Advanced analytical frameworks analyzing Medicare billing patterns, provider behavior, and healthcare access across federal programs serving millions of 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 through temporal validation.
Risk scoring framework analyzing 1.03 million prescribers to identify high risk opioid prescribing patterns through peer benchmarking and triple validation.
Anomaly detection system for injectable drug waste achieving 100% recall with federal validation concordance across 824 medications analyzed.
Comprehensive anomaly detection protecting vulnerable beneficiaries from high risk durable medical equipment suppliers through dual method validation.
Advanced system addressing COVID 19 telehealth expansion vulnerabilities with validated statistical precision for critical risk provider identification.
DRG manipulation detection across 2,911 facilities with 88% PEPPER concordance and systematic risk stratification framework.
Triple validated detection system protecting vulnerable populations requiring home based and end of life care services across 12,112 agencies.
County level framework analyzing all 3,198 U.S. counties achieving 98.4% HRSA concordance in identifying healthcare access disparities.
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 have developed analytical frameworks analyzing millions of Medicare claims and providers, achieving validation rates exceeding 98% against federal benchmarks including HRSA, CMS, and MedPAC.