Core Programs
Four Pillars of the Accelerator
The foundation operates through four integrated program areas designed to build credibility, attract federal and private funding, and establish public legitimacy for healthcare AI infrastructure.
Each program is structured to produce measurable outcomes, publishable evidence, and institutional capacity that supports long-term sustainability and access to federal grants and appropriations.
Fellowship Programs
Building the next generation of healthcare AI leaders
Structured fellowship tracks for clinicians, health system administrators, data scientists, and policy professionals to develop applied expertise in healthcare AI deployment, governance, and evaluation.
Program Components
Clinical AI Fellowship
For practicing physicians, nurses, and allied health professionals seeking to lead AI adoption within their health systems. Combines hands-on model evaluation with clinical workflow integration.
Health System Leadership Fellowship
For C-suite and senior administrators focused on operational AI strategy, vendor evaluation, change management, and cost-impact analysis.
AI Safety & Governance Fellowship
For professionals in health policy, bioethics, and technology governance focused on building institutional capacity for responsible AI oversight.
Research & Evaluation Fellowship
For health economists, data scientists, and outcomes researchers conducting rigorous evaluation of AI model performance, cost savings, and equity impacts.
Target Outcomes
200+ fellows trained across FL & TX pilot systems by Year 3
Credentialed AI readiness assessors embedded in participating health systems
Published fellowship curriculum available as open-access resource
Pipeline of qualified leaders for advisory and governance roles
Pilot Funding
De-risking AI adoption for health systems that need it most
Targeted grant funding and implementation support for health systems deploying foundation-approved AI models. Focused on safety-net hospitals, rural health systems, and high Medicare-mix providers where cost reduction has the greatest per-beneficiary impact.
Program Components
Implementation Grants
Direct funding for EHR integration, staff training, workflow redesign, and model deployment costs. Structured as milestone-based awards tied to measurable outcomes.
Technical Assistance Awards
Funding for health systems needing infrastructure readiness assessment, data pipeline development, or interoperability upgrades before model deployment.
Evaluation & Outcomes Grants
Support for independent cost-impact analysis, clinical outcomes measurement, and health equity assessments at participating pilot sites.
Sustainability Planning Grants
Post-pilot funding for health systems developing long-term AI governance structures, budget models, and workforce transition plans.
Target Outcomes
Targeted deployment at 20+ pilot sites across Florida and Texas
Priority funding for safety-net and rural health systems
Measurable cost reduction benchmarks tied to Medicare per-beneficiary spend
Grant-funded independent evaluation of every funded deployment
AI Model Validation Sandbox
Rigorous, independent testing before any model reaches patients
A controlled evaluation environment where AI models undergo systematic testing for clinical safety, algorithmic fairness, performance consistency, and operational reliability before deployment in live health system settings.
Program Components
Clinical Safety Testing
Structured evaluation protocols assessing model performance against clinical benchmarks, edge cases, failure modes, and patient safety thresholds.
Bias & Equity Auditing
Systematic fairness testing across demographic groups, geographic populations, and payer mixes to identify and mitigate disparate impact before deployment.
Performance Benchmarking
Standardized evaluation metrics for accuracy, reliability, latency, and cost-efficiency across model types and deployment contexts.
Interoperability Certification
Testing model compatibility with major EHR platforms, data standards (FHIR, HL7), and health system IT environments.
Target Outcomes
Every deployed model independently validated before live use
Published validation reports for transparency and public accountability
Standardized evaluation framework available to the broader health AI ecosystem
Continuous post-deployment monitoring and performance tracking
Medicare Innovation Lab
Generating the evidence base for federal AI policy
A dedicated research and analysis function producing rigorous cost-impact evidence, policy recommendations, and operational insights designed to inform CMS, CMMI, and Congressional decision-making on AI-enabled Medicare reform.
Program Components
Cost-Impact Modeling
Econometric analysis of AI deployment costs versus savings at the per-beneficiary, per-facility, and system-wide levels. Projections for Medicare Trust Fund impact.
