⚠️ Important Disclaimer
ML predictions are statistical indicators only, not accusations of fraud. These providers exhibit prescribing patterns similar to confirmed fraud cases, but there may be legitimate medical explanations. High-volume pain specialists, for example, may appear flagged due to medically appropriate opioid prescribing. Always consider clinical context.
1,077,354
Providers Scored
4,183
ML Flagged (≥80%)
76.5%
Known Fraud Recall
83.0%
Model Precision
How the ML Model Works
Training Data
- • 281 confirmed fraud cases from the OIG LEIE exclusion list cross-matched to active Medicare prescribers
- • 1,077,354 total providers with ≥50 claims in 2023
- • 20 prescribing features per provider including opioid rates, costs, brand preferences, specialty-adjusted z-scores, and drug combination patterns
Model Performance
- • Precision: 83.0% — 83% of flagged providers match fraud patterns
- • Recall: 66.6% — catches 67% of known fraud cases
- • F1 Score: 0.738 — harmonic mean of precision and recall
- • 5-fold cross-validation on held-out data
Model: BaggedDecisionTrees ensemble with 20 trees. Trained with oversampled fraud cases and reservoir-sampled negatives. See full methodology.
Risk Tiers
785 providers
Very High (≥95% ML confidence)
Strongest match to fraud patterns
1,215 providers
High (85-94% ML confidence)
Strong match to fraud patterns
0 providers
Elevated (80-84% ML confidence)
Notable pattern similarities
Highest ML Fraud Scores
Top 100 providers ranked by ML fraud probability. These providers were not in the LEIE exclusion list — they are new predictions.
Top Flagged Specialties
Top Flagged States
ML vs. Rule-Based Scoring
OpenPrescriber uses two complementary fraud detection approaches:
📋 Rule-Based (10-Component Score)
- • Hand-tuned thresholds and z-scores
- • Transparent and explainable
- • Good at catching extreme outliers
- • View flagged providers →
🤖 Machine Learning (This Page)
- • Trained on confirmed fraud cases
- • Finds non-obvious pattern combinations
- • Better at detecting subtle fraud
- • Identifies providers rules miss
Related Resources
Data from CMS Medicare Part D Prescriber Public Use File, 2023. Fraud labels from OIG LEIE. ML predictions are statistical indicators, not accusations.