Predictive analytics for detecting fraud in provider claims – Digital harbor
Provides savings in excess of three times by avoiding costly post-payment recovery
Minimizes fraud prevention and hence losses to the enterprises predictive analytics technology
Helps improve provider relations by narrowing the set of suspicious claims
Enhances efficiency of existing workforce by significantly reducing false positives predictive analytics companies
Is there a pattern in these frauds? Can there be few early leads?
Typically, such frauds are organized i.e. there is a chain of entities involved in achieving the means such as prescribing provider, DME, marketers/stringers (one who would hunt for beneficiaries) and beneficiaries. PPACA compliance typically frauds in DME are centered on stolen Medical Identities and false bills. If this information and link between provider, DME, people working to recruit beneficiaries and beneficiaries are rolled up to a meaningful pattern, it can offer a model to detect and nip such frauds in the bud. In essence, a summary of working relationship of providers with other players, beneficiaries and the flux of claims (rolled up for beneficiaries). This study can act as a much needed deterrence for such frauds that continue to siphon off tax-payer’s money. Provider Lifecycle Management
Consider a situation where a business provider has been excluded by the Office of Inspector General (OIG), CMS, a state’s Medicaid or Children Health Insurance program (CHIP); those who own that particular business entity may or may not be direct healthcare providers as they need not be physicians themselves. Predictive Analytics
Is there a possibility that there are other billing providers with Medicaid or Medicare where such individuals have a share of ownership?
Do such providers (sharing a degree of ownership) go scot-free without even coming under the radar of suspicion?
How can such a possibility be ruled-out where there are other providers owned directly by the excluded individual or blood relatives of such individuals? Compliance and Risk solutions
What stops such owners of excluded providers to open up a new business some time later (either owned by themselves or by their blood relatives) and start the billing again with Medicaid or Medicare?
2. Is it possible to implement a ‘Complete Exclusion’ without loopholes?
If these fraud events are closely looked into, it is found in a few cases that an excluded provider has come back again due to one of the following reasons – forgotten, ignored, and missed over time or lack of coordination among different agencies? In other cases “through proxy” or “through indirection”. This makes us think that these rejected providers are lured by the huge money pool lying in the Medicaid system that they somehow find and utilize the existing loopholes to make a re-entry. Provider enrollment application
OIG contains around 60 K excluded entities; System for award management (SAM) contains over 120 K excluded entities; Medicare revocations adds up to around 35 K; the Medicaid and Children’s Health Insurance Program State Information Sharing System (MCSIS) maintains around 8 K exclusions; all States’ Medicaid Exclusions would sum up to around 50 K excluded providers. There is a degree of overlap among these data sources. A conservative estimate of all these entities comes up to around 150 K excluded providers.
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