Sergei Ananyan, Ph.D. Healthcare Fraud Detection through Data Mining Your Knowledge Partner TM www.megaputer.com (c) Megaputer Intelligence

Your Knowledge Partner TM Problem Your Knowledge Partner TM

Problem US annual healthcare expenditures - $1.3 trillion Fraud detection through DM Problem US annual healthcare expenditures - $1.3 trillion Fraudulent transactions - $100 billion About one dollar in every ten is spent to pay for fraud

Problem Typical healthcare fraud schemes include Fraud detection through DM Problem Typical healthcare fraud schemes include Billing for services not actually performed Falsifying a patient's diagnosis to justify procedures that aren't medically necessary Misrepresenting procedures performed to obtain payment for non-covered services, such as cosmetic surgery "Upcoding" — billing for a more costly service than the one actually performed "Unbundling" — billing each stage of a procedure as if it were a separate procedure Accepting kickbacks for patient referrals Waiving patient co-pays or deductibles and over-billing the insurance carrier or benefit plan (involves both the provider and the patient)

Fraud detection through DM Problem Fraudulent activities CAN be discovered by professional investigators through in-depth manual analysis Yet, due to huge volume of patients, providers and transactions involved, human investigators can analyze only a small portion of available data This makes the task of fraud detection very difficult and results in the majority of fraud activities going undetected for extended periods of time

Challenge Overwhelming volumes of data Fraud detection through DM Challenge Overwhelming volumes of data 15,000 diagnoses 20,000 procedures 40,000 providers 1,000,000 patients 150,000,000 transactions per year Investigators need automated fraud detection tools

Fraud detection through DM Automated analysis? It is hard to separate fraud from errors - and often even from normal practices Automated tools can only catch most suspicious cases and bring them to attention of human investigators Final determination of fraud is a result of thorough and possibly very non-trivial investigation by human analysts Knowing what cases to focus their attention on, can dramatically increase effectiveness of human investigators

Your Knowledge Partner TM Automated fraud detection approaches Your Knowledge Partner TM

Approaches Rule-based approach Data Mining approach Hybrid approach Fraud detection through DM Approaches Rule-based approach Data Mining approach Hybrid approach New Future

Rule-based systems Are based on Some systems are available (IBM, ViPS) Fraud detection through DM Rule-based systems Are based on Documenting knowledge acquired from domain experts: storing signatures of known fraud schemes as a collection of business rules Using the developed business rules to comb historical data and discover inconsistencies Some systems are available (IBM, ViPS) BUT Can catch only a small portion of bad transactions matching ALREADY KNOWN fraud schemes Do not help in discovering new fraud schemes Are quite costly to purchase and maintain Cause investigators to seek more powerful tools

Data Mining approach New generation of self-learning tools Fraud detection through DM Data Mining approach New generation of self-learning tools Are based on Determining typical “normal” courses of treatment of patients with different diagnoses from the analysis of huge volumes of historical data Detect and red-flag anomalous deviations from the norm Output Lists of the most suspicious providers, patients and transactions for further manual investigation Can catch and help understand even unknown fraud schemes

Data Mining system CAN discover previously unknown fraud schemes Fraud detection through DM Data Mining system CAN discover previously unknown fraud schemes Provides tools for the visualization and interactive manipulation of the obtained results Requires minimum knowledge from domain expert Is cheap to maintain

Future: hybrid systems Fraud detection through DM Future: hybrid systems A Data Mining-based fraud detection system enriched with known domain-specific business rules can provide the best results Merging two approaches will require good domain expertise The resulting system can be used for real-time fraud prevention Both Rule-based and Data Mining tools can only suggest suspicious cases, which have to be further scrutinized by professional investigators

Your Knowledge Partner TM Developed data mining solution Your Knowledge Partner TM

DM system for fraud detection Fraud detection through DM DM system for fraud detection Megaputer MediCop™ - first dedicated Data Mining system for Fraud Detection Discovers and helps understand situations involving previously unknown fraud schemes Carries out an objective, data-drive and bias-free analysis Requires minimal intervention and maintenance from human analysts Provides interactive data visualization and aggregation simplifying further in-depth analysis of red-flagged data by human investigators Can further improve its performance by incorporating rules holding background knowledge coming from a domain expert – hybrid approach

Data Mining solution Megaputer MediCop™ Fraud detection through DM Data Mining solution Megaputer MediCop™ Was developed in a project with Medicaid Agency of the State of Missouri and Medstat Determines “anomalous” transactions, patients and providers based on the analysis of data only Demonstrated good accuracy in capturing “bad” providers (over 65%) Is currently being enriched with additional self-learning fraud detection scenarios

