Medicaid Fraud Control Units were notably inefficient in 2015, recovering just $744 million of an estimated $53.6 billion in improper payments. These numbers are particularly concerning given the increase in Medicaid enrollment, the projected spending within the program over the next six years and the fact that improper payment rates continue to push skyward. Is this a one-year anomaly, or is an unmanageable Medicaid budget outpacing our existing tools for controlling fraud?
Two of three fraud cases in 2010 involved more than one scheme, including fraudulent billing, falsified patient records and kickbacks, according to a new report released by the Government Accountability Office.
Minnesota overpaid as much as $271 million over a five-month period on ineligible beneficiaries within the state's health insurance exchange program, according to a recently released audit.
The push for healthcare providers to adopt electronic health records has been fueled by promises of improved efficiency and usability, greater accessibility to health information, and in some cases, better patient care. Despite multiple warnings from experts, researchers and government agencies, fraud vulnerabilities still exist within current EHR systems, leading to improper billing, and in some cases, brazenly fraudulent records.
Big data and predictive analytics were supposed help Medicare prevent fraudulent payments the same way credit card companies deny suspicious charges. Fraud schemes still plague Medicare because the Centers for Medicare & Medicaid Services is too concerned about provider backlash to use the full force of claims data, according to an article published in Pacific Standard.
A multi-million dollar project between Michigan and Illinois has moved each state's Medicaid system to the cloud, which could offer better fraud detection mechanisms.
Predictive analytics is the hot new buzzword in healthcare. Now, it is changing the way payers to identify instances of fraud, waste and abuse. Increasingly, both public and private payers are turning to data analytics to identify high risk fraud trends, said Andrew Asher, senior fellow and director of data analytics at Mathematica in an exclusive interview with FierceHealthPayer: AntiFraud. However, payers like Aetna are gradually realizing the full impact of using healthcare claims data to accurately predict fraud schemes.
Enforcement officials in Jacksonville, Florida, are relying on data analytics to uncover fraud, waste and abuse, a method that has already paved the way for several multi-million dollar settlements this year, according to the St. Augustine Record.
As patients, we select physicians based on personality, wait times and location, without putting much thought into the possibilty of their involvement in a fraud or kickback scheme. Part of this is because we still don't have the necessary information at our fingertips, and as one recent article points out, it's difficult to identify physicians who might be involved in health insurance fraud.
Thousands of doctors in the Centers for Medicare & Medicaid Services' (CMS) Provider Enrollment, Chain and Ownership System (PECOS) list medical degrees from universities that no longer exist, all of which went unnoticed by CMS, and often the physicians themselves.