Pharmacy Fraud in Medicare: How We Detect It
Machine learning predictions identify statistical patterns consistent with confirmed fraud cases. A high ML score does NOT mean a provider is committing fraud — it means their prescribing patterns statistically resemble those of providers who were.
Read our methodology →Medicare fraud costs taxpayers an estimated $60 billion per year. We built a multi-layered detection system that combines statistical analysis, peer comparison, and machine learning to identify prescribing patterns consistent with fraud — all from public CMS data.
1,077,354
Providers Scored by ML
4,183
ML-Flagged Providers
6,706
High-Risk (Rule-Based)
372
OIG-Excluded Active
Our Three-Layer Detection Approach
Layer 1: Rule-Based Risk Scoring
Our 10-component risk scoring model evaluates every provider against their specialty peers. Components include:
- Opioid prescribing rate vs. specialty median (z-score)
- Population-level opioid percentile
- Cost outlier detection (per-beneficiary)
- Brand preference vs. peers
- Long-acting opioid rates
- Antipsychotic prescribing patterns
- Dangerous drug combinations (opioid+benzo)
- OIG exclusion list matching
- Low drug diversity (few unique drugs, many claims)
- Frequent fill patterns
Most Common Risk Flags
| Flag | Providers |
|---|---|
| extreme_opioid_vs_peers | 3,581 |
| high_la_opioid_vs_peers | 3,484 |
| 95th_pctile_opioid | 3,188 |
| very_high_opioid_vs_peers | 2,341 |
| 99th_pctile_opioid | 1,360 |
| 90th_pctile_opioid | 1,073 |
| elevated_la_opioid | 845 |
| high_antipsych_elderly | 676 |
| opioid_benzo_coprescriber | 523 |
| extreme_fills_per_patient | 469 |
Layer 2: Machine Learning
We trained a BaggedDecisionTrees model using 281 confirmed fraud labels from OIG enforcement actions. The model uses 20 features to identify prescribing patterns that resemble known fraud cases.
Model Performance
83.0%
Precision
66.6%
Recall (CV)
73.8%
F1 Score
Cross-validated on held-out fraud labels. Production recall on full LEIE match set: 76.5%.
The model flagged 4,183 providers out of 1,077,354 scored — a flag rate of 0.39%. This is intentionally conservative: high precision minimizes false positives.
Layer 3: OIG Exclusion Cross-Reference
We match all prescribers against the OIG List of Excluded Individuals and Entities (LEIE). Result: 372 excluded providers appear in active 2023 prescribing data. These providers are prohibited by federal law from billing Medicare.
Top ML-Flagged Providers
Providers with the highest machine learning fraud probability scores:
| Provider | Specialty | State | ML Score | Cost | Opioid % |
|---|---|---|---|---|---|
| Jerry Flynn | Family Practice | AK | 1.00 | $374K | 6.3% |
| Milla Karev | Geriatric Medicine | OH | 1.00 | $3.5M | 1.8% |
| Marcel Haulard | Family Practice | MO | 1.00 | $649K | 8.1% |
| Herman Chavis | Family Practice | NC | 1.00 | $1.8M | 10.4% |
| Jason Ham | Family Practice | AL | 1.00 | $4.0M | 8.8% |
| Joseph Scott | Family Practice | TX | 1.00 | $956K | 10.4% |
| Vadim Baram | Psychiatry | MO | 1.00 | $9.3M | 0.0% |
| Dumitru Sandulescu | Internal Medicine | MI | 1.00 | $1.1M | 9.4% |
| Jackie Maxey | Family Practice | KY | 1.00 | $3.4M | 6.7% |
| Jon Bowen | Family Practice | WV | 1.00 | $1.4M | 5.3% |
Case Studies: Dual-Flagged Providers
Providers flagged by both our rule-based system (high risk score) and the ML model represent the strongest signals. Here are examples:
Nurse Practitioner — Cottonwood, AZ
How Pharmacy Fraud Works
Common pharmacy fraud schemes that our system can detect signals of:
Phantom Billing
Billing for prescriptions never dispensed. Signal: high claims volume with few unique beneficiaries.
🔄 Upcoding
Substituting cheaper generics but billing for brand. Signal: abnormally high brand percentage vs peers.
🏭 Pill Mill Operations
High-volume controlled substance dispensing. Signal: extreme opioid rates, low drug diversity.
🚫 Excluded Provider Billing
Billing through another provider's credentials. Signal: LEIE match with active claims.
Limitations & Ethics
Our detection system identifies statistical patterns, not confirmed fraud. Important caveats:
- A high risk score or ML flag is not an accusation — many legitimate reasons can explain outlier patterns
- Pain management, oncology, and palliative care providers will naturally score higher on opioid metrics
- Rural providers may appear as cost outliers due to smaller patient panels
- Only law enforcement with full claims data can determine actual fraud
Our Goal
We aim to make public data accessible for transparency. Every data point comes from CMS public use files. We provide analysis — not accusations. If you believe our data contains errors, contact us.
Explore Fraud Detection
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