Policy Brief Development
Evidence-based policy documents translating pilot findings into actionable recommendations for CMS rulemaking, Congressional appropriations, and HHS strategy.
Regulatory Sandbox Coordination
Working with CMS Innovation Center to design evaluation frameworks for AI-enabled care models within existing waiver and demonstration authorities.
Outcomes Research Program
Longitudinal study of clinical outcomes, workforce impact, patient experience, and cost trajectories at pilot sites to build the evidence base for national scale-up.
Target Outcomes
Quarterly cost-impact reports to CMS and Congressional committees
Evidence-based framework for AI inclusion in value-based care programs
Published research contributing to federal healthcare AI standards
Data-driven recommendations for Medicare Trust Fund sustainability
Implementation Timeline
Three-Year Milestone Plan
Phased implementation with measurable deliverables at each stage, designed to demonstrate impact for continued federal funding.
Foundation & Pilot Launch
- Organizational formation and 501(c)(3) filing
- First fellowship cohort enrolled (40+ fellows)
- 5-8 pilot site agreements signed in FL & TX
- Validation sandbox infrastructure operational
- Baseline cost metrics collected at all pilot sites
Expansion & Evidence Generation
- Second fellowship cohort; 100+ total fellows trained
- 15-20 active pilot sites across both states
- First quarterly cost-impact reports published
- Model validation reports publicly available
- Medicare Innovation Lab policy briefs to CMS
Evaluation & National Readiness
- 200+ fellows across all four tracks
- 20+ pilot sites with full outcomes data
- Comprehensive cost-savings analysis published
- National expansion readiness assessment
- Congressional reporting and appropriations briefing
Budget Overview
Estimated Program Allocation
Projected three-year budget across all four program pillars, subject to federal appropriations and grant outcomes.
Fellowship Programs
$4.2M
Fellow stipends, curriculum development, program operations, credentialing infrastructure
Pilot Funding
$5.5M
Implementation grants, technical assistance awards, evaluation grants, sustainability planning
Model Validation Sandbox
$2.8M
Testing infrastructure, bias auditing tools, benchmarking systems, interoperability certification
Medicare Innovation Lab
$2.5M
Cost-impact modeling, policy brief production, regulatory coordination, outcomes research
Total Estimated (3-Year)
$15.0M
Preliminary estimate; subject to federal appropriations, grant awards, and pilot scope adjustments.
Evaluation Framework
Key Performance Indicators
All programs are evaluated against standardized KPIs with independent verification. Baselines are established during Phase 1 of the pilot at each participating site.
Medicare Cost per Beneficiary
Pre/post comparison at pilot sites, adjusted for case mix and regional factors
Administrative Cost Ratio
Percentage of operating expense on admin functions, measured quarterly
Fellows Trained & Placed
Enrollment tracking, completion rates, post-fellowship placement in health system AI roles
Models Validated
Sandbox submissions processed, validation reports published, pass/fail rates
Pilot Sites Active
Signed agreements, deployment milestones achieved, outcomes data reporting
Policy Briefs Published
Quarterly publications to CMS, Congressional committees, and public release
Proof of Concept
Illustrative Case Studies
The following case studies reflect published research and documented outcomes from comparable healthcare AI deployments. They illustrate the category of impact NHAIEF programs are designed to achieve and the implementation pathway we support. Health system names are illustrative; detailed citations available on request.
Outcomes reflect peer-reviewed publications and publicly available program evaluations, not NHAIEF pilot data. Actual pilot results will be independently evaluated and published.
Mid-Size Safety-Net System, Southeast U.S.
