David-Ferguson SAS Presales

Copyright © 2010 SAS Institute Inc. All rights reserved.

Fraud in Healthcare – How technology supports this…

David FergusonPrincipal Pre Sales Consultant

SAS Institute

Mars 3, 20101

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Copyright © 2010, SAS Institute Inc. All rights reserved. 2

Agenda

Introduction- Overview of what we have heard from you

The process of Fraud Detection

- Business and Technology

The Data Issue

Hybrid approach to detcting fraud

Turning insight into Understanding

Demonstration of SAS Fraud Framework for

Health care

Q&A

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Copyright © 2010, SAS Institute Inc. All rights reserved.

What we have heard (Survey)

Data Mining, Analytics – what and how

The Data – challenges with that

Risk Management

Easy to use and Understand

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Scope of the Problem

The National Health Care Anti-Fraud Association (NHCAA) estimates

conservatively that 3% of all health care spending—or $70 billion—is

lost to health care fraud each year (100 times credit card estimates).

Other estimates by government and law enforcement agencies place

the loss due to health care fraud as high as 10 percent of our nation’s

annual health care expenditure—or a staggering $234 billion—each

year.

56 billion € (6% of health care spend) is lost every year to health

care fraud alone and more is probably lost to corruption.

http://online.wsj.com/

http://online.wsj.com/

http://www.ehfcn.org/index.asp

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Current Health Care Environment

Fraud, Waste & Abuse Perpetrators Far more sophisticated – organized, patient, sharing of rules

Leveraging multiple channels (providers & facilities) at the same time

Continuously evolving fraud strategies

Current Health Care Fraud Systems Most current detection systems act on claim level data alone

Investigations limited to individual members, providers and facilities

Focus on rules based approaches (linear and limited to known schemes)

Current Health Care Fraud Operations Limited to 3rd party systems and rules

No real proactive steps taken to combat fraud, waste, and abuse

Inefficiencies driven by amount of data and disparate sources

Fraud, Waste & Abuse

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Scope of the Problem – An Industry at Risk

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Claim Receipt &

Data Integration

Moving analytics and fraud detection upstream in the claims

lifecycle to become proactive, versus “pay and chase”

Alert Triage &

Case

Management

Adjudication

Processing

Adjudication

Integration /

Claim Edits

Fraud Detection

& Alert

Generation

Claim

Payment

The current SIU standard – “Pay and Chase”

Claim Receipt &

Data Integration

Alert Triage &

Case

Management

Adjudication

Processing

Adjudication

Integration /

Claim Edits

Fraud Detection

& Alert

Generation

Claim

Payment

Step 1: Pre-payment Fraud Detection

Trend in Health Care – Fraud Management

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Step 2: Pre-adjudication Fraud Detection

Claim Receipt &

Data Integration

Alert Triage &

Case

Management

Adjudication

Processing

Adjudication

Integration /

Claim Edits

Fraud Detection

& Alert

Generation

Claim

Payment

Step 1: Pre-payment Fraud Detection

Claim Receipt &

Data Integration

Alert Triage &

Case

Management

Adjudication

Processing

Adjudication

Integration /

Claim Edits

Fraud Detection

& Alert

Generation

Claim

Payment

Trend in Health Care – Fraud Management

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The Data Challenge

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The Data Challenge

What data do I need – is there a process and definition to support fraud analysis

How does data quality impact and what do I do about it

I have multiple, complex data sources

Is there a common definition for the data

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High-level Required Data Sources

Enterprise entity

reference data sources

• 18-36 months member static data and

static data changes

• 18-36 months provider & facility static

data and static data changes

• 12-18 months provider & facility variable

data (e.g., monthly charges, financials)

• 18-36 months line item claim data

• 18-36 months claim/medical bill payment

data

• 18-36 months Medical Insurance filings

• 12 months financial data

• 12 months non-monetary transaction data

(e.g., call center details)

Other internal and external

reference data sources

• 18-36 months employee static data and

static data changes

• 12 months employee processing data

(e.g., IDs that processed claims,

transactions, payments)

• Current client model details (e.g., positive

and negative scoring criteria)

• Current client model result sets (for

supervised modelling)

• Fraud, waste & abuse referral data

• Internal and external watch-lists and

indicators (e.g., known fraud red flags,

case management results)

• Medical records data

• 3rd party reference data

Any data formats acceptable to promote ease of transition for clients

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Lost in translation

―Predicted fraud score has a p value of .05, it is statistically significant‖

How do I utilise the smarts to help my SIU’s, decision making, the fraud prevention process

How do I know when things are bad

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The Analytical Challenge

Where do I start

What do I look for

What techniques should I use

Does it matter

How can I make it robust and industrialised

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Advanced Analytics are Required

Proactively applies combination of all 4 approaches at member, provider, facility, and network

levels

Hybrid Approach

Using a Hybrid Approach for Fraud, Waste & Abuse Detection

Suitable for known

patterns

Suitable for unknown

patterns

Suitable for complex

patterns

Suitable for associative

link patterns

Providers Members

Facilities Claims

FinancialsReferrals

Enterprise Data

Fraud

Flags 3rd Party

Data

Rules

Rules to filter

fraudulent claims and

behaviors

Examples:

• upcoding / correct

coding

• Value of charges for

procedure exceeds

threshold

• Daily provider billing

exceeds possible

Anomaly

Detection

Detect individual and

aggregated abnormal

patterns vs. peer groups

Examples:

• Ratio of € / procedure

exceed norm

• # procedures / provider

exceeds norm

• # patients from outside

surrounding area

exceeds norm

Predictive Models

Predictive assessment

against known fraud

cases

Examples:

