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Data Mining Snares Health Insurance Fraud Neil Versel , Information Week, November 15, 2011. Shipi Kankane Prashanth Nakirekommula. The news . Applying analytics and risk- management capabilities to health insurance through LexisNexis data platforms.

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data mining snares health insurance fraud neil versel information week november 15 2011

Data Mining Snares Health Insurance FraudNeil Versel,Information Week, November 15, 2011

ShipiKankane

PrashanthNakirekommula

the news
The news
  • Applying analytics and risk- management capabilities to health insurance through LexisNexis data platforms.
  • Databases on 250 million people in the U.S. sampled from 35 billion public records.
  • Data analysis using its supercomputer platform, which is built on top of open-source platform (High Performance Computing Cluster)
  • Allows for fast queries of "massive amounts of big data.”
lexisnexis healthcare solution
LexisNexis HealthCare Solution
  • Earlier Situation: Pay and Chase model - insufficient and unsustainable
  • Current Solution:
    • Proactive - identifies and mitigates fraud throughout business workflow
    • Identify fraud patterns and risk indicators as they emerge.
slide4

Predictive Modeling

Metrics

Scores

Detect inherent risks

Traditional Method:

Single claim

Claim Edit

No pattern identification

predictive analysis
Predictive Analysis

Definition: Predictive Analysis translates data into descriptive or predictive models using various forms of statistical analysis techniques

  • Classification and Regression Trees (CART) Chi Square Automatic Interaction Detection (CHAID) . 
  • Linear and logistic regression models
  • Analysis of variance
  • Discriminate analysis.
benefits
Benefits
  • Early detection of fraud, waste and abuse
  • Prioritized results with fewer false positives
  • Alerts concerning adverse changes in the status of

individuals or entities

  • Consistent control over risk, quality and costs using automated screening and monitoring
  • Lower claims losses, better financial management than traditional “post-payment only” methods
ibm spss modeler
IBM SPSS Modeler
  • Understand your data – Patient demographics, Hospital Information etc.
  • Determine your population makeup – Sampling issues, representativeness
  • Discover relationships in your data – Relations between various variables identified in Step 1
  • Build a model – Rule induction and based on step 3
  • Use model against actual records – Test for predictive power
  • Identify anomalies – Outliers
other vendors
Other Vendors
  • SAS datamining tool – tree based model , multianalytics approach
  • IBM SPSS modeler – rule induction
references
References
  • Neil Versel , Data Mining Snares Health Insurance Fraud,  InformationWeek , November 15 ,2011
  • LexisNexis® Health Care Solutions for Fraud, Waste and Abuse Prevention retrieved from lexisnexis.com
  • HPCC Systems (www.hpccsystems.com)
  • IBM Software Business Analytics, IBM Corporation, May 2011