1. © 2010 IBM Corporation © 2011 IBM Corporation September 6, 2012 NCDHHS FAMS Overview for Behavioral Health Managed Care Organizations

2. 2 2 What is FAMS? Source:If applicable, describe source origin IBM Fraud and Abuse Management System (FAMS) Developed 16 years ago to allow users platform to use statistical scoring to evaluate peer groups of healthcare providers, and identify behaviors of fraud, waste, and abuse within the population using behavioral analysis. identification and ResearchFAMS fits into the investigative process in the identification and Research steps Implemented in North Carolina in 2010 in many service areas Identify Research Prioritize Investigate Resolve

3. 3 Background Why we are here today: – http://www.wral.com/news/local/wral_investigat es/video/11126237/#/vid11126237 – http://www.wral.com/news/local/wral_investigat es/video/11130897/#/vid11130897 – http://www.wral.com/news/local/wral_investigat es/video/11356150/#/vid11356150

4. 4 4 Investigators are challenged to find suspicious behaviors that are buried within the massive volume of healthcare claims  Payers are under great pressure to pay claims quickly  Fraudsters hide “bad” behaviors amongst the hundreds of millions of claims submitted annually  Investigators are overburdened with case loads and lack the resources and technology to find fraud fast  Payers adopted a ‘pay-and-chase’ strategy, pursuing cases based on tips received through fraud hotlines

5. 5 5 FAMS integrates technology, people and experience Seventeen years of experience in helping public and private payers detect healthcare fraud, waste, and abuse A library of over 8,500 algorithms that are the basis for specialty- specific models and successful implementations at over 40 clients worldwide A powerful analytics engine to quickly sort through large quantities of data using efficient algorithms and specialty-specific models Software Assets & Tools Intellectual Capital Consulting Expertise

6. 6 6 Using behavior modeling can help find suspicious behaviors faster  Which providers are behaving differently than others (in a suspicious way)?  How “good” or “bad” is a provider behaving, relative to other providers?  What it is “normal” behavior? Outlier Detection Predictive Modeling Data mining and segmentation  Which providers are likely to behave “badly” in the future?  What are the indicators that a provider’s behavior is getting “better” over time? “Worse” over time?  What are patterns of non-compliant (and criminal) behavior that I don’t know about?  If I catch a “bad” provider, how can I find out who else is behaving like that?  Are there groups of providers who are behaving in the same way?

7. 7 7 Behavior modeling uses analytical methods to select outliers that they must be investigated BehaviorPopulation What behavior is being identified? What data can be used to discover “behaviors”? What are the characteristics and relationships of those behaving in this way? What data can be used to identify “who”?

8. 8 8 Analyzes healthcare claims for mathematical anomalies Used after claims are paid to enable investigators to focus on the right providers Can be deployed at a complaint intake level to validate incoming information How FAMS if differentiates of a traditional approach? It detects multiple behaviors and schemes simultaneously moves analysis from claim level to provider level Shortens the time to investigate and recover funds Measures fraud scientifically IBM Fraud and Abuse Management System (FAMS) FAMS helps your investigators to pinpoint suspicious claims by using advanced analytics to identify “bad” behaviors Focus areas for consideration are discussed FAMS analysis techniques are used to determine who is behaving differently and how Investigators conduct further investigation Reports are reviewed and actions planned Providers are ranked and scored and categorized

9. 9 Key Terms Feature – A feature is a measured attribute of a provider, a feature is a query run against claims data for a provider, or a feature is a simple calculation of claims information. A feature is a numeric or categorical attribute of an entity used in entity profiling. In a profile, each numeric feature's value is translated to a score using the scoring associated with that feature. Model – A Model is comprised of groups of features that will be used to measure a specific Peer Group’s behavior. A model is a hierarchical construct of features within groups within the composite that defines the structure and content of a profile. – Models are simply the lists of features, they do not include claims or provider information Profile – Result of applying values measured over a specific time period against a model, and scoring those values against the peer group. A hierarchy of scores (composite, group, feature) developed for each entity within a peer group.

10. 10 Modeling and Profiling Models – Lists of questions to ask of the data for a peer group – Consists of 50 to 150 questions related to the peer group, that are organized into subgroups by type of service or hypothesis Peer Group Analysis Profile – Answers to the list of questions scored in relationship to the peer group from 1 to 1000 – Where analysis occurs – Created by running claims for a provider peer group, for a certain timeframe, against a model x ProviderRecipientDateServicePaid 1234510011/7/201290801$80.00 5485623761/8/2012H2022$225.00 9725614001/10/201290806$75.00 = Analysis Profiles Peer Group Claims Data FAMS Model

11. 11 FAMS Demo Slides FAMS features an easy to use, graphic user interface – Analytics capabilities include: Peer group profile visualization and reporting analysis – Composite level scoring and ranking – Feature and feature group scoring and ranking – Tracking change over time – Visualization analysis tools Claims reporting functions – Basic claims reporting functionalities – -Claim detail extraction – -Reporting on combinations of providers, diagnosis, procedures, recipients, etc… – Recipient drift reporting

12. 12 Visualization Analysis Geospatial Mapping Peer Group Segmentation Graphing Charts Parallel Coordinates