Typical users Health insurance companies HMOs Fraud detection through DM Typical users Health insurance companies HMOs Medicaid and Medicare agencies

Your Knowledge Partner TM Fraud Detection through data mining Your Knowledge Partner TM

Steps in fighting fraud Fraud detection through DM Steps in fighting fraud Fraud Detection Investigation of red-flagged cases Distilling rules for transaction monitoring PolyAnalyst can automate the analysis and significantly simplify the steps of Fraud Detection – MediCop module Investigation – Text OLAP and Link Analysis modules

Transaction Monitoring Fraud Detection system Fraud detection through DM Methodology Automated Transaction Monitoring System 3 Bad transactions Automated Fraud Detection system based on Data Mining 1 MediCop™ Distill new rules for detecting and preventing suspicious transactions in real time Enrich with domain- specific business rules 2 Investigate and detect exploited fraud scheme Text OLAP™ Catch fraudsters and recover the money

Your Knowledge Partner TM Fraud Detection: MediCop™ Your Knowledge Partner TM

Fraud Detection - MediCop Fraud detection through DM Fraud Detection - MediCop MediCop™ Determine the “norm” for different situations and identify deviations from the norm Raw data Distinguish and rank most anomalous patients for different groups of diagnoses, ages, etc. Identify anomalous providers and their profiles Suspicious providers Combine obtained results with relevant business considerations

MediCop output List of most anomalous patients for various diagnoses Fraud detection through DM MediCop output List of most anomalous patients for various diagnoses

MediCop output List of most anomalous providers Fraud detection through DM MediCop output List of most anomalous providers

MediCop output Groups of providers sharing many anomalous patients Fraud detection through DM MediCop output Groups of providers sharing many anomalous patients

Fraud detection through DM MediCop output Visualizing anomalous patients, providers and procedures

Fraud detection through DM MediCop output Different view of the same results – supports drill-down

Fraud detection through DM MediCop drill-down Drilling down to get a summary for anomalous provider

MediCop drill-down List of anomalous patients for this provider Fraud detection through DM MediCop drill-down List of anomalous patients for this provider

MediCop drill-down List of anomalous procedures for this provider Fraud detection through DM MediCop drill-down List of anomalous procedures for this provider

Fraud detection through DM MediCop drill-down Drilling down to get a summary for anomalous patient

Fraud detection through DM MediCop drill-down Distribution of procedures between different providers

Fraud detection through DM MediCop drill-down List of diagnoses and anomalous providers for a patient

Your Knowledge Partner TM Fraud Investigation: Text OLAP™ Your Knowledge Partner TM

Fraud detection through DM Fraud investigation Megaputer Text OLAP engine allows the user to directly answer numerous questions: What is the profile of a considered provider? Which providers perform unusual procedures for patients with a particular diagnosis? Do these providers perform unusual procedures for patients with other diagnoses? Which providers tend to perform more expensive procedures given the same patient diagnosis? What is the distribution of providers the patient had visited during the last year?

Fraud detection through DM Text OLAP drill-down Seeking providers performing unusual procedures for patients with a selected diagnosis

Your Knowledge Partner TM Fraud Investigation: Association analysis Your Knowledge Partner TM

Fraud detection through DM Association analysis Helps reveal and visually display groups of related providers sharing significant numbers of patients

Your Knowledge Partner TM Fraud Investigation: Link Analysis Your Knowledge Partner TM

Fraud detection through DM Link Analysis Visualize correlations between providers, patients, diagnoses and procedures

Your Knowledge Partner TM Benefits Your Knowledge Partner TM

Result Dozens of millions of transactions Response prediction Result A short list of the most suspicious transactions and providers Fraud Dozens of millions of transactions Automated focusing on a dramatically reduced number of records representing the most suspicious cases

Fraud detection through DM Benefits Dramatic cost reduction and increase in quality and speed of the analysis compared to manual processing Objective data-driven analysis and solid foundation for further in-depth analysis Discovery of even unknown fraud schemes Automated monitoring of known problems and timely discovery of newly developing issues Utilization of 100% of available data Easy and cheap to maintain solution Analytical foundation for future expansion in other fraud detection areas

Your Knowledge Partner TM Further Questions? www.megaputer.com Call Megaputer at (812) 330-0110 or write info@megaputer.com 120 W Seventh Street, Suite 310 Bloomington, IN 47404 USA Your Knowledge Partner TM