So your doctor has determined that you need an organ transplant. Now there's some important criteria that will help determine how fast you'll actually receive that transplant. But is the way the data is crunched actually serving patients as well as it can? Not exactly.
The system for determining who gets a transplant varies depending on what kind of organ the patient is waiting on. Say someone needs a lung transplant. If he or she is more than 12-years-old, the United Network for Organ Sharing requires the doctor to assign a numerical lung allocation score that supposedly determines the statistical likelihood of a survival post-transplant. This is based on a number of factors, from diagnosis, to body mass to how far the person can walk in six minutes. The higher the score, the more likely the person is to receive a transplant. But if the patient is under 12, it's simply a first-come, first-served situation that just takes blood type and proximity to the donor hospital into account. And that might not be the most fair way to determine who is privy to a life-saving procedure.
Now, using data for this purpose isn't necessarily wrong, but it's kind of a slippery slope. IEEE says:
Should we allocate organs to those who have waited the longest, those who are most likely to die while waiting for a transplant, or those who have the highest chance of survival? And what happens if the rules systematically exclude certain individuals?... First, prediction is not something to fear—we have always done it, it will always be imperfect, but technology can help us do it more accurately. Second, data analytics should not be a black box—we need to challenge its underlying assumptions and values. This will allow us to gain the benefits of data while avoiding both the tyranny of the bureaucrat and the tyranny of the algorithm.
Take for example Pennsylvania 10-year-old Sarah Murnaghan. She has cystic fibrosis and was originally denied a lung transplant. But if she were just a little bit older, her lung allocation score would have placed her higher on the priority list. Her parents felt it was unfair that just two data points determined that Sarah could not have a transplant, and a judge ruled in their favor. She was placed on the list with adults, and has since received a transplant and is currently recovering.
There's no way to remove bias entirely from stats, but there is a way to use data better. Is it always right to make medical decisions based on someone's likelihood of survival? Not exactly. That doesn't necessarily mean we should shy away from it. But maybe we should rethink it. [IEEE]
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