Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases.

ABSTRACT:

Background

The healthcare sector is an interesting target for fraudsters. The availability of a great amount of data makes it possible to tackle this issue with the adoption of data mining techniques, making the auditing process more efficient and effective. This research has the objective of developing a novel data mining model devoted to fraud detection among hospitals using Hospital Discharge Charts (HDC) in Administrative Databases. In particular, it is focused on the DRG upcoding practice, i.e., the tendency of registering codes for provided services and inpatients health status so to make the hospitalization fall within a more remunerative DRG class.

Methods

We propose a two-step algorithm: the first step entails kmeans clustering of providers to identify locally consi

SUBMITTER: Massi MC 

PROVIDER: S-EPMC7362640 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature