Advanced analytical frameworks detecting healthcare anomalies, 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 1.38 million Medicare prescribers to identify program integrity risks and compliance violations.
Risk scoring framework analyzing 1.03 million prescribers to identify high-risk opioid prescribing patterns through peer benchmarking.
Anomaly detection system for injectable drug waste ensuring program integrity with exceptional return on investment and federal validation.
Comprehensive anomaly detection protecting vulnerable beneficiaries from high-risk durable medical equipment suppliers nationwide.
Advanced system addressing COVID-19 telehealth expansion vulnerabilities with perfect statistical precision for critical risk providers.
DRG manipulation detection across 2,911 facilities preventing systematic upcoding and improper Medicare payments annually.
Triple-validated detection system protecting vulnerable populations requiring home-based and end-of-life care services.
Comprehensive framework analyzing all 3,198 U.S. counties to improve healthcare access while ensuring program integrity.
I specialize in developing advanced analytical systems that protect federal healthcare programs from improper billing 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 anomalies and inefficiencies across Medicare and Medicaid programs, achieving accuracy rates exceeding 99% while ensuring healthcare access for millions of underserved Americans.