The IntegrityM Blog

Data Matching Techniques For Healthcare Fraud Detection

Healthcare fraud very seldom happens in a vacuum. Therefore, data matching — the ability to identify, match, and merge records that correspond to the same entities — is essential to healthcare data analysis. Using The NPPES Database For Data Matching The Centers for Medicare & Medicaid Services issues a unique identification number called a national […]

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Medicare Overpayment Recovery: Identifying and Calculating Overpayments

In fiscal year (FY) 2015, the error rate for the Medicare fee-for-service (FFS) program was 12.1 percent, or $43.3 billion. This result is an improvement over FY 2014, in which Medicare FFS had an improper payment rate of 12.7 percent, or $45.8 billion. Identifying Medicare overpayments is no easy task. The Medicare program is large […]

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GLȲD(Σ): Revolutionizing Healthcare Data and Statistical Analysis

What if there was a healthcare data analysis solution that could save you a significant amount of time in the sampling and extrapolation process, resulting in substantial cost savings, and therefore greatly increasing your return on every dollar invested into this process?  More results with less time and costs! Manually performing sampling and extrapolation can […]

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