A Study of the Influence of Textual Features in Learning Medical Prior Authorization

Abstract

In Brazil, a current health problem is the low capacity of meeting an increasing demand for medical services. As a result, some people have resorted to supplementary health care, which involves the operation of private health plans and health insurance. However, many health maintenance organizations (HMO) face financial difficulties due to unnecessary procedures, fraud or abuses in the use of health services. In order to avoid unnecessary expenses, the HMO began to use a mechanism called prior authorization, where a prior analysis of each user's need is made to authorize or deny the required requests. This work aims to study the influence of the use of textual features in automatic prior authorization evaluation, by using Text Mining, Natural Language Processing and Machine Learning techniques. Experiments were performed using several machine learning algorithms combined with textual features, increasing the performance of the automatic prior authorization. Results indicate not only the textual features influence to the evaluation of the automatic prior authorization process but also improved the prediction of the classifiers.