Does the UK’s liver transplant matching algorithm systematically exclude younger patients?
SubscribeSign in
Does the UK’s liver transplant matching algorithm systematically exclude younger patients?<br>Seemingly minor technical decisions can have life-or-death effects
Arvind Narayanan and Sayash Kapoor<br>Nov 11, 2024
103
11
Share
By Arvind Narayanan, Angelina Wang, Sayash Kapoor, and Solon Barocas<br>Predictive algorithms are used in many life-or-death situations. In the paper Against Predictive Optimization, we argued that the use of predictive logic for making decisions about people has recurring, inherent flaws, and should be rejected in many cases.<br>A wrenching case study comes from the UK’s liver allocation algorithm, which appears to discriminate by age, with some younger patients seemingly unable to receive a transplant, no matter how ill. What went wrong here? Can it be fixed? Or should health systems avoid using algorithms for liver transplant matching?<br>How the liver allocation algorithm works
The UK nationalized its liver transplant system in 2018, replacing previous regional systems where livers were prioritized based on disease severity.1 When a liver becomes available, the new algorithm uses predictive logic to calculate how much each patient on the national waiting list would benefit from being given that liver.<br>Specifically, the algorithm predicts how long each patient would live if they were given that liver, and how long they would live if they didn’t get a transplant. The difference between the two is the patient’s Transplant Benefit Score (TBS). Patients are sorted in decreasing order of the score, and the top patient is offered the liver (if they decline, the next patient is offered, and so on).<br>Given this description, one would expect that the algorithm would favor younger patients, as they will potentially gain many more decades of life through a transplant compared to older patients. If the algorithm has the opposite effect, either the score has been inaccurately portrayed or it is being calculated incorrectly. We’ll see which one it is. But first, let’s discuss a more basic question.<br>Why is predictive AI even needed?
Discussions of the ethics of algorithmic decision making often narrowly focus on bias, ignoring the question of whether it is legitimate to use an algorithm in the first place. For example, consider pretrial risk prediction in the criminal justice system. While bias is a serious concern, a deeper question is whether it is morally justified to deny defendants their freedom based on a prediction of what they might do rather than a determination of guilt, especially when that prediction is barely more accurate than a coin flip.<br>Organ transplantation is different in many ways. The health system needs to make efficient and ethical use of a very limited and valuable resource, and must find some principled way of allocating it to many deserving people, all of whom have reasonable claims for why they should be entitled to it. There are thousands of potential recipients, and decisions must be made quickly when an organ becomes available. Human judgment doesn’t scale.2<br>Another way to try to avoid the need for predictive algorithms is to increase the pool of organs so that they are no longer as scarce. Encouraging people to sign up for organ donation is definitely important. But even if the supply of livers is no longer a constraint, it would still be useful to predict which patient will benefit the most from a specific liver.<br>Sometimes simple statistical formulas provide most of the benefits of predictive AI without the downsides. In fact, the previous liver transplant system in the UK was based on a relatively simple formula for predicting disease severity, called the UK End-stage Liver Disease score, which is based on the blood levels of a few markers. The new system takes into account the benefit of transplantation in addition to disease severity. It is also more of a black box. It is “AI” in the sense that it is derived from a data-driven optimization process and is too complex to be mentally understood by doctors or patients. It uses 28 variables from the donor and recipient to make a prediction.<br>It seems at least plausible that this complexity is justified in this context because health outcomes are much more predictable than who will commit a crime (though this varies by disease). Follow-up studies have confirmed that the matching algorithm does indeed save more lives than the system that it replaced.<br>So there isn’t necessarily a prima facie case for arguing against the use of the algorithm. Instead, we have to look at the details of what went wrong. Let’s turn to those details.<br>The Financial Times investigation
In November 2023, the Financial Times published a bombshell investigation about bias in the algorithm. It centers on a 31 year old patient, Sarah Meredith, with multiple genetic conditions including cystic fibrosis. It describes her accidental discovery that the...