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Eligibility Made Easy (Emmy) using Consent-Based Verification (CBV) is a prototype that allows benefit applicants to verify their income directly using payroll providers. It is currently being piloted for testing and validation purposes.
Python toolkit for Medicaid claims data analysis — preprocessing, cleaning, risk adjustment (Elixhauser, CDPS-Rx), quality measures (PQI, BETOS, low-value care), and patient-level analytic file construction for MAX and TAF CMS data. Built on Dask for scalable processing of large-scale healthcare claims datasets.
These are the official datasets used on the Medicare.gov Hospital Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at over 4,000 Medicare-certified hospitals across the country.
The Poplin project seeks to develop a cohesive, free, open-source set of application programming interfaces (APIs) for states to use in developing a modern, modular Medicaid IT system.
Open pipeline that turns buried public-health PDFs (Medicaid EQR, hospital CHNA, maternal mortality reviews) into research-ready, provenance-stamped datasets via AI extraction with a verification layer.
A regression analysis of the various types of health insurance offerings and its impact on the U.S. healthcare system. All regressions are routed to control for heteroskedasticity and empirical models in this study are adjusted for R2 with equally distributed residuals.
Reproducible systematic review workflow for Medicaid postpartum coverage policy, maternal health access, continuity of care, and health equity evidence synthesis.
Model-agnostic multi-agent system that discovers government benefits and insurance plans, validates eligibility, and produces prioritized enrollment action plans. Built with Kealu Vector, an enterprise AI workflow orchestrator, and integrated with Google Antigravity via MCP.