# Healthcare Provider Exclusions by State: 45% of Fraud Risk Concentrated in Just 5 States .jpg) ## Executive Summary This study examines the geographic distribution of 7,212 healthcare providers excluded from federal healthcare programs across 2,802 cities in the United States. Our analysis reveals distinct geographic concentration patterns that defy simple population-based explanations. With 45% of all exclusions concentrated in just five states and one-third located in only 100 cities, these findings provide new insights into the geography of healthcare compliance risk. Our research identifies several states with disproportionately high exclusion rates relative to their population, with Ohio having more than double the expected rate based on population alone. These findings have significant implications for healthcare organizations, compliance officers, and regulatory bodies tasked with protecting program integrity and preventing fraud. ## Introduction: The Geography of Healthcare Exclusions The Office of Inspector General's List of Excluded Individuals and Entities (LEIE) is an important safeguard in the U.S. healthcare system. This database identifies healthcare providers barred from participating in Medicare, Medicaid, and other federal healthcare programs due to fraud, abuse, or misconduct. Despite its importance, few studies have explored the geographic distribution patterns of these exclusions. The consequences can be severe when a healthcare organization employs or contracts with an excluded provider. Penalties can reach $10,000 per claim plus three times the amount claimed for each service an excluded individual provides. These financial risks make understanding the geographic concentration of excluded providers essential for effective compliance programs. This report presents findings from a large-scale geographic analysis of healthcare provider exclusions, offering insights into regional risk factors and concentration patterns that should inform strategic compliance efforts nationwide. ## Methodology: Mapping the Landscape of Exclusion Risk Our research employed a rigorous, data-driven approach designed to ensure accuracy and reliability. The study utilized two authoritative public datasets: 1. **The OIG's List of Excluded Individuals and Entities (LEIE)**: The definitive record of providers excluded from participation in federal healthcare programs 2. **National Provider Identifier (NPI) data from NPPES**: Contains verified address information for healthcare providers nationwide ### Analytical Process Our analytical methodology was designed to transform raw data into actionable intelligence: 1. **Data Integration**: LEIE exclusion records were cross-referenced with NPI registry data using National Provider Identifiers as the linking key 2. **Geographic Validation**: Provider records underwent careful filtering to include only those with verified, current address information. Records with P.O. boxes or incomplete address information were excluded to ensure geographic precision 3. **Data Aggregation**: The dataset was aggregated at both the state and city levels to identify meaningful patterns 4. **Comparative Analysis**: For each geographic region, we calculated both absolute counts and relative percentages of nationwide exclusions 5. **Pattern Recognition**: Statistical analysis identified urban concentration patterns, regional variations, and deviations from population-based expectations This methodology ensures our findings accurately represent actual geographic distributions of excluded providers while maintaining individual provider privacy. ## Key Findings: America's Exclusion Hotspots Revealed Our analysis has uncovered striking patterns in the geographic distribution of excluded providers, challenging conventional assumptions about how compliance risk is distributed across the United States. ### State-Level Distribution: The Concentrated 45% This map shows the number of exclusions by state, highlighting areas with the highest concentrations. Darker colors represent more exclusions. | State | Exclusions | % of Total | % of U.S. Population | Risk Ratio | |-------------|------------|-------------|---------------------|------------| | California | 930 | 12.90% | 11.91% | 1.08 | | New York | 699 | 9.69% | 5.83% | 1.66 | | Florida | 600 | 8.32% | 6.58% | 1.26 | | Ohio | 595 | 8.25% | 3.56% | 2.32 | | Texas | 429 | 5.95% | 8.92% | 0.67 | What's particularly striking is that Ohio, with just 3.56% of the U.S. population, accounts for 8.25% of all exclusions—a concentration more than double what would be expected based solely on population size. ## City-Level Concentrations: The Critical 100 This map shows the number of exclusions by city, with larger circles indicating higher concentrations. Hover over a city to see details. | City | Number of Exclusions | % of National Total | |--------------|----------------------|---------------------| | Las Vegas, NV| 115 | 1.59% | | Brooklyn, NY | 101 | 1.40% | | Los Angeles, CA| 99 | 1.37% | | Houston, TX | 95 | 1.32% | | Miami, FL | 92 | 1.28% | Our analysis found that the top 21 cities account for 16.61% of all healthcare provider exclusions nationwide. Even more striking, the top 100 cities