Duke's Health AI Partnership Helps Small Hospitals and FQHCs Access AI, Reduce Administrative Burden

Practical AI Access for Smaller Hospitals and FQHCs

Many smaller hospitals and federally qualified health centers (FQHCs) want the gains from AI but lack the data, staffing, and infrastructure to deploy it. Duke's Dr. Mark Sendak highlights how the Health AI Partnership is helping these organizations access tools that cut administrative work and improve efficiency.

The goal is simple: get safe, proven AI into the hands of lean teams without adding complexity or risk.

Why adoption stalls

• Limited data and IT capacity: Fragmented EHR data, few engineers, and no MLOps.
• Budget and vendor risk: High upfront costs and unclear ROI across a crowded market.
• Compliance concerns: PHI protection, algorithm bias, and unclear accountability.
• Workflow friction: Tools that don't fit clinician workflows create more clicks, not fewer.

How the Health AI Partnership helps

• Access to proven use cases: Focus on administrative relief and operational efficiency first.
• Implementation playbooks: Clear steps for data access, integration, testing, and go-live.
• Procurement support: Vendor evaluation criteria, model documentation, and security checklists.
• Safety and equity practices: Practical guidance for bias testing, monitoring, and governance.

The point is to reduce guesswork so smaller teams can adopt AI with confidence and speed.

High-yield use cases to start now

• Clinical documentation assistance: Draft notes, summarize visits, and reduce after-hours charting.
• Prior authorization and fax intake: Extract, validate, and route payer requirements faster.
• Denials prevention and CDI: Surface missing codes, support queries, and improve reimbursement.
• Population health outreach: Identify care gaps and auto-generate outreach lists and messages.
• Scheduling and capacity management: Predict no-shows and optimize templates to reduce bottlenecks.

A lean 90-day adoption plan

• Weeks 0-2: Pick one use case with clear ROI; define success (time saved, turnaround, error rate).
• Weeks 2-4: Shortlist vendors; require a workflow demo inside your EHR or inbox-not a standalone app.
• Weeks 4-6: Run a no-PHI sandbox or synthetic data test; check output quality and edge cases.
• Weeks 6-8: Small pilot (5-10 users); measure baseline vs. pilot metrics weekly.
• Weeks 8-12: Decide go/no-go; if go, expand in waves and set monthly monitoring and re-training cadence.

Governance, safety, and equity

• Set a lightweight AI review group: Clinician lead, privacy/security, quality, and IT.
• Use a risk framework: Map risks and controls using the NIST AI Risk Management Framework.
• Bias checks: Compare output quality across demographics; set thresholds and escalation steps.
• Human-in-the-loop: Keep clinicians in control for high-impact tasks; log overrides for learning.
• Monitoring: Track drift, error rates, and user feedback; schedule periodic revalidation.

Tech approaches that fit small teams

• Cloud-first, vendor-hosted AI: Reduce local infrastructure needs; insist on BAA and PHI controls.
• EHR-integrated apps: Minimize clicks by embedding in notes, inbox, or orders.
• API + RPA hybrids: Use simple automation for repetitive steps while AI handles text-heavy tasks.
• Data minimalism: Send only what's needed; prefer on-record summarization over raw note exports.

Funding and procurement tips

• Start small: One use case, fixed-price pilot, exit criteria in writing.
• Grants and partnerships: FQHC-focused grants and consortium buying can lower costs.
• Value-based alignment: Prioritize use cases that hit quality measures or reduce administrative waste.
• Shared contracts: Leverage group purchasing and reference sites with similar size and EHR.

Metrics that matter

• Minutes of admin work saved per encounter
• Prior authorization turnaround time and approval rate
• Denial rate and preventable write-offs
• Clinician documentation time and after-hours work
• Care gap closure and outreach completion rate
• User satisfaction and adoption rate

Bottom line

Smaller hospitals and FQHCs can implement AI without heavy infrastructure by focusing on a narrow set of high-value tasks, insisting on workflow-native tools, and adopting simple, visible governance. Initiatives like the Health AI Partnership aim to make that path predictable and safe.

Further resources

• NIST AI Risk Management Framework for risk controls and governance patterns.
• Complete AI Training: Courses by Job for upskilling staff who will use and oversee AI.