• Like upcoding

behavior as known

fraud provider

• Predicted diagnosis

does not match actual

• Like provider/network

growth rate (velocity)

Social Network

Analysis

Knowledge discovery

through associative

link analysis

Examples:

• Provider association

to known fraud

• Linked members with

like suspicious

behaviors

• Suspicious referrals

to linked providers

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SNA Network Analysis Process Flow Diagram

Source Data to Investigator’s UI

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Process Flow

Alert Generation Process

SAS® SocialNetworkAnalysis

NetworkRules

NetworkAnalytics

AlertAdministration

BusinessRules

Analytics

AnomalyDetection

PredictiveModeling

Fraud DataStaging

IntelligentFraud Repository

Exploratory

Data Analysis &

Transformation

Operational

Data Sources

Case Management

Alert Management &BI / ReportingLearn and

Improve

Cycle

Providers

Members

Facilities

Claims

SAS® Fraud Framework

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Business Analytics Framework

Business Intelligence & Reporting

Data Quality & Integration

Analytics

SAS Fraud Framework

Detection & Alert Generation

Alert ManagementSocial Network

AnalysisCase Management

Health Care Solutions (sample)

Provider Fraud

CollusionPharmacy

Fraud

Health Care

Claims Fraud

Waste Control

Detection

Prevention


SAS Fraud Framework Demonstration

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Why SAS Fraud Framework for Health Care?

More prioritized Fraud, Waste, & Abuse cases identified

Including both previously undetected entities and networks and

extensions to already identified cases

Reduction in false positive rates

Hybrid approach reduces false positives by up to 10+ times over

traditional rules-based approaches

Improved analyst / investigation efficiency

Each alert takes 1/2 – 1/3 of the time to investigate due to data

aggregation and visualization

Provides alert logic and suggested path to initiate investigation

Significant increase in ROI per analyst / investigator

Provides the ability to apply Rules, Predictive Models,

and Anomaly Detection on linked data

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A Cross-Vertical Opportunity

Select results from SAS Fraud Analytics

• Large Blue Plan: Implemented SAS Fraud Predictive Models and

recognized $12 million in savings year 1. SAS also drove efficiency up

30% and recognized productivity gains of $200,000 per quarter.

• State HHS Medicaid: Implemented SAS Fraud Analytical tools to

recover $27M in overpayments and $14M in prevented payments – yr 1.

• Large US Insurer: Implemented SAS Fraud Predictive Models to

recognize a savings of $19 million in a single year identifying

overpayment of claims.

• Government Entity – WC Fund: Premium Evasion & Employer Risk

Analytics ($2M in additional premiums received annually through

targeted audits and education)

• Banking Customer: Implemented SAS Fraud Predictive Models and

decreased fraud losses by 30%.

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Case Study – Workers Compensation Insurer

SAS Approach

SAS subjected 4 years of historical data to the predictive capabilities of the SAS

Fraud Framework. Client investigators evaluated the solution results during a 3 week

validation period to identify incremental fraud detection at the claim and network levels,

reduction in false positives, and enhancements to investigative efficiency.

Highlights

• Advanced analytics drove 35%

better results than competition

• 36% lift on claim referrals

• 25% lift on network referrals

• Incremental estimated save of

$10.3M annually (for same # of

annual investigations)

• 57% lift over current process

• 45% correct hit rate on claims

• 67% correct hit rate on networks

• 100% of WC and GL claims

processed (~$16B claims)

Business Problem

A large US commercial insurer was incurring significant fraud losses across their lines

of business. The insurer engaged 3 vendors in a competitive pilot to determine the

solution that would provide the most lift over their current rules and models and

enhance effectiveness of the triage and fraud investigation teams.

Results

The key client decisioning factors for vendor selection include:

• Incremental Detection: $10.3M annually (for same number of investigations)

• ADVANCED ANALYTICS, allowing the appropriate prioritization of

investigator time and extraction of maximum value. Using SAS advanced

analytics, SAS performed 35% better than all other vendors.

• OPEN ARCHITECTURE, allowing client to become self sufficient vs. other black

box + services based approaches (self sufficiency can result in significant

annual savings on services costs.).

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Model Building and Training

Rules and Predictive Analytics

SAS Enterprise Miner:

• Generate and test the validity of new fraud

business rules and analytical models (e.g.,

decision trees, logistic regression, neural

networks)

• Run model and rule comparisons

• Deploy from Enterprise Miner to SFF

SAS Text Miner:

• Use textual data in generation of fraud models

• Configurable parsing, tagging, and extracting of

free text for use in fraud analytics

• Combine quantitative and qualitative data with

text analysis to improve predictions

Find relationships amongst phrases

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Model Building and Training

Rules and Predictive Analytics

SAS Model Manager:

• Repeatable process for registering,

testing and validating models

• Supports champion vs. challenger

capability to confirm best fit models

• Configurable analysis of how models

perform over time using lift charts,

result deviation, and response stability

SAS Business Intelligence:

• Trend analysis of fraud model and

detection program effectiveness

• Role-based, self-service interfaces

• Create a wide array of customized

reports and dashboards

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SFF Enterprise Case Management

Single portal for holistic view of fraud – can see both current and historical cases

Permission based access defines the level of viewing capabilities

Automated method to define fraud processes and design a consistent workflow

Create multiple, customized workflows for various cases types

Workflows require users to complete specific actions

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SFF Enterprise Case Management

Critical information in readily consumable format via the visual interface

Create customized reports and management dashboards

Interactive dashboard access on demand


Q&A / Next Steps

David-Ferguson SAS Presales

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