Embedding AI Triage into the Radiology Workflow of a Hospital Imaging Network
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Problem
Licensed AI models sitting unused
A 14-hospital imaging network had licensed regulator-cleared AI triage models for urgent findings, but they sat unused: nothing connected them to the PACS, radiologist worklists or on-call escalation paths. Studies still queued strictly first-in-first-out overnight, and the network had no way to route model flags to the right reader or record whether anyone acted on them.
Solution
Triage wired into PACS workflow
We built the integration layer the model vendors did not supply: a DICOM router streaming eligible studies to the third-party triage services, an orchestration service writing prioritisation flags back into PACS worklists, and mobile on-call notifications with full audit logging. Rollout ran hospital by hospital behind feature flags, with dashboards tracking pipeline latency, uptime and flag volumes.
Measurable Impact
What changed after launch
Median time from scan completion to a flagged study reaching a radiologist's worklist dropped from 47 minutes to under 4
94% of eligible study volume routed automatically through the triage pipeline by the end of rollout
All 14 hospitals live on the integrated workflow within 7 months, with zero unplanned PACS downtime
On-call escalations acknowledged within 10 minutes rose from 58% to 87% after mobile notifications launched
Tech & Tools Used
What powered the build
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