Using machine learning to support healthcare professionals in making preauthorisation decisions.

Abstract

Preauthorisation is a control mechanism that is used by Health Insurance Providers (HIPs) to minimise wastage of resources through the denial of the procedures that were unduly requested. However, an efficient preauthorisation process requires the round-the-clock presence of a preauthorisation reviewer, which increases the operating expenses of the HIP. In this context, the aim of this study was to learn the preauthorisation process using the dental set from an existing database of a non-profit HIP. Pre-processing data techniques as filtering algorithms, random under-sample and imputation were used to mitigate problems that arise from the selection of relevant attributes, class balancing and filling unknown data. The performance of classifiers Random Tree, Naive bayes, Support Vector Machine and Nearest Neighbor was evaluated according to kappa index and the best classifiers were combined by using ensembles. The number of attributes were reduced from 164 to 15 and also were created 12 new attributes from existing discrete data associated with the beneficiary's history. The final result was the development of a decision support mechanism that yielded hit rates above 96%. It is possible to create a tool based on computational intelligence techniques to evaluate the requests of test/procedure with a high accuracy. This tool can be used to support the activities of the professionals and automatically evaluate less complex cases, like requests not involving risk to the life of patients.