© 2010 IBM Corporation© 2011 IBM Corporation

September 6, 2012

NCDHHS FAMS Overview for Behavioral Health Managed Care Organizations

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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.

•FAMS 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

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Background

• Why we are here today:– http://www.wral.com/news/local/

wral_investigates/video/11126237/#/vid11126237

– http://www.wral.com/news/local/wral_investigates/video/11130897/#/vid11130897

– http://www.wral.com/news/local/wral_investigates/video/11356150/#/vid11356150

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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

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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

Intellectu

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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?

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Behavior modeling uses analytical methods to select outliers that they must be

investigated Behavior Population

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”?

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Analyzes healthcare claims for mathematical anomaliesUsed after claims are paid to enable investigators to focus on the right providersCan 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

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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.

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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

xProvider Recipient Date Service Paid

12345 1001 1/7/2012 90801 $80.00

54856 2376 1/8/2012 H2022 $225.00

97256 1400 1/10/2012 90806 $75.00

= Analysis ProfilesPeer Group Claims DataFAMS Model

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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

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Visualization Analysis

• Geospatial Mapping• Peer Group Segmentation• Graphing• Charts• Parallel Coordinates