Imagine you are waiting for a kidney transplant. A kidney becomes available. Now you and your doctor must make a stark choice: accept it, even if the quality level is not ideal, or hold out for a better one that might last longer.
to your inbox.
We’ll send you one email a week with content you actually want to read, curated by the Insight team.
A kidney from a healthy young person who was killed in a car accident, for instance, will function for many more years than a kidney from a 75-year-old with high blood pressure. But when people turn down organs, they run the risk that a higher-quality one will not arrive for a long time.
“The question is, should you be accepting this kidney or not?” says Chaithanya Bandi, an assistant professor of operations at the Kellogg School.
That decision has ripple effects for others on the kidney transplant waiting list, which currently has about 100,000 patients. As potential recipients consider their choices, the clock is ticking. If too many people reject the organ, it may spoil within 48 hours. Then no one benefits.
A low-quality kidney “would have been acceptable to people much further down the list,” says James Schummer, an associate professor of managerial economics and decision sciences at Kellogg. But because the list is so long, “you never get a chance to ask those people.”
Is there a way to improve this system so that more people get the organs they desperately need and fewer kidneys go to waste?
“What kind of policies should we be using that would lead to the most good for the most people?” —James Schummer
In two new, unrelated studies, Bandi and Schummer explore how to improve the kidney allocation process. Bandi and his colleagues focused on helping patients and physicians decide whether to accept an organ by creating a mathematical model that uses historical data to predict how soon a kidney of a certain quality will come along.
Schummer examined the effect of changing the policy on turning down organs. People are currently allowed to refuse as often as they like, but what if they were gently encouraged to accept organs in borderline cases? Schummer found that overall, the patients on the kidney transplant waiting list would likely benefit from such a change. But the answer depends partly on how patients feel about risk—do they prefer to wait longer for a kidney or have less control over which kidney they accept.
In such a high-stakes situation, Schummer cannot offer easy answers. Even if the group as a whole fares better, the people at the front of the list will do worse since they will not be holding out for the best possible organ.
“This is the ethical dilemma that is always faced in rationing,” he says. “Any decision you make is going to hurt some people and help some others.”
Tough Choices for Organ Allocation
The organ allocation process is more complicated than first come, first served. And a new list is generated with each new kidney.
When a kidney becomes available, the nonprofit United Network for Organ Sharing (UNOS) produces an ordered list of possible recipients. Many factors are taken into account, including blood and tissue type compatibility, how long the patient has been waiting, whether the recipient’s hospital is near the donor’s hospital, and how long the patient could potentially live with a transplant. Patients whose bodies are particularly likely to reject organ transplants receive higher priority if an organ is a good match.
Once an offer is issued, the medical team has one hour to decide whether to accept.
Schummer likens the process to playing a lottery with ticket values that range from $500 to $10,000. Imagine that for every minute that you wait, you lose money. If you win a $500 ticket, you have to decide whether to cut your losses and take it or turn it down and wait for a better ticket to arrive. In the case of the waitlist patient, your health declines with time.
A Nuanced Answer
Knowing how long you might have to wait for a better option is a key part of this decision. Yet it is very hard to predict. This is partly because the answer depends on how the people ahead of you in line behave. If they reject organs frequently, you might get one faster. But if they are not very choosy, you could wait a long time.
Bandi, who became interested in hospital operations because of the poor healthcare system in his home country of India, wanted to help organ-transplant recipients make more informed decisions. So he and his collaborators, Nikolaos Trichakis of Harvard Business School and Phebe Vayanos of the University of Southern California, developed a computer model of the waitlist process.
The U.S. transplant system, called the Organ Procurement and Transplantation Network (OPTN), does list median waiting times on its website. But by developing a new online tool, Bandi hopes to deliver more fine-grained predictions that incorporate information about the patient’s kidney-quality preferences.
The researchers used historical data on kidney allocations in the U.S. from 2007 to 2013. They used the first half of the data set to “train” the model so it could estimate how long people at various queue positions would wait for kidneys of a given quality. The team then tested the model’s accuracy by comparing its results with the second half of the historical data set.
The model provides patients not only with average wait times but also with an understanding of the range of wait times that went into that average. For instance, an average wait time of 10 months could include only people who waited between 9 months and 11 months. But a 10-month average could also include people who waited between three days and seven years. Understanding that distribution could have an impact on people’s decisions about an organ.
Bandi and his colleagues are now testing their online tool at a hospital in Illinois. Physicians or patients enter information such as patient age, how long they have been waiting, and the desired kidney-quality level. The software then provides estimated wait times. The researchers plan to incorporate feedback and release the tool to the public by June.
One caveat is that the software assumes that future patients’ preferences for kidney-quality levels will be similar to those of past patients’. Those patterns could change with shifting health care options and costs. For instance, if dialysis becomes cheaper, patients may be willing to wait longer for a transplant. But the team should be able to adjust the model as more data flow in, Bandi says.
Schummer investigated the system from another angle. He wondered what would happen if policymakers adopted new guidelines to reduce the number of times patients and physicians reject organs and thus the number of organs that spoil.
“The fact that we allow all these people to defer is what’s causing the spoilage problem,” he says.
Forcing people to accept kidneys is not realistic, Schummer says. But consider cases where doctors or patients are on the fence. What if they were encouraged to go ahead with the transplant? Overall, would such a policy benefit patients on the waiting list by shortening average wait times, increasing the years of life added, and reducing the number of spoiled organs?
Schummer created a mathematical model to test this scenario. He found that if patients were generally willing to take certain risks—that is, they did not mind giving up some control over the organ’s quality in exchange for shorter average wait times—the overall effect was positive. In that situation, “the queue as a whole clearly benefits,” he says.
But if patients disliked taking those risks, the answer was more complicated. In certain scenarios, people ranked lower on the list did not necessarily benefit even if higher-ranked patients accepted organs more frequently. That is because those lower-ranked patients may not want to be nudged into taking a lower-quality organ, even if the queue’s average wait time is shorter.
Still, these situations are rare, Schummer says. Overall, “it’s not likely that you’re going to end up making things worse,” he says.
The research cannot address whether this strategy would be ethical, however. After all, even if people on the list benefit on average, patients at the front of the line would suffer disproportionately because they would be taking lower-quality kidneys. Ultimately, Schummer says, the decision is up to physicians and policymakers.
These questions are not entirely theoretical to Schummer. Several years ago, his 3-year-old niece underwent a heart transplant. During his research, he recalled the emotional toll the operation took on her parents.
“Could we be doing better?” he asked himself. “What kind of policies should we be using that would lead to the most good for the most people?”
Schummer, James. 2016. “Influencing Waiting Lists.” Working paper.
It’s not just subject-matter expertise, according to a new study.
Coworkers can make us crazy. Here’s how to handle tough situations.
Plus: Four questions to consider before becoming a social-impact entrepreneur.
Finding and nurturing high performers isn’t easy, but it pays off.
A Broadway songwriter and a marketing professor discuss the connection between our favorite tunes and how they make us feel.