Using machine learning to minimize delays caused by prior authorization: A brief report

Abstract

A patient’s medical insurance coverage plays an essential role in determining the post-acute care (PAC) discharge disposition. The prior authorization process postpones the PAC discharge disposition, increases the inpatient length of stay, and affects patient health. Our study implements predictive analytics for the early prediction of the PAC discharge disposition to reduce the deferments caused by prior authorization and in turn minimizes the inpatient length of stay, and inpatient stay expenses. We conducted a group discussion involving 25 patient care facilitators (PCFs) and two registered nurses (RNs) and retrieved 1600 patient data records from the initial nursing assessment and discharge notes to conduct a retrospective analysis of PAC discharge dispositions using predictive analytics. The chi-squared automatic interaction detector (CHAID) algorithm enabled the early prediction of the PAC discharge disposition, accelerated the prior health insurance process, decreased the inpatient length of stay by an average of 22.22%. The model produced an overall accuracy of 84.16% and an area under the receiver operating characteristic (ROC) curve value of 0.81. The early prediction of PAC discharge dispositions can reduce the PAC delay caused by the prior health insurance authorization process and simultaneously minimize the inpatient length of stay and related expenses incurred by the hospital.