Budget estimates included in the rule say the effort is likely to raise nearly $72 million between 2014 and 2023 in recovered proceeds from fraud, while costing $12 million to implement, generating a net gain of $60 million for the state and federal governments.
Those figures are based on an OIG estimate that such data-mining is expected to bring back $6.90 for every dollar spent on the programs.
Numerous state employees and officials with the National Association of State Medicaid Fraud Control Units could not be reached for comment Friday.
Former New York Medicaid Inspector General James Sheehan said Medicaid officials have been pushing for rules like the one published Friday for more than a decade. He said the rule comes at a time when many Medicaid fraud-control units are evolving from reactionary organizations that respond to tips into groups that actively search for potential problems in the programs.
He said the timing of the change coincides with the expansion of Medicaid programs under the Patient Protection and Affordable Care Act, which provides federal funding for states that expand the eligibility criteria to allow more residents to enroll in the program.
“There will be a lot more risk of fraud because you have a rapidly growing program,” said Sheehan, who stepped down from New York Medicaid in 2011 to become chief integrity officer in the New York City Human Resources Administration.
The rule goes into effect June 17, but it doesn't eliminate the ban on federal funding for data-mining by Medicaid fraud control units. Rather, it allows the programs to receive a waiver from the ban after submitting a proposal for how they will do three things:
- Coordinate the data-mining with state Medicaid agencies to agree on enforcement priorities and ensure that the programs are subject to audit reviews;
- Communicate with state Medicaid agencies to ensure that data-miners are using the most up-to-date interpretations of state-specific Medicaid regulations; and
- Train Medicaid fraud-control unit staff on the advanced computer-analysis skills required for data-mining.
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