Key Takeaways:
- AI has gotten better at detecting abnormalities in scans, but detection alone doesn’t guarantee a patient receives needed follow-up care.
- Incidental findings often cross multiple departments — radiology, primary care, specialty care — and without clear ownership, patients can fall through the cracks.
- What’s needed isn’t another dashboard, but tools that build a care pathway, automate outreach and scheduling, and track patients until follow-up is complete.
- AI-generated flags should always trace back to the original report so staff can verify findings rather than act on them blindly.
- Better detection technology increases the volume of patients needing follow-up, so practices need a plan for clinical capacity before deploying new tools.
Why Detecting a Problem Isn’t the Same as Solving It
While healthcare technology has become increasingly good at detecting potential problems, it’s lacking in follow-through systems. AI can scan radiology reports and identify abnormal findings, but identifying a problem doesn’t guarantee the patient will receive the follow-up care they need.
This is a structural problem. An incidental finding may be detected during a scan that was ordered for an unrelated problem, but what happens next? Building better systems around that follow-through can improve patient care while reducing the administrative burden on teams that are already stretched thin.
Detection is Just the First Step
If an abnormality is detected during a CT scan for an unrelated issue, the radiologist will document the finding in the report, but who makes sure the patient schedules additional imaging? Who confirms that imaging takes place? And who continues tracking the patient if ongoing monitoring is needed? Those responsibilities may cross many different departments (radiology, primary care, specialty care, etc.), and without clear ownership, patients can fall through the cracks.
What Needs to Change
Many healthcare organizations are already dealing with staff shortages and administrative overload, so adding another dashboard isn’t the fix. What’s needed are tools that help create a care pathway, automate routine patient outreach and scheduling, and continue tracking the patient until recommended follow-up is complete. And ideally, these tools should integrate with existing electronic medical record systems and fit into workflows staff already use.
Automating manual steps frees staff from reviewing separate work queues, tracking down patients, coordinating across departments, and confirming that follow-up actually occurred. Clear workflows also prevent duplicate work and establish ownership over each step in the process, which allows staff to spend more time on work that needs their expertise.
AI results also need to be easy to verify. Every AI-generated flag needs to trace back to the original report for verification and a clear source of truth.
Make Sure Your Practice Can Handle What Technology Finds
Better detection likely leads to more patients who need follow-up. Who is responsible for reviewing findings? Who is responsible for contacting patients? Do you have enough clinical capacity for additional appointments and testing? Can technology handle enough of the administrative work to avoid requiring additional staff? Without a plan in place, better tools just create more clutter.
For healthcare practice owners and leaders, efficiency also means being able to manage more patients without adding too much to the administrative burden. Automating routine follow-up tasks can help staff handle more patients while focusing on cases that need human attention.
Hospitals and healthcare practices are already sitting on data that could catch disease earlier, but this data is buried in routine scans and reports. There is a real opportunity to make better use of information healthcare organizations already have.
With technology that connects detection to scheduling, follow-up, and long-term tracking, healthcare organizations can build more efficient care pathways. And with these systems in place, an incidental finding could become an opportunity to diagnose disease earlier.
