Methodology
How we identify unusual billing patterns across 522,900 Medicaid providers.
Important Disclaimer
This dashboard identifies statistically unusual billing patterns — it does not accuse anyone of fraud. There are many legitimate reasons a provider may be flagged: specialty drugs, per-diem billing, large multi-provider organizations, academic medical centers, and county health systems all routinely trigger statistical outlier detection. Every flag should be interpreted as a question (“Why is this unusual?”), not an answer. Proper investigation requires access to claim-level detail, clinical records, and subject-matter expertise that is beyond the scope of this aggregate analysis.
Data Source
Dataset: CMS Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files, specifically the “Top 500 Most Frequently Billed HCPCS Codes by Provider” file.
Source: data.medicaid.gov (CMS Open Data)
Records: 227 million rows covering the top 500 HCPCS codes billed by each provider, 2018-2024.
Coverage: All 50 states + District of Columbia — 522,900 unique providers, $967.5 billion in total spending, 16.5 billion claims.
Provider enrichment: NPIs cross-referenced against the NPPES National Plan and Provider Enumeration System (8.96M US healthcare providers) for provider names, entity types, and taxonomy codes. Taxonomy codes translated to human-readable specialties using the NUCC Healthcare Provider Taxonomy Code Set (Version 25.1, 883 codes).
Limitations: Data is aggregated at the provider-state-HCPCS level (not individual claim lines). We cannot see individual patient encounters, diagnoses, or modifier codes. This limits the precision of volume-based detection methods.
Statistical Approach
Robust statistics: We use median and Median Absolute Deviation (MAD) instead of mean and standard deviation. MAD is resistant to the very outliers we're trying to detect — using mean/std would let a single extreme provider inflate the threshold and escape detection.
Code-specific benchmarks: Rather than comparing all providers against a single threshold, we compute separate benchmarks for each of 6,752 HCPCS codes. A dermatologist billing $200/claim is compared to other dermatologists billing the same code, not to pharmacies dispensing $5 prescriptions. Full percentile distributions (p10 through p99) are stored in the code_benchmarks table.
Multi-flag requirement: A provider must be flagged by at least 2 independent tests to appear on the watchlist. Single-flag providers are excluded except for high-confidence signals (LEIE exclusion). This dramatically reduces false positives — if each test has a 10% FP rate, the probability of 2+ false flags is 1%.
Detection Tests (13 total)
Spending
Flags providers whose total Medicaid payments exceed the state p99 (99th percentile).
Threshold: Total paid > state 99th percentile
Catches: Unusually large billing operations within a state
Compares each provider's average cost per claim against the national median for the same HCPCS code, using Median Absolute Deviation (MAD) instead of standard deviation for robustness.
Threshold: MAD score > 5.0 (5+ MADs above the median for their top HCPCS code)
Catches: Upcoding, billing at rates far above peers for the same procedure
Extends cost outlier analysis across all HCPCS codes a provider bills, weighted by volume. Providers whose volume-weighted average MAD score is extreme are flagged.
Threshold: Weighted MAD score > 3.0 across all billed codes
Catches: Providers systematically overbilling across their entire practice, not just one code
Volume
Identifies providers where the ratio of claims to unique beneficiaries is abnormally high.
Threshold: Claims/beneficiary > state median + 5 * MAD, AND > 100 claims/beneficiary
Catches: Overutilization — performing unnecessary services on the same patients
Calculates claims per working day (22 days/month) to identify physically impossible billing rates.
Threshold: Individual providers: >50 claims/day; Organizations: >5,000 claims/day
Catches: Phantom billing — submitting claims for services never rendered
Pattern
Flags providers who bill 90%+ of their claims under a single HCPCS code, suggesting a "mill" operation.
Threshold: >90% of claims under one code AND >1,000 total claims
Catches: Procedure mills (e.g., urine drug testing mills, therapy mills)
Compares each provider's E&M code level distribution (99211-99215) against their specialty peers in the same state. Upcoding patterns are among the most common findings in OIG program integrity reviews.
Threshold: Average E&M level > peer average + 0.5, chi-squared > 100, high-level code % > peer baseline + 20%
Catches: Systematic billing of higher-complexity office visit codes than justified — upcoding
Growth
Detects months where a provider's billing jumps >3x their median monthly billing.
Threshold: Any month > 3x the provider's median monthly paid amount
Catches: Sudden ramp-up in billing -- a pattern that warrants further review
Compares the last 12 months of billing to the previous 12 months.
Threshold: Second-half billing > 3x first-half billing
Catches: Rapidly growing billing operations
Flags providers who appear in only the last 2 years of data but already bill above the state median.
Threshold: Active ≤ 24 months AND total paid > state median
Catches: New providers who immediately bill at high volumes — potential shell companies
Temporal
Uses Cumulative Sum (CUSUM) control charts to detect the exact month when a provider's billing pattern underwent a permanent, sustained shift.
Threshold: Post-change mean > 3x pre-change mean, minimum 6 months of data
Catches: Abrupt regime changes in billing behavior, pinpointing the exact month
External
Cross-references all providers against the OIG List of Excluded Individuals/Entities (LEIE). Excluded providers are legally barred from billing federal healthcare programs.
Threshold: NPI match in LEIE database (82,714 excluded entities)
Catches: Providers billing Medicaid while officially excluded — potentially illegal
Risk Tiers
| Tier | Flags Required | Providers | False Positive Estimate |
|---|---|---|---|
| Highest Confidence | 5+ | ~1,700 | 0.001% (1 in 100,000) |
| Critical | 4 | ~3,900 | 0.01% (1 in 10,000) |
| High | 3 | ~11,300 | 0.1% (1 in 1,000) |
| Elevated | 2 | ~37,300 | 1% (1 in 100) |
| Moderate | 1 (high-confidence) | ~370 | LEIE match only |
False positive estimates assume test independence. In practice, some tests are correlated (e.g., high spending and cost outlier), so actual FP rates may be higher than shown.
Known Limitations
- Government entities: County health departments, state hospital systems, and public universities (e.g., “CITY OF NEW YORK”, “NYC HEALTH AND HOSPITALS CORPORATION”) are large by design and frequently trigger volume-based flags. They appear on the watchlist but are expected given their scale and mission.
- Per-diem and bundled billing: Facilities that bill per-diem rates (e.g., nursing facilities) may show high claims-per-beneficiary ratios that are normal for their billing model.
- Specialty drugs: Providers dispensing high-cost specialty medications will have legitimately high cost-per-claim figures.
- Fiscal intermediaries: Some NPIs represent billing intermediaries, not individual practitioners. They aggregate billing for many downstream providers, inflating their apparent volume.
- Aggregate data only: This dataset provides provider-level aggregates, not claim-line detail. We cannot assess medical necessity, verify services were rendered, or examine modifier codes.
- LEIE name matching: We match LEIE entries by NPI only. Providers who were excluded under a different NPI or before receiving an NPI may not be detected.
Explore the Analysis
National Watchlist
Browse all 54.6K multi-flag providers
Highest Confidence
1,700 providers with 5+ flags
Change Points
CUSUM temporal shift detection
Benford's Law
Leading digit distribution analysis
Impossible Volume
Billing velocity analysis
LEIE Cross-Reference
OIG excluded entities matched against our data