harbor 33.36% of all excluded providers. This urban concentration creates targeted high-risk zones for healthcare organizations. ## The Urban Concentration Phenomenon: State-Specific Patterns Our analysis reveals three distinct patterns of urban concentration that require different compliance approaches: ### 1. Extreme Concentration States - **Nevada**: 82.91% of exclusions in just 3 cities - **Rhode Island**: 80.00% of exclusions in top 3 cities - **Delaware**: 71.43% of exclusions in top 3 cities ### 2. Moderate Concentration States - **Louisiana**: 39.50% of exclusions in the top 3 cities - **Minnesota**: 38.81% of exclusions in the top 3 cities - **Michigan**: 35.92% of exclusions in the top 3 cities ### 3. Distributed Risk States - **California**: 278 cities with at least one excluded provider - **New York**: 235 cities with excluded providers - **Ohio**: 205 cities with excluded providers ## Per Capita Exclusion Rates: The True Risk Intensity When examining exclusions per million residents, a different picture of risk intensity emerges: | State | Exclusions per Million | Multiple of the National Average | |---------|-----------------------|----------------------------------| | Nevada | 64.19 | 2.79× | | Ohio | 50.85 | 2.21× | | Maine | 44.62 | 1.94× | | New York| 35.85 | 1.56× | | Texas | 14.57 | 0.63× | These per capita rates reveal that states like Nevada and Ohio face significantly elevated exclusion risks, which raw counts alone do not fully illustrate. ## Analysis and Implications: Beyond the Numbers The geographic patterns revealed in this analysis have important implications for how healthcare organizations should approach compliance risk management. These findings suggest the need for rethinking compliance strategies that have traditionally overlooked location-specific risk factors. ### Disproportionate Geographic Risk Our data establishes that healthcare compliance risk related to excluded providers does not follow simple population distribution patterns. Organizations in high-exclusion states like Ohio face a risk intensity more than twice what their population size would suggest. This demands geographically calibrated compliance protocols—what works in Texas (with disproportionately low exclusion rates) may prove inadequate in Nevada or Ohio (with disproportionately high rates). ### Factors Behind Geographic Disparities These geographic disparities likely stem from several interrelated factors: 1. **Regulatory Enforcement Variation**: States like New York and California have historically maintained robust healthcare fraud task forces and enforcement initiatives, which may lead to higher detection and exclusion rates 2. **Practice Density Dynamics**: Metropolitan areas with higher provider density create environments where improper practices may spread more readily through professional networks 3. **Regional Healthcare Economics**: States with particular reimbursement structures, cost pressures, or practice patterns may create environments where certain types of fraud or misconduct become more prevalent 4. **Market Complexity Factors**: Regions with more complex healthcare delivery systems, involving numerous facilities, specialists, and fragmented care delivery, create environments where oversight gaps can be exploited ### Compliance Implications These findings have significant implications for healthcare compliance strategies: 1. **Enhanced Verification in High-Risk Regions**: Organizations operating in identified hotspot regions should implement more frequent and thorough verification processes 2. **Geographically-Informed Screening Protocols**: Compliance programs should be tailored to an organization's specific geographic risk profile rather than applying uniform standards across regions 3. **Continuous Monitoring Necessity**: The concentration of exclusions in certain regions underscores the importance of continuous monitoring rather than periodic screening, particularly in high-risk areas 4. **Resource Allocation**: Compliance resources should be allocated proportionally to geographic risk, with enhanced focus on operations in high-concentration regions ## Conclusion: Regional Patterns in Compliance Risk This study provides new insights into healthcare compliance risk geography. The concentration patterns—with 45% of exclusions in just five states and one-third in just 100 cities—suggest that healthcare organizations should reconsider how they approach provider screening and monitoring. Our findings establish that: 1. **Exclusion risk varies by location**, with some regions facing more than double the national average risk 2. **Population alone does not predict exclusion patterns**, as demonstrated by Ohio's exclusion rate being more than double what its population would suggest 3. **Urban concentration creates areas** where risk is notably concentrated 4. **States show different risk patterns**, from high concentration (Nevada) to widely distributed patterns (California) These geographic differences suggest that compliance strategies treating risk as uniform across regions may be insufficient. Organizations should consider geography-informed compliance strategies that address these regional variations in exclusion risk.