GenAI-Assisted Reporting for Clinical Teams
How we would approach AI-drafted preliminary reports and critical-finding triage under strict data-residency rules.
A radiology department has a backlog of routine reports, and critical findings can sit in the queue. This scenario walks through an assistant that drafts structured preliminary reports and prioritises the queue, with clinicians making every final decision.
What we would agree up front
No patient data leaves the hospital environment
Clinician sign-off on every report; the AI never finalises
Turnaround and edit rate measured against the pre-launch baseline
A composite scenario showing how we approach this kind of problem, not a specific client engagement.
The situation
Study volume has outgrown the team, turnaround for routine reports is slow, and referring physicians are waiting on reports before making treatment decisions.
Constraints we would design around
Patient data must not leave the hospital environment. The system must integrate with existing PACS and EHR systems over HL7/FHIR. Clinicians need full transparency into what the system produced and why, and it must fail safe: if the assistant is down, the existing workflow continues unchanged.
Approach
Position the system as an assistant, not a replacement. It drafts a structured preliminary report with confidence indicators and pushes suspected critical findings up the queue; the clinician accepts, edits or rejects every draft. Edits are tracked as the evaluation signal for future model updates.
Architecture
Models deployed on hospital-managed infrastructure (on-premise or the hospital's private cloud). Clinician workspace with the image viewer and report editor side by side. HL7/FHIR connectors to PACS and EHR. Audit logging of every system action. Any clinical use would follow the applicable medical-device and regulatory review before deployment.
Design notes
In clinical settings, adoption depends on transparency features — confidence indicators and visible reasoning — more than on raw model quality. On-premise deployment removes the hardest data-residency questions from the security review.