This study aims at exploiting Administrative Databases to identify potentially fraudulent providers. It focuses on the DRG upcoding practice, i.e. the tendency of coding within Hospital Discharge Charts (HDC), codes for provided services and inpatients health status so to make the hospitalization fall within a more remunerative DRG class. The model here proposed is constituted by two steps: one first step entails the clustering of providers, in order to spot outliers within groups of similar peers; in the second step, a cross-validation is performed, to verify the suspiciousness of the identified outliers. The proposed model was tested on a database relative to HDC collected by Regione Lombardia (Italy) in a time period of three years (2013 2015), focusing on the treatment of heart failure.

Data Mining Application to Healthcare Fraud Detection: Two-Step Unsupervised Clustering Method for Outlier Detection with Administrative Databases

MASSI, MICHELA CARLOTTA;F. Ieva;E. Lettieri
2019

Abstract

This study aims at exploiting Administrative Databases to identify potentially fraudulent providers. It focuses on the DRG upcoding practice, i.e. the tendency of coding within Hospital Discharge Charts (HDC), codes for provided services and inpatients health status so to make the hospitalization fall within a more remunerative DRG class. The model here proposed is constituted by two steps: one first step entails the clustering of providers, in order to spot outliers within groups of similar peers; in the second step, a cross-validation is performed, to verify the suspiciousness of the identified outliers. The proposed model was tested on a database relative to HDC collected by Regione Lombardia (Italy) in a time period of three years (2013 2015), focusing on the treatment of heart failure.
9788891915108
Data Mining, Healthcare Fraud, DRG Upcoding, Administrative Database
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1103634
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