Advanced analytical frameworks detecting healthcare fraud, improving access, and ensuring the sustainability of federal programs serving 65 million Americans
Comprehensive analytical frameworks addressing critical challenges in U.S. healthcare
Machine learning system analyzing 8.9 million Medicare claims to identify fraudulent billing patterns across all healthcare sectors.
Risk scoring framework analyzing 1.38 million prescribers to combat the opioid epidemic affecting millions of Americans nationwide.
Detection system for injectable drug waste fraud with exceptional return on investment and comprehensive federal validation.
Comprehensive fraud detection protecting vulnerable beneficiaries from fraudulent medical equipment suppliers nationwide.
Advanced system addressing COVID-19 telehealth expansion vulnerabilities with perfect precision for critical risk providers.
DRG manipulation detection across 2,911 facilities preventing billions in improper Medicare payments annually.
Triple-validated detection system protecting vulnerable populations requiring home-based and end-of-life care services.
Equity framework analyzing all 3,198 U.S. counties to improve healthcare access for underserved populations.
I specialize in developing advanced analytical systems that protect federal healthcare programs from fraud while improving access for vulnerable populations. My work focuses on creating reproducible, validated frameworks that federal agencies can immediately deploy.
Through rigorous statistical methods and machine learning techniques, I've identified over $50 billion in fraud and inefficiencies across Medicare and Medicaid programs, achieving accuracy rates exceeding 99% while ensuring healthcare access for millions of underserved Americans.