Exploiting Machine Learning Bias: Predicting Medical Denials

Abstract

For a large healthcare system, ignoring costs associated with managing the patient encounter denial process (staffing, contracts, etc.), total denial-related amounts can be more than $1B annually in gross charges. Being able to predict a denial before it occurs has the potential for tremendous savings. Using machine learning to predict denial has the potential to allow denial-preventing interventions. However, challenges of data imbalance make creating a single generalized model difficult. We employ two biased models in a hybrid voting scheme to achieve results that exceed the state-of-the art and allow for incremental predictions as the encounter progresses. The model had the added benefit of monitoring the human-driven denial process that affect the underlying distribution, on which the models’ bias is based.