~340 beds · High Medicare mix
Intervention: AI-assisted prior authorization and clinical documentation
31% reduction in prior authorization denial rates over 14 months
2.1 hrs/day recovered per clinician from documentation burden
$2.4M annual administrative cost reduction across three facilities
Comparable to: JAMA Network Open (2023) — AI documentation assistant RCT
Rural Health System, Central Texas
Critical Access Hospital · 4 facilities
Intervention: Readmission risk prediction model integrated with discharge planning workflow
18% reduction in 30-day all-cause readmissions over 12 months
CMS Penalty avoided — saved ~$680K in readmission adjustment losses
Risk model adopted by 92% of attending physicians within 6 months
Comparable to: Health Affairs (2022) — Rural AI readmission intervention study
Large Public Health System, South Florida
~900 beds · Medicaid/Medicare primary
Intervention: Scheduling optimization and no-show prediction model
14% improvement in appointment utilization across 8 outpatient sites
~$1.1M annual revenue cycle improvement from recovered appointment slots
Equity analysis showed no disparate impact across demographic groups
Comparable to: NEJM Catalyst (2023) — Scheduling AI equity validation study
Return on Investment
Cost-Effectiveness Analysis
Preliminary cost-effectiveness projections based on published healthcare AI research, comparable CMMI model results, and the foundation's target KPIs.
Actual ROI will be determined by independent evaluation during the pilot phase. The projections below are conservative estimates designed for appropriations-level planning.
Cost per Fellow Trained
$21K
Across all four fellowship tracks, including stipends, curriculum, and credentialing. Comparable federal programs average $30-50K per trainee.
Cost per Pilot Site
$275K
Average implementation grant plus technical assistance, evaluation, and sustainability support per participating health system.
Cost per Model Validated
$56K
Average validation cost including sandbox testing, bias auditing, and interoperability certification per AI model submission.
Cost per Policy Brief
$208K
Average cost per quarterly policy brief including underlying research, cost-impact modeling, and CMS/Congressional delivery.
Theory of Change
From Investment to Medicare Impact
The logic model connecting federal investment to measurable Medicare cost reduction.
- $15M federal/grant funding
- Health system partnerships
- EHR integration infrastructure
- Clinical & policy expertise
- De-identified Medicare data
- Train 200+ AI fellows
- Deploy to 20+ pilot sites
- Validate 50+ AI models
- Produce 12+ policy briefs
- Conduct cost-impact research
- Credentialed AI workforce
- Validated deployment toolkit
- Published cost-savings data
- Federal policy recommendations
- Replicable pilot framework
- 8-12% Medicare cost reduction at pilot sites
- Scalable model for national deployment
- Evidence base for CMS AI policy
- Reduced administrative burden
- Improved health equity metrics
Strategic Value
Why This Structure Matters
The four-pillar accelerator model is designed to achieve three strategic objectives that are essential for long-term organizational sustainability and public impact.
Credibility
Fellowship programs and the validation sandbox establish NHAIEF as a rigorous, evidence-based institution — not a vendor or advocacy organization. Published validation reports and trained fellows create a reputation for scientific integrity.
Access to Grants
Pilot funding and the Medicare Innovation Lab produce the measurable outcomes and policy-relevant evidence that federal grant programs require. The accelerator structure aligns with CMMI, AHRQ, and NIH funding criteria.
Public Legitimacy
Independent model validation, transparent governance, and a fellowship pipeline demonstrate that the foundation serves the public interest. This legitimacy is essential for Congressional appropriations and CMS partnership.
Evidence & Impact
Case Studies & Pilot Outcomes
Detailed case studies will be published as pilot sites complete their evaluation phases. The studies below represent planned analyses currently in development across the Florida and Texas pilot network.
Administrative Automation at a Safety-Net Hospital
Measuring the impact of AI-driven prior authorization and claims processing automation on administrative costs and staff workload at a high-volume safety-net facility.
Rural Health System Workforce Optimization
Evaluating AI-assisted staffing, scheduling, and resource allocation models in rural health systems with limited workforce capacity and high patient-to-provider ratios.
Medicare Cost Impact: Multi-Site Analysis
Comprehensive cross-site analysis of Medicare per-beneficiary cost changes across pilot locations, controlling for case mix, geography, and system characteristics.