I build lightweight, AI-informed tools for rural health policy, medical education, GME workforce planning, and research strategy — using open data, automation, and clear metrics to support senior leadership decisions in academic medicine and health systems.
Enterprise-grade tools built on open public health data — designed for policymakers, health system leaders, and researchers.
A fully interactive enterprise analytics dashboard built from the 2025 America's Health Rankings report. Covers national benchmarking, state rankings, racial equity analysis, and a Louisiana strategic spotlight — with a four-quadrant priority matrix for health system leaders and policymakers.
Strategic analytics for understanding federal research funding patterns across institutions, states, and mechanisms.
A strategic intelligence tool for analyzing NIH funding patterns across fiscal years 2014–2025. Explore award trends by institution, state, mechanism, and activity code — designed to support research strategy, grant planning, and competitive positioning for academic medical centers and health systems.
High-signal, coach-facing analysis built as interactive GitHub Pages briefs.
Mechanism, evidence strength by event, GI-risk mitigation, individualized timing logic, key debates — including Bob Bowman's critique — and a practical decision framework for coaches working with elite swimmers.
Automated monitors and tools focused on rural hospitals, provider pipelines, and health policy signals.
A fully automated daily monitor that ingests rural health policy news from multiple RSS feeds and produces a structured digest with risk, opportunity, GME, and Deep South (LA, MS, AR, AL) signals. Runs entirely on GitHub Actions with no paid APIs.
A bill-tracking pipeline exploring how to scrape, filter, and summarize federal legislation related to rural health, critical access hospitals, telehealth, and workforce incentives.
Curated frameworks to safely vet AI tools for clinical, educational, and research settings.
A structured framework and checklist to assess AI tools used in clinical care, education, and administration — with emphasis on bias, transparency, data use, and alignment with institutional policies.
A living lexicon of AI concepts translated for clinicians, educators, and administrators — designed to support curriculum development, grant writing, and informed decision-making.
Tools linking admissions, match outcomes, and training programs to workforce needs.
A sandbox predictive model using mock data to explore how student metrics, application patterns, and institutional factors relate to residency Match outcomes.
A collection of experiments analyzing provider supply, access, and outcomes. Serves as a sandbox for prototyping visualizations and metrics across health system domains.
Combining AI, wearables, and biomechanics to understand elite swimming performance through human performance analytics and data-driven coaching insights.
I work at the intersection of academic medicine, strategy, and analytics — turning messy, fragmented data into clear narratives, scenario options, and decision tools for deans, department chairs, and health system leaders.
My work connects rural health and workforce policy with the realities of GME expansion, LCME/ACGME standards, research funding intelligence, and financial constraints. A recurring theme is building governance-minded, future-oriented frameworks for AI adoption, workforce planning, and research growth.
I enjoy designing strategy-ready prototypes — small, opinionated tools that make it easier for leadership to see trade-offs, align stakeholders, and move from discussion to execution.