Digitization of Prior Authorization in Healthcare Management Using Machine Learning

Abstract

Background: Prior Authorization is a widely used process by health insurance companies in the United States before they agree to cover prescribed medication under medical insurance. How-ever, the traditional approach includes long-length papers, leading to patients' delayed processing of their claims. This delay may deteriorate the patient’s medical condition. Also, due to man-made er-rors, there is a chance of incorrect decision-making on the claims. On the other hand, physicians are losing their time getting their prescribed medication approved. It is essential to reduce the wait time of patients and the tedious work of physicians for healthcare to be effective. This demands advanced technology that can boost the decision-making process of prior authorization methodology. Objective: This work aims to digitize the prior authorization process by implementing classification algorithms to classify the initial authorization applications into Accepted/Rejected/Partially Accepted classes. A web application that inputs prior authorization claim details and outputs the predicted class of the claim was proposed. Methods: Analyzed and collected significant features by implementing feature selection. Developed classification models using Artificial Neural Networks and Random Forest. Implemented model val-idation techniques to evaluate classifier performance. Results: From the research findings, generic medication cost, type of health insurance plan, addictive nature and side effects of the prescribed drug, patient physical qualities like Age/Gender/Current Medical condition are the significant attributes that impact the decision-making process in the prior authorization process. Then, implemented classifiers exhibited accurate performance on the Train and Test data. Amongst Artificial Neural Networks classification model portrayed higher accuracy. Further a confusion matrix was further analyzed for developed models. In addition, k-fold cross-validation and availed performance evaluation metrics were conducted to validate the model perfor-mance. Conclusion: Ameliorated Healthcare by removing time and location barriers in the Prior Authoriza-tion process while ensuring patients get quality and economical medication. The proposed web ap-plication with a machine learning predictive model as a backend automates the prior authorization process by classifying the applications in a few seconds.