1. Knowledge Discovery From Massive Healthcare Claims Data
Varun Chandola, Sreenivas Sukumar, Jack Schryver
Presented by Anatoli Shein

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

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

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

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

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

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

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

9. 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

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

11. Data Maintained for Operation
Claims information
Patient enrollment and eligibility
Provider enrollment
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12. Challenges and Opportunities
Fraud
Waste
Abuse
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5/25/2018

13. Fraud Billing for not provided services Large scale fraud
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14. Waste Improper payments Double payments Duplicate claims
Outdated fee schedule
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5/25/2018

15. Abuse
Prospective payment system
Upcoding
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5/25/2018

16. 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

17. Claims Data
Anatoli Shein
5/25/2018

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

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

20. 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

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

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

24. Provider Network
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5/25/2018

25. Texas Provider Network
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26. Extracting Features from Provider Network
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5/25/2018

27. 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

28. Anatoli Shein
5/25/2018

29. 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
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30. Fraudulent Provider Detection
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31. 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

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