Case Study
Imaging Queue Prioritisation for Clinical Review
Organisation: North East Diagnostics Partnership (Exemplar)
A healthcare team tested AI-assisted prioritisation to surface urgent imaging cases sooner for clinician review.
Overview
Overview
- Sectors
-
Life Sciences
- Competencies
-
C41 C52 C56
- Duties
-
D12 D13 D13
- Audience
-
FE/HE , Adult learners
- Skills areas
-
Responsible AI practice , Governance and risk management
- Routing
-
Schools , Colleges
People and engagement
- People
-
- Industry mentor lead - non-traditional pathway highlighted in delivery notes
- Engagement
-
- Delivery mode agreed during brokerage routing
Curriculum
- AI Skills for Business competency-linked activity
The challenge
What needed to change
Growing scan volumes made it difficult to maintain timely triage while ensuring every case received appropriate clinical oversight.
The approach
How AI was introduced
The service deployed AI flagging for potential urgency, but retained clinician-led prioritisation and explicit override logging.
The impact
What changed in practice
Teams reported better visibility of potential high-priority cases and improved confidence in escalation workflows.
Case Narrative
This exemplar is useful for discussing human-AI collaboration in high-stakes contexts. The implementation emphasised explainability and clear