An experiment on the impact of predictive analytics on kidney offers acceptance decisions

Abstract

Because of the breadth of factors that might affect kidney transplant decisions to accept an organ or wait for another, presumably "better" offer, a high degree of heterogeneity in decision making exists among transplant surgeons and hospitals. These decisions do not typically include objective predictions regarding the future availability of equivalent or better-quality organs or the likelihood of patient death while waiting for another organ. To investigate the impact of displaying such predictions on organ donation decision making, we conducted a statistically designed experiment involving 53 kidney transplant professionals, in which kidney offers were presented via an online application and systematically altered to observe the effects on decision making. We found that providing predictive analytics for time-to-better offers and patient mortality improved decision consensus and decision-maker confidence in their decisions. Providing a visual display of the patient's mortality slope under accept/reject conditions shortened the time-to-decide but did not have an impact on the decision itself. Presenting the risk of death in a loss frame as opposed to a gain frame improved decision consensus and decision confidence. Patient-specific predictions surrounding future organ offers and mortality may improve decision quality, confidence, and expediency while improving organ utilization and patient outcomes.

Keywords: UNOS; clinical decision making; kidney; organ procurement transplantation network; time-to-next offer.