From Health Data Management
magine this scenario: A patient named John has waited 5.5 years for a much-needed kidney transplant. One day, he learns that a deceased donor kidney is available and that he is the 153rd patient to whom this kidney was offered.
Clearly, this is not a “high-quality” organ if it was declined by 152 patients or the clinicians treating them. But because John has been waiting a long time for a new kidney, should he accept or decline the kidney? And can analytics and machine learning help make that decision easier?
Currently, that decision is usually made by John’s doctor based on a variety of factors, such as John’s current overall health status on dialysis and a gut instinct about whether (and when) John will get a better offer for a healthier kidney.
If John is young and relatively healthy, the risk of prematurely accepting a lower-quality kidney is future organ failure and more surgeries. If John’s health status is critical and he rejects the kidney, he could be underestimating how long it will take until a higher-quality organ is available. The decision could be a matter of life or death.
John’s dilemma isn’t unique in the world of kidney transplantation, where current demand outpaces supply. Since 2002, the number of candidates on the waiting list has nearly doubled, from slightly more than 50,000 to more than 96,000 in 2013. During the same time, live donation rates have decreased. Complicating this problem of supply and demand is an unacceptably high deceased donor organ discard rate, as much as 50 percent in some instances.