GenAI-Powered Analytics and Diagnosis Support
How a regional hospital network deployed AI-assisted diagnostic analytics to reduce report turnaround time by 85% and improve diagnostic consistency.
Westbrook Health Network's radiology department was struggling with a 72-hour backlog on routine imaging reports. VictorLabs built a GenAI-powered analytics platform that automated preliminary report generation, flagged critical findings for immediate review, and provided diagnostic decision support — all within HIPAA compliance.
Report turnaround
72 hrs → 4 hrs
Critical finding review
<15 minutes
Report editing time
60% reduction
Monthly studies
12,000+
Context
Westbrook Health Network operates 3 hospitals and 12 clinics across a regional network. Their radiology department processes 12,000+ imaging studies monthly across X-ray, CT, MRI, and ultrasound. A team of 8 radiologists was responsible for reviewing all studies and generating reports — a workflow designed for half the current volume.
Problem
Report turnaround time averaged 72 hours for routine studies, with critical findings occasionally buried in the queue. Radiologist burnout was high — two had left in the previous year. Referring physicians were making treatment decisions without complete radiology reports. The hospital administrator identified report turnaround as the #1 physician satisfaction issue.
Constraints
HIPAA compliance was non-negotiable — no patient data could leave the hospital's environment. The system needed to integrate with the existing PACS (Picture Archiving and Communication System) and EHR (Epic). Radiologists were skeptical of AI assistance and required full transparency into how the system generated its outputs. Any system failure could impact patient care.
Approach
We designed the system as a radiologist assistant, not a replacement. The AI generates a structured preliminary report with findings, measurements, and a differential diagnosis suggestion — flagged with confidence indicators. Reports are queued for radiologist review, with critical findings escalated to the top of the queue. The radiologist can accept, modify, or reject the AI-generated content. All modifications are tracked for continuous improvement.
Solution
The platform ingests imaging studies from PACS. A vision-language model analyzes the images and generates a structured preliminary report with annotated findings. A natural language generation layer formats the findings into a standard radiology report template. Critical findings (e.g., suspected malignancies, acute hemorrhages) are flagged for immediate review with visual highlighting. Radiologists review and finalize reports in a purpose-built interface that shows the AI's reasoning alongside the original images. Reports are pushed to Epic upon finalization.
Architecture
Deployed entirely within the hospital's on-premise infrastructure. Backend: Python services running on hospital-managed servers with GPU acceleration. Vision-language model: fine-tuned medical imaging model running locally. Frontend: React-based radiologist workspace with side-by-side image viewer and report editor. Integration: HL7/FHIR connectors for PACS and Epic. All PHI remains within the hospital network. Audit logging captures every system action for compliance.
Results
Routine report turnaround dropped from 72 hours to under 4 hours. Critical finding time-to-review decreased from hours to under 15 minutes. Radiologist report editing time reduced by 60% — the AI-generated draft required mostly verification rather than creation. Physician satisfaction scores improved 40 points in the first quarter. The system detected 3 critical findings in the first month that had been initially overlooked in the queue backlog.
Lessons
The decision to deploy on-premise (rather than cloud) was essential for hospital IT acceptance — it eliminated data residency concerns and simplified the security review. The 'AI as assistant, not replacement' positioning was critical for radiologist adoption. The confidence indicators and reasoning transparency features were the most-requested capabilities during user testing and proved essential to building trust.
“The system has fundamentally changed how our radiology department operates. Our radiologists went from being perpetually behind to having bandwidth for complex cases and research. The AI doesn't replace them — it makes them faster and more consistent.”
— Dr. James Okonkwo, Chief of Radiology, Westbrook Health Network