From Paperwork to Patient Care: How AI Improves Health for Veterans

Using AI to Advance Veteran Healthcare: Practical Playbook from Capitol Hill to the Clinic

Nearly 16 million veterans live in the U.S., according to the U.S. Census. The House Committee on Veterans' Affairs recently examined how artificial intelligence can improve care delivery, reduce friction in the system and drive better outcomes. Michigan State University's Mohammad Ghassemi shared evidence and examples with the Technology Modernization Subcommittee on Sept. 15, offering a clear path for healthcare leaders.

AI that improves what happens during care

• Clinical documentation: AI transcription can draft visit notes in real time, freeing clinicians to listen, examine and decide.
• Emergency prioritization: Triage tools can flag the sickest patients sooner by analyzing presenting symptoms and vitals.
• Continuous monitoring: Wearables and bedside devices can detect early signs of conditions like atrial fibrillation before they're obvious to clinicians.

Result: safer, timelier care and more face time with patients instead of screens.

Fixing the operational gaps that delay care

• Fewer no-shows: Automated reminders and confirmations reduce missed appointments and protect scarce clinical time.
• Closed loops on referrals: Systems can flag missing information, track follow-ups and surface stalled handoffs between primary care and specialists.
• Follow-up on incidental findings: If a scan reveals an unexpected lung nodule, tracking tools ensure a clinician reviews and acts so treatable issues are not lost.

Better outcomes through proactive, personalized support

• Adherence and care gaps: For chronic conditions, AI can watch for missed meds, labs or visits and trigger timely reminders.
• Risk stratification: Predictive models can identify patients at high risk of overdose or readmission and prompt outreach before harm occurs.
• Critical care decisions: Analysis of EEG data after cardiac arrest can sharpen prognosis, helping teams focus treatment where recovery is likely.

These tools don't replace clinical judgment. They make complex data readable and actionable at the point of care.

Guardrails that protect patients and clinicians

• Disciplined pilots: Start small, define success upfront and compare against current practice.
• Clear metrics: Track clinical outcomes, time saved, equity impacts and alert fatigue-not just accuracy.
• Safety, equity, privacy: Require human oversight, bias testing, audit logs, and strong data governance.
• Patient transparency: Explain where AI is used, how it affects care and how data is protected.

What healthcare leaders can do now

• Pick high-value, low-risk use cases: documentation assistance, referral tracking, no-show reduction, incidental finding follow-up.
• Integrate with workflow: Put insights in the EHR where teams already work; avoid extra clicks.
• Validate locally: Re-check model performance on your veteran population; monitor drift and equity.
• Upskill teams: Train clinicians and operations staff on prompts, limitations, privacy and escalation paths.
• Set governance: Create a review board, incident process and a living model inventory.

Why this matters for veterans

Veterans often manage multiple conditions, see many specialists and rely on precise coordination. AI can return time to the bedside, close dangerous gaps and support the hardest calls in critical care-making care more efficient, more responsive and more humane.

For policy context and collaboration opportunities, see the Department of Veterans Affairs' work through the National Artificial Intelligence Institute.

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