Enriching Medicare Severity-Diagnosis Related Group (MS-DRG) Payments for better Service to inpatients using ANFIS

Abstract

Variations in the cost for the same diagnosis among different hospital providers is a great concern to the public at large. With huge amounts of data being availed every second, utilising the data for the benefit of the society is commendable. In this research a neuro-fuzzy approach is proposed for Medicare payments data. Machine learning clustering algorithms on neuro-fuzzy results are compared to understand the variations in price for same treatment and diagnosis among different healthcare providers. Cluster analysis has been applied in various domains to help reveal hidden structures. Cluster analysis has not been well exploited in healthcare claims datasets, the reason being that healthcare expenditure data is highly skewed which make analysis complicated. The Inpatient charges is a large dataset that has 163065 and 12 attributes describing amounts paid by Centers for Medicare and Medicaid Services (CMS) to different healthcare providers using different Diagnostic Related Group (DRGs).