Author: Natasha Williams, Chief Operating Officer There is a wave of excitement in the industry surrounding artificial intelligence (AI), and not without reason. AI, with its ability to synthesize massive quantities of information at lightning speed, promises limitless possibility. I share in the collective optimism surrounding this new frontier. The detection and prevention of fraud,…
Data & Statistical Analysis
Data Analytics & COVID-19 Recovery: A Math-Free Explanation
Data Analytics is an indispensable tool being used in COVID-19 recovery efforts. Many of us take for granted that this is being used, but are not quite sure how. IntegrityM CTO Swathi Young and COO Natasha Williams break down the basics of Data Analytics and COVID-19 to shed light on the role this field plays….
So You’ve Detected an Outlier… Now What?
Fighting fraud, waste, and abuse in health care programs is labor intensive often requiring multiple experts including but not limited to data analysts and statisticians, certified coders, clinicians, auditors and fraud investigators. According to The National Health Expenditure Accounts (NHEA), U.S. health care expenditures rose by 2.8 percent in 2015 accounting for approximately 17.8 percent…
Statistical Bias in Health Care Fraud Detection and Prevention Systems
Organizations across the health care program integrity industry are using analytic solutions for fraud detection and prevention that intake and analyze data to signal the threat of fraud. These solutions can process huge amounts of data from multiple sources and remove much of the human element of potential fraud detection. When we think about these…
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…
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…
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…
