legal. The access is not equal.
The consultant class
Here is where the story of Steve Jobs stops being an outlier and becomes a pattern.
Wealthy patients and their families hire transplant consultants: professionals (sometimes nurses, sometimes epidemiologists, sometimes health care attorneys) who analyze the transplant system on the patient's behalf. These consultants do exactly what you'd expect: they study OPO-level supply data, center-level acceptance rates, regional wait times, donor demographics, and logistical constraints to identify the optimal listing strategy.
They identify which cities have the shortest waits for a given organ type. They evaluate which centers accept the widest range of donor organs. They calculate whether double listing in a second territory is worth the cost. They advise on timing, travel, and even which surgeons have the best outcomes for specific procedures.
None of this is illegal. None of it is secret in the sense that the underlying data is classified. The SRTR publishes it. UNOS policies are public. But the analysis requires expertise that most patients don't have and can't afford to hire.
The information asymmetry is the problem. Not the rules. Not the data. The fact that understanding the data, combining it, and acting on it requires resources that are distributed as unevenly as the organs themselves.
Reform is happening. Slowly.
It's worth noting that UNOS has been moving toward continuous distribution, a framework that replaces the hard geographic boundaries of the concentric circles model with a composite scoring system. For kidneys, continuous distribution launched in 2023. The composite score weighs medical urgency, time on waitlist, donor-recipient biological compatibility, and distance (as a continuous variable rather than a binary local/regional/national cutoff).
This is a meaningful structural improvement. It softens the geographic cliff effects that made gaming so profitable. A patient 251 miles away is no longer categorically behind a patient 249 miles away.
But continuous distribution doesn't eliminate geography as a factor; it smooths it. Distance is still a scoring component. OPO-level variation in donor supply is still real. And center-level acceptance behavior, the most opaque variable in the entire system, is unchanged by the new framework. A patient listed at a conservative center still faces a different effective waitlist than a patient at an aggressive center in the same city.
The reform reduces the magnitude of the advantage that geographic optimization provides. It does not eliminate it.
Who has the map
So the system is getting better. The bluntest edges are being filed down. But the core dynamic remains: the transplant system is navigable, the navigation requires analysis, and the analysis is available to people with resources.
A patient who walks into the nearest transplant center, trusts their coordinator, and waits is doing what the system expects. A patient who hires a consultant, studies SRTR data, evaluates center-level acceptance rates, and double-lists in an optimized second territory is also doing what the system allows. The difference between them is not character or deservingness. It is information.
There are roughly 104,000 people on the US organ transplant waitlist today. About 17 will die waiting before tomorrow. Some of those deaths are medically inevitable. Some are not. Some are a consequence of being listed at the wrong center in the wrong territory, without anyone telling the patient that "wrong" was even a category.
The data to make better decisions already exists. It's published. It's free. What doesn't exist, at least not in any form available to the average patient, is the analysis layer: the part where 50 variables across 22 cities get combined into a recommendation that a patient can actually use.
What if that analysis were available to everyone?
Next in this series: The Tool That Shouldn't Exist. A billionaire who needs a kidney calls an epidemiologist. Here is exactly what that epidemiologist does, and what it looks like when you automate it.