Driving High-Reliability Follow-Up Without More Staff
Human-Centered AI in Radiology
June 20, 2025 | Discover how human-centered AI empowers radiology teams to close follow-up gaps, improve outcomes, and drive high reliability—without added staff.
Radiology follow-ups are critical—but often missed. This blog explores how human-centered AI can transform follow-up workflows by supporting radiologists, automating manual tasks, and driving high-reliability performance. Learn how health systems like East Alabama Medical Center improved closure rates by 74% and boosted efficiency without hiring additional staff. Discover how to make AI work for your team and deliver better outcomes at scale.
AI is often heralded as the answer to healthcare’s biggest challenges. But what actually works when it comes to ensuring follow-up on radiology recommendations? The answer lies in combining human-centered AI with high-reliability principles—a strategy that elevates the clinical team, not replaces them.
What Is Human-Centered AI?
Angela Adams, RN, CEO of Inflo Health, says it best:
“The most effective AI-enabled solutions elevate the work of the human.”
In radiology follow-up, AI should:
Detect actionable findings reliably
Automate repetitive, manual tasks
Surface the right information at the right time
Support—but never override—clinical expertise
Designing for High Reliability
Inflo Health’s solution is built around the five high-reliability organization (HRO) pillars, operationalized through the platform:
HRO Principle
Technology Application
Outcomes
Preoccupation with failure
AI-driven identification and tracking of 100% of follow-up needs
Automated escalations via text, mail, alerts, etc.
Higher adherence rates, fewer care gaps
Making AI Work in Real Hospitals
Too often, AI fails because it is deployed at the problem rather than with the team. The ACR’s ImPower Program has found that when vendors act as true collaborators—working like part of the clinical team—technology adoption succeeds.
That’s why Inflo Health embeds with clients, aligns to their workflows, and designs AI that’s both flexible and functional.
The Role of the Radiologist
Rather than replace radiologists, high-reliability AI acts as a force multiplier:
It flags recommendations based on radiologist language—not arbitrary logic
It routes follow-ups according to local protocols
It documents progress, but always defers to clinician review
You Don’t Need More FTEs
Here’s the myth: to improve follow-up adherence, you need more staff. Here’s the truth: you need smarter systems.
At East Alabama Medical Center, Inflo’s AI enabled:
A 95% reduction in manual tracking effort
A 74% increase in completed follow-ups
All achieved without new FTEs or major hiring
A Maturity Model for Progress
Not every system is ready to jump to AI overnight. Inflo and the ACR developed a follow-up maturity model that lets organizations assess where they are—and where they can go. Key levers include:
Ownership: Who’s responsible for follow-ups?
Identification: How are follow-up needs flagged?
Tracking: How is progress monitored?
Closure: What’s your true closure rate?
Whether you’re just starting or scaling across departments, the path to high reliability is clear—and achievable.
Final Thoughts
Human-centered AI can fundamentally transform how radiology departments ensure follow-up. But success doesn’t come from automation alone—it comes from embedding reliability into every layer of the system.
The good news? You don’t have to wait. High-reliability follow-up is possible today—and the patients you serve can’t afford for it to wait until tomorrow.
