NHAIEF
National Healthcare AI Enablement Foundation
A 501(c)(3) Public-Private AI Adoption Accelerator
Reducing U.S. Healthcare Costs Through Scalable AI Implementation
A national initiative deploying AI solutions across health systems to reduce administrative burden, improve workforce efficiency, and lower Medicare-related costs — starting with a pilot in Florida and Texas.
NHAIEF is the implementation layer between AI innovation and health system execution — not advisory, not tech, not policy alone. All three, coordinated as public infrastructure.
$4.8T
Projected 2026 U.S. healthcare spending
CMS National Health Expenditure Projections, 2024
30%
Administrative cost share of total spend
Annals of Internal Medicine, 2019; JAMA, 2022
67M+
Medicare beneficiaries served annually
CMS Medicare Enrollment Dashboard, 2025
2 States
Florida & Texas pilot deployment
NHAIEF Pilot Strategy
Sources are publicly available federal data and peer-reviewed publications. Figures are rounded for clarity.
What We Do
Four Programs. One Goal: Make AI Work for Every Health System.
Most hospitals want to use AI but lack the staff, resources, and trusted tools to do it safely. NHAIEF provides all four — as public infrastructure, not a vendor product.
Train the Workforce
Fellowship programs that prepare clinicians, administrators, and data scientists to lead AI adoption at their health systems.
Fellowship Programs
Fund the Deployment
Grants for safety-net and rural hospitals to cover integration, training, and evaluation — so cost is never a barrier to participation.
Pilot Funding
Validate the Technology
An independent testing environment where AI models are evaluated for safety, fairness, and reliability before they reach patients.
AI Validation Sandbox
Prove the Impact
A dedicated research lab producing the cost-savings evidence that CMS and Congress need to support AI-enabled Medicare reform.
Medicare Innovation Lab
How It Works
The NHAIEF Implementation Model
A repeatable, five-step process that takes a health system from AI readiness assessment to measurable cost reduction — and translates those results into national Medicare policy.
01
Identify
Map the highest-cost, highest-volume workflows at each participating health system — prior auth, clinical documentation, staffing, readmissions.
02
Match
Select and configure validated AI solutions from the NHAIEF model library, matched to the health system's EHR environment and patient population.
03
Deploy
Embed AI directly into existing workflows with dedicated implementation support — no proprietary hardware, no vendor lock-in, no disruption to clinical care.
04
Measure
Track cost reduction, workflow efficiency, and clinical outcomes at 90 days, 6 months, and 18 months. Independent evaluators verify all results.
05
Scale
Publish findings. Translate evidence into Medicare policy. Expand to additional health systems nationally.
Non-Profit & Federally Aligned
A registered 501(c)(3) operating as public infrastructure — neutral, transparent, and designed to complement CMS Innovation Center pilots and value-based care.
No Vendor Lock-In
Open architecture serving all participating health systems equally. No exclusive licensing, no proprietary dependencies.
Evidence-Based
Every model is independently validated. Every deployment is measured against cost and quality outcomes. Every finding is published.
The Problem
AI Could Save Medicare Billions. Hospitals Can't Get There Alone.
U.S. healthcare is approaching a fiscal breaking point. Nearly one in three dollars spent on healthcare never touches patient care — it funds billing, paperwork, and administrative overhead. AI can change this. But the gap between innovation and implementation remains wide open.
30%
of all healthcare spending goes to administrative overhead
Annals of Internal Medicine, 2019
46%
of U.S. physicians report burnout — partly driven by documentation burden
AMA Physician Survey, 2023
15 hrs
per week clinicians spend on paperwork instead of patients
Annals of Family Medicine, 2022
2.5x
variation in per-beneficiary Medicare costs across U.S. regions
Dartmouth Atlas of Health Care
Why Hospitals Can't Act Unilaterally
Four Barriers Blocking AI Adoption at Scale
No trained workforce
Most hospitals lack clinicians or administrators with AI deployment experience. There is no national training pipeline filling this gap.
No independent testing infrastructure
There is no public-interest environment to validate AI models for safety, bias, and reliability before deployment at scale.
No access for safety-net systems
Rural and safety-net hospitals — where cost reduction matters most — cannot afford enterprise AI tools without external support.
No policy translation mechanism
Even when AI demonstrably works, there is no structured pathway to translate proven savings into durable Medicare cost policy.
Where the Money Goes
AI Targets the Biggest Cost Drivers in Healthcare
NHAIEF focuses AI on the operational areas where hospitals waste the most money — not on replacing clinicians, but on removing the inefficiencies that drive up costs for everyone.
Administrative Costs
Prior authorization, claims processing, coding accuracy, denial prevention
Largest single savings opportunity
Workforce Strain
Predictive staffing, reduced agency nurse reliance, documentation time
Cuts labor cost inflation
Unnecessary Utilization
Avoidable readmissions, duplicate testing, length-of-stay reduction
Lowers per-beneficiary costs
Quality Penalties
Readmission risk prediction, care pathway optimization, post-acute planning
Reduces avoidable penalties
Projected Federal Impact
What This Means for Medicare
Conservative estimates based on published research and comparable federal programs.
Return on Investment
6:1 to 12:1
For every $1 invested, an estimated $6-$12 in reduced Medicare per-beneficiary costs.
Cost Reduction Target
8-12%
Per-beneficiary spending reduction at pilot sites within 18 months.
Total Program Cost
$15M / 3 Years
Less than 0.002% of annual Medicare spending, structured as milestone-based funding.
Projections are preliminary estimates based on published research and comparable federal programs. Actual outcomes will be determined by independent evaluation during the pilot phase.
Who This Is For
Built for the People Working to Fix Healthcare
Whether you run a hospital, shape federal policy, conduct research, or fund public-interest work, NHAIEF has a structured engagement pathway for you.
News & Updates
Recent Developments
Key milestones and announcements from the National Healthcare AI Enablement Foundation.
Foundation Formation Announcement
Announcing organizational formation and 501(c)(3) filing
FL & TX Pilot RFI Published
Request for information issued to health systems in Florida and Texas
Fellowship Program Design Complete
Four fellowship tracks finalized with curriculum development underway
Validation Sandbox Architecture Finalized
Technical architecture for AI model testing environment approved