Using Machine Learning to predict post-acute care and minimize delays caused by Prior

Abstract

Objective: 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 effects patient health. Our study implements predictive analytics for the early prediction of the PAC discharge disposition to reduce the deferments caused by prior authorization, the inpatient length of stay, and inpatient stay expenses. Methodology: 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 Results: 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. Conclusion: The early prediction of PAC discharge dispositions can reduce authorization process and simultaneously minimize the inpatient the PAC delay caused by the prior health insurance length of stay and related expenses.