Effective reading throughput up ~22%; average wait for highly suspicious cases down ~40%.
C-02-02
Apex Imaging Network operates 12 imaging centers handling mixed emergency and outpatient queues. Equipment and reporting habits vary by site, and reading workload stays elevated during peak hours.
The network wanted unified prioritization, structured drafts, and QC spot-check standards without disrupting physician workstation habits.
C-02-03
Exam volume grows faster than staffing; emergency and outpatient queues are hard to balance.
"Our biggest problem was that highly suspicious cases were often buried in the queue during peak hours."
Report style and review depth vary across sites.
External web AI forces physicians to switch systems—low adoption.
Consultation prep still relies on manual screenshots and verbal handoffs.
C-02-04
Prioritization and prompts appear in the existing reading interface—near-zero switching cost.
Structured drafts and review audit trails support unified standards across centers.
Same capabilities across 12 nodes with centralized version and policy governance.
C-02-05
Two flagship centers first, then rolling replication across all 12 nodes.
Complete PACS sidecar, queue rules, and QC templates.
Compare against manual prioritization; tune false positive and miss thresholds.
Deploy standard pack to remaining centers with unified versions and spot-check rules.
Cross-center consultation packages and QC dashboards enter daily operations.
C-02-06
| Metric | Pre-pilot | Post-pilot | Change |
|---|---|---|---|
| Effective reading throughput per unit time | Baseline | Improved | +22% |
| Average wait for highly suspicious cases | 100 | 60 | −40% |
| QC spot-check preparation time | 100 | 70 | −30% |
Note: how efficiency gains show up in queue structure.
C-02-07
"The key is AI stays in the workstation. Physicians don't open another page—highly suspicious cases rise to the top, and QC spot checks are finally consistent network-wide."Dr. Kenji Okada · Imaging Network Operations Lead, Apex Imaging