Anti-Fraud Products

1. Anti-Fraud Products

2. IntroductionProduct types I’ll talk about • EDI services and claims editors • “Predictive analytics” (versus pay and chase) • Fraud and abuse detection systems • Surveillance and utilization review system • Algorithms for detecting fiddly things (with explanations) • Case management system for following up in

3. EDI systems and claims editors • Virtually all health claims arrive as X12 837 transactions • HIPAA 5010 transaction set • Big providers plug in directly; small ones use claims intermediaries • Payors execute a set of deterministic rules to validate claims • Deterministic as opposed to probabilistic – straight-up yes or no • These are called claims editors • Bounced claims get sent back for repair • There claims editors available for providers to get the claim right in the first place

4. What does a claims editor do? • Checks a claim for compliance with deterministic rules • CMS issues a steady stream of national and local coverage determinations and post prices • Claims editors take those abstract rules and have a larger number of rules that execute against actual data • That works out to millions of rules – nine million just before the I10 conversion • Payor version picks the appropriate EDI code for the bounce message • Provider version offers advice on how to repair the claim if it finds a problem

5. Predictive analytics • In the anti-fraud context “predictive analytics” means predicting whether you’re likely to want to audit the claim • If yes, change the status to “suspend” and notify provider what supporting documentation you want • This is as opposed to “pay and chase” where audit is after payment • Implies you have a means of statistically rating both providers and claims on fraud, waster • These products need to be hooked into the claims adjudication system • In both predictive and pay and chase the provider gets asked for medical records documentation

6. Fraud and Abuse Detection systems • Several different functions • Surveillance and Utilization Review Subsystem (SURS) • Builds statistical provides of provider utilization • Algorithms to identify potential audit candidates • Case management for follow up with providers • Track and store medical record requests, communications, etc • Anti-fraud, waste, and abuse systems are mandated by Medicaid

7. Examples of FADS functionalities • Peer group profiling: Compares providers or members to their peers through the use of statistical profiling, and determines the outliers based on an unlimited number of behaviors • Algorithmic fraud detection: Reviews claims looking for collusive fraud that requires in-depth cross-claim and intra-claim logic to discover • ·        Provider spike activity: Compares each provider’s behavior to himself from week to week, looking for unusually high growth rates • ·        Long term care review: Reviews services provided to residents of long term care facilities, to determine those providers who are billing for services that should have been reimbursed by the LTC • ·        High cost members: Finds members who have high costs but do not appear to have justifiably severe diagnoses • ·        Browse and Search:  Provides a quick way for users to display all the claims in the data mart, and to filter and sort them for their current business purpose • ·        Analytical Report Library: Provides an open-ended library of supporting analytical reports to aid users in their research

8. Examples of FADS algorithms

9. The Friction of Anti-Fraud • You (usually) only want to audit when it’s a positive net value deal • CMS provides quantitative limits on audit contractors on how many records they can audit and how often they can contact individual providers • EDI produces difficult-to-understand error messages • Providers often need to phone payers asking for clarification • The obtuseness helps drive the market for provider side claims editors and for EDI operating rules for meaningful bounce messages • It’s expensive to send medical records • Often go by print/FedEx or fax due to combo of HIPAA & doctor signature requirements

10. Conclusion and a few final points • I find it unfortunate that many providers feel they need to buy their own claims editors to cope with the complexity of billing • Medical billing is mostly an honor system – the audits are spot checks for reasonableness and to ask for medical record backup • Nothing analogous to automated matching of 1099’s and W2’s in income tax • There is so much natural variance in individual physician practice that it’s really hard to pick up on abuse unless it’s outrageous