Knowledge Discovery From Massive Healthcare Claims Data Varun Chandola, Sreenivas Sukumar, Jack Schryver Presented by Anatoli Shein (aus4@pitt.edu)

Motivation: US health care 2008: 15.2% of GDP 2017: 19.5% of GDP Anatoli Shein 5/25/2018

Goal: Improve cost-care ratio Improve healthcare operations. Reduce fraud, waste, and abuse. Anatoli Shein 5/25/2018

Big Data Analytics in HealthCare Anatoli Shein 5/25/2018

Big Data in HealthCare Categorized Anatoli Shein 5/25/2018

Data quality and availability Clinical Data, Behavior data, and Pharmaceutical Data: Useful but unavailable Anatoli Shein 5/25/2018

Data quality and availability Health insurance Data Available but needs preparation Anatoli Shein 5/25/2018

State of the Art Analytics for Massive HealthCare Data: Network analysis Text mining Temporal analysis Higher order feature construction Anatoli Shein 5/25/2018

Health Insurance 85% of Americans have it It’s data is stored to : Track payments Address fraud Address economic challenges. Strong analytic insight into healthcare. Anatoli Shein 5/25/2018

Health Insurance Data Model Fee-for-service model Provider -> Service -> Patient -> Cost -> Justification -> Payor Anatoli Shein 5/25/2018

Data Maintained for Operation Claims information Patient enrollment and eligibility Provider enrollment Anatoli Shein 5/25/2018

Challenges and Opportunities Fraud Waste Abuse Anatoli Shein 5/25/2018

Fraud Billing for not provided services Large scale fraud Anatoli Shein 5/25/2018

Waste Improper payments Double payments Duplicate claims Outdated fee schedule Anatoli Shein 5/25/2018

Abuse Prospective payment system Upcoding Anatoli Shein 5/25/2018

Data Used Claims data (48 million beneficiaries in the US) from transactional data warehouses Provider enrollment data (from private organizations) Fraudulent providers (from Office of Inspector General’s exclusion) The rest are treated as non-fraudulent Anatoli Shein 5/25/2018

Claims Data Anatoli Shein 5/25/2018

Analysis Identification of typical treatment profiles Identification of costly areas Anatoli Shein 5/25/2018

Text Analysis, profile building Apache Mahout Hadoop Based technology Map Reduce Anatoli Shein 5/25/2018

Entities as Documents Document-term matrixes P(providers) B(beneficiaries) C(procedures) G(diagnoses) D(drugs) Ex: PG (providers/diagnoses) Anatoli Shein 5/25/2018

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Interesting find Some seemingly different diagnosis codes got grouped to the same topics Ex: Diabetes and Dermatoses Anatoli Shein 5/25/2018

Social Network Analysis Estimate the risk of a provider fraud before making any claims by constructing social network Anatoli Shein 5/25/2018

Provider Network Anatoli Shein 5/25/2018

Texas Provider Network Anatoli Shein 5/25/2018

Extracting Features from Provider Network Anatoli Shein 5/25/2018

Information complexity measure Most distinguishing features showed to be: Node degree Number of fraudulent providers in 2-hop network Eigenvector centrality Current-flow closeness centrality Anatoli Shein 5/25/2018

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Temporal Feature Construction By looking at provider data over time we can find anomalies Increase in number of patients Taking patients with conditions different from their past profiles Anatoli Shein 5/25/2018

Fraudulent Provider Detection Anatoli Shein 5/25/2018

Conclusions Introduced domain of “big” healthcare claims data Analyzed health care claims data on a country level using state of art analytics for massive data Problem was transformed to well known analysis problems in the data mining community Several approaches presented for identifying fraud, waste and abuse Anatoli Shein 5/25/2018

Thank you. Questions? Anatoli Shein 5/25/2018