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# LeAnna Kent - Using Network Analysis to Detect Kickback Schemes Among Medical Providers

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Using Network Analysis to Detect Kickback Schemes Among Medical Providers

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### LeAnna Kent - Using Network Analysis to Detect Kickback Schemes Among Medical Providers

1. 1. Detecting Kickback Schemes Using Network Analysis LeAnna Kent 1
2. 2. About Me | 2
3. 3. Challenge Estimated 20% of all medical fraud is attributed to kickback schemes† Time and cost of investigations means we don’t have sufficient known cases for supervised modelling | 3 † https://www.gao.gov/assets/680/674771.pdf
4. 4. | 4 What is a Kickback? • Something of value being offered in return for a favorable action • In health care: • Referrals • Prescribing the use of specific drugs/equipment • As a result: • A large percentage of a doctor’s patients will see the same doctor and/or receive the same drug or equipment
5. 5. | 5 What are Networks? • Represent relationships between entities • Start with nodes (doctors) • Connect nodes based on associations (shared patients)
6. 6. Connecting Providers | 6 𝐽 𝐴, 𝐵 = 𝐴 ∩ 𝐵 𝐴 ∪ 𝐵 = 𝐴 ∩ 𝐵 𝐴 + 𝐵 − 𝐴 ∩ 𝐵
7. 7. Connecting Providers | 7 𝐽 𝐴, 𝐵 = 𝐴 ∩ 𝐵 𝐴 ∪ 𝐵 = 𝐴 ∩ 𝐵 𝐴 + 𝐵 − 𝐴 ∩ 𝐵 +
8. 8. | 8 Analyzing the Network
9. 9. | 9 Analyzing the Network
10. 10. | 10 Analyzing an Egonet
11. 11. | 11 Analyzing an Egonet
12. 12. Subgraph Strength Formula | 12 𝑆 𝑔 𝑛 = 𝑤 − ∆ 𝑛 𝑛 𝑤 𝑛 𝑝 𝑤 𝑛 weight of alter node 𝑛 𝑤 avg weight of alter nodes ∆ 𝑛 change in 𝑤 𝑛 alter nodes used 𝑝 maximum possible edges 𝑤 𝑛 weight of alter node 𝑛 𝑛 alter nodes used 𝑝 maximum possible edges 𝑆 𝑔 𝑛 = 𝑤 − ∆ 𝑛 𝑛 𝑤 𝑛 𝑝 ∆ 𝑛 change in 𝑤 𝑤 avg weight of alter nodes 𝑆 𝑔 𝑛 = 𝑤 − ∆ 𝑛 𝑛 𝑤 𝑛 𝑝 𝑆 𝑔 𝑛 = 𝑤 − ∆ 𝑛 𝑛 𝑤 𝑛 𝑝 𝑆 𝑔 𝑛 = 𝑤 − ∆ 𝑛 𝑛 𝑤 𝑛 𝑝
13. 13. Example | 13
14. 14. Algorithm Results | 14 Original Graph: Resulting Graph: • 6k nodes • 600k edges • Median degree of 60 • 1 connected component • 6k edges • 93% stopped after first node • Maximum degree of 16 • 1,316 connected components Run Time: < 2 min
15. 15. Interpreting Results Each egonet’s subgraph now has a score, by which they can be ranked | 15
16. 16. Interpreting Results Each egonet’s subgraph now has a score, by which they can be ranked When providing ranked list, remove any perfect subgraphs | 16 27% of nodes’ resulting egonets were redundant
17. 17. Interpreting Results Each egonet’s subgraph now has a score, by which they can be ranked When providing ranked list, remove any perfect subgraphs Can pair with supplemental information such as patients or dollars involved | 17
18. 18. Conclusions • Built graph where nodes were physicians, and edges were built based on shared patients and weighted by the Jaccard index • Focused on the egonet of each node independently • Applied algorithm to select an egonet’s subgraph with the relative strongest edges • Ranked subgraphs for investigation based on subgraph strength score | 18