DATAMET RISK ANALYTICS ENGINE

Kleber GallardoCEO, Alivia Technology

8/20/2014

• , 8/16/2014: “Fraud and systematic overcharging are estimated at roughly $60 billion, or 10 percent, of Medicare’s costs every year, but the administration recovered only about $4.3 billion last year. The Centers for Medicare and Medicaid Services, which is responsible for overseeing the effort, manually reviews just three million of the estimated 1.2 billion claims it receives each year. “

• Medicaid waste fraud and abuse estimated 60-80 Billon

• US Healthcare National expenditure was 2.8 Trillion or $8,915 per person in 2012 – US HHS

What’s the Business Problem?

Global Healthcare Costs

Why is it Difficult?

• New Fraud Patterns created all the time

• Identify groups and networks of risky providers and members in the medical claims arena.

• Help investigators to do risk based investigations and focus on the most likely providers and members of fraud, waste and abuse.

• System automatically and continually learns patterns of risk based on known data fraud cases as well as continually learn on new cases as they are identified.

• This enables continuous feedback and improvement of the system and automated detection of risk cases based on these patterns.

How does data mining help?

Implementation Process

Pilot and Prod

Build Analytics Engine

Data Warehouse

Infrastructure

Identify and Access Data

Sources

Hire and Train Team

Collaborative Process

Business and Technology Identify Need

DataMet Risk Analytics Process

Stand AloneData sources

Databases, flat files of

different types

Preprocess dataCollect, clean, Aggregate and

store

Revise/refine Analysis

Data Analyst reviews output

Take action based on findings

Integrated Data Warehouse

Results feedback

Investigatorinterprets

results

Data analysis findings

DataMet Analytics EngineMachine learning,

statistics, KDD, data mining, risk scoring, and

others

DataMet Risk Engine Components• Rapid Miner and R for the analytics

• RapidMiner server for the dashboards, user management, resource sharing and scheduler

• Ontologies – Hierarchies of concepts

– Data Source Management

– Connection between data attributes to concepts

• Rule templates and Process Templates– Connect data with groups or sequences of algorithms with data sources

• DataGrid and Process Grids– Enable high speed access and processing of the data from various sources

• Social Network Visualization to show relationships between entities

• Geo Mapping to show locations of concentrations

• Designed to support Multilingual and Multi Currency capability

DataMet Sample Algorithms

• Benford’s Laws• Outlier Detections• Decision Trees• SVM• Attribute Ranking• Correlation Analysis• Death Match• Fuzzy Matching• Network Relationship Graphs

Risk Matrix

LOW

LOW

LOW

MEDIUM

MEDIUM

MEDIUM

MEDIUM MEDIUM

HIGH HIGH

HIGH

HIGH

HIGH

CRITICAL CRITICAL

CRITICAL

CRITICAL

MEDIUMMEDIUM

HIGH

LOW

MEDIUM

MEDIUM

HIGH

MEDIUM

SevereMajorMarginal Minor Moderate

Almost Certain

Likely

Possible

Unlikely

Rare

FINANCIAL OR SYSTEMIC RISK LEVEL

EVEN

T LI

KEL

IHO

OD

Risk Analytics Process

Apply Algorithms

Calculate Risk Score

Predictive Models

Detail Analysis

Data Driven Risk Assessment

ETL1

2

4

3

6

5

Cleanse

Risk RankedInsights

Output

Data Sources

DataMet

DataMet 21st Century Risk Analysis Process

Data Sources

Captured Resources

Collect Analyze ReportTrack

Results

Data Driven Risk Assessment

Improved ProcessesCollaboration

Management Dashboards

Learning

Tracking And Follow up

Improved Algorithms

Informed Decisions

Pharmacy Claims Example

Filter Narcotics

Calculate Risk Score with Analytics Engine

Filter Paid > $250/Year

Select Members with Risk Score greater than 25

Claims Members Paid 170M 2M 5B

Filter for year 201225M 0.9M $577M

14M 0.7M $541

0.67M 0.16M $9.5M

114K 6K $7.4M

5K 130 $1.4M

1

2

4

3

6

5

Get Paid Claims

• Capture indicators of risk

– Aggregates

– Rates of change

– Ratios

– Daily/Weekly/Monthly/Quarterly/Yearly

Metrics and Granularity

• Homeland Security

• Tax Records

• Health Care Claims

• Bank Transactions,…

Details Analysis of Risk Cases

Risk Analytics and Business

• Risk analytics is not an intuitive process

• It is the exercise of applying rules, algorithms and domain knowledge to data

• The data model is imbedded in the business processes and in the data that it produces

• Subject area expertise is essential to understanding the data and success of the project

• The blend of subject area knowledge and analytic expertise is a powerful combination

• DataMet can effectively identify groups and networks of risky providers

• DataMet helps investigators to focus on the most likely providers and members of fraud, waste and abuse.

• DataMet automatically learns patterns of risk based on known data fraud cases as well as well as new cases .

• DataMet enables continuous feedback and improvement of the system and automated detection of risk cases based on these patterns.

• DataMet is aimed at reducing the global waste and abuse which is estimated to be $600 billion/year US and globally

Conclusions

Questions?

Kleber Gallardo, CEOAlivia Technology

kgallardo@aliviatechnology.com+1781 354-8593