NCATS MINIMIZING BIAS AND MAXIMIZING LONG-TERM ACCURACY, UTILITY, AND GENERALIZABILITY OF PREDICTIVE ALGORITHMS IN HEALTHCARE CHALLENGE

Investigators

Abstract

As AI/ML algorithms are increasingly utilized in healthcare systems, accuracy, generalizability, and avoidance of bias and drift appropriately come to the forefront. Bias can primarily surface in the form of predictive bias—algorithmic inaccuracies in producing estimates that significantly differ from the underlying truth; and/or social bias—systemic inequities in care delivery leading to suboptimal health outcomes for certain populations. These subtle shifts over time can cause degradation of the predictive capability of an algorithm, which can effectively negate the benefits of these types of systems in the clinic (and on already underserved / disenfranchised patient populations).