In the delicate and uncertain world of organ transplantation, for example, transplant surgeons occasionally resort to the game of the system in an effort to save their patient’s life. Because there are never enough donors to go around, patients are put on a transplant waiting lists and to prioritize a rating that measures the severity of their illness. This encouraged some doctors to be more liberal to order certain medical treatments or interventions that would bring their patients higher on the waiting directory – such as sending them to the Intensive Care Unit (ICU) even when they cannot be necessary.
UNOS, a non -profit organization that matches donor organs in waiting patients, “really stopped using the ICU as a factor in the hierarchy of liver transplant patients due to the abuse of this method,” he says, he says, he says, ” James SchummerKellogg Associate Professor of Directorates of Finance and Sciences that study the allocation of resources. “And when UNOS decided to stop, ICU treatments for patients with liver transplantation fell half. So this could be the” gambling “problem.”
Certainly, many of the doctors playing the system only tried to do their patients properly. But what if there was a way to correct these deformed motives? Schummer and Edwin Muñoz-Rodriguez From Mexico College came with an idea: Keep a small fraction of the available organs that would normally go to patients at the highest risk and offer them to patients with a lower risk. This distribution, economists believe, could reduce doctors’ motives to overcome their patients-while also increasing the chances that patients at higher risk will receive the organs they need.
To try out their theory, Schummer and Muñoz-Rodriguez created a mathematical model of patients, doctors, therapies and a “developer” responsible for distributing donors (such as UNOS). The model showed that a designer could always find a “sweet spot” where, even if an organs fraction was separated for patients with a lower risk, all patients in the system would be more likely to receive an organ-especially the most ill. In other words, the approach of researchers will always provide better results than the conventional method only to prioritize patients at higher risk.
“It’s a bit paradoxical: you pay some of the patients who don’t play the system,” says Schummer. “And at the end of the day, everyone is still coming forward.”
Stripes
Ordering additional medical care, such as an ICU stay, is not insignificant for patients to increase higher on the waiting list. “Being in the ICU is, honestly, quite unpleasant. You don’t want to be there if you don’t have to be,” says Schummer. And these patients are not difficult. Anyone who is on a transplant waiting list is already in poor condition. So what is the damage to doctors who play the system a little?
The damage comes from what Schummer calls “congestion results”. As the name suggests, these results occur when there is a crowd around a desired resource, making it the least accessible to everyone.
Schummer compares the waiting list of a transplant in circulation on a busy highway. Everyone is heading to the same destination – that is, taking a donor instrument – but some people are ahead of others. The most ill patients, however, should have a way to get there faster – something like a lane carpool. And indeed, they do it: just as cars with more passengers are rewarded with access to a less filled carpool lane, patients who are more ill are more priority.
But what happens if the individual movements started doing something unnecessary, such as taking the hitchhikers, so they could use the Carpool strip? Soon the Carpool strip would also be full and would not move at all. “This is the result of congestion,” says Schummer. The same would happen on the waiting list of the transplant: “The more you encourage people to improve their waiting position by taking these extra treatments, the more people are going to reach this lane and slow it down for everyone else.”
In addition, the percentage of truly patients receiving an organ is reduced. “If there are 100 people in the” Carpool Strip “, and only 50 organs, you still have 50 percent shooting,” Schummer explains. “But if only 50 people have just appeared, there are now 150 people in the lane. Your odds have just come down. ”
Change the game
There he enters the game. In the researchers’ model, the developer-that is, an organization such as UNOS that manages circulation in the transplant waiting list-obviously admits a certain number of organs for lower risk patients. This would be as if we were saying to all individual moving on the highway that a certain number of them – randomly selected – will be allowed to pull the shoulder and magnify.
“Now these patients with less shrinking can say,” I will stay in my lane because now I have a shot. “That’s why you balance this congestion while still favoring people in the” Carpool Strip ” – the really sick patients.”
Schummer and Muñoz-Rodriguez then adjust the amount of distribution to their model to consider how they can affect everyone in the system. In fact, they pretended to be the designer. By converting a fantastic selector that controls how many random cars will be allowed to magnify the shoulder, they could see how the standards changed in traffic. Would he get more congestion or less? Will more people reach their destination or not?
Finding the sweet spot
Schummer and Muñoz-Rodriguez have found that, in situations where many individual doctors face individual patients, there is always an ideal environment where the designer could reduce enough organs for patients with a lower risk to prevent doctors from playing the system. This adjustment also maximizes the number of patients with a higher risk that the organs really receive.
“This is not an obvious result,” says Schummer. “It may be ideal for not the diet and just allowing the game to take place because you will still take the instruments to the right people, but mathematically, you can’t.”
Researchers also adapt their model to see if their distribution approach would work in an environment that suits the real world. Doctors were grouped into larger “transplant centers” that have faced many patients at the same time. Schummer and Muñoz-Rodriguez found that not only did the distribution approach still produce better overall results, it worked even better than it did when individual doctors and patients competed immediately.
“This is a complex strategic situation because transplant centers have many patients to heal and they have to decide what to do with all this,” Schummer says. “But what is true is that you can adjust the distribution table even more towards the benefit of patients and avoid playing the system.”
In other words, the developer should not accidentally attribute quite a few patients with a lower risk of transplant centers compared to situations where individual doctors compete.
“This means that more organs that go to more sick patients,” Schummer explains. “That’s what you want.”
A path forward
Schummer emphasizes that theoretical models – even a lot of promising like his – cannot be applied immediately to the real world. “It’s a proof of the concept,” he says. “How do you adjust this model to any given application requires a lot more work, but this opens the door for other researchers to do so.”
The model also shows that when distributing rare resources such as organs, most competition is not always better – a result that contradicts conventional economic wisdom.
‘If there is a silver lining on it, it is that in this area [of organ rationing]The smallest competition between fewer transplant centers is actually better, “says Schummer.” It reduces the number of people playing the system. “
