1 / 11

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

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.

apria
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Mining Snares Health Insurance FraudNeil Versel,Information Week, November 15, 2011 ShipiKankane PrashanthNakirekommula

  2. 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.”

  3. 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.

  4. Predictive Modeling Metrics Scores Detect inherent risks Traditional Method: Single claim Claim Edit No pattern identification

  5. 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.

  6. Predictive Analysis

  7. 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

  8. 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

  9. Other Vendors • SAS datamining tool – tree based model , multianalytics approach • IBM SPSS modeler – rule induction

  10. Take Away Message

  11. 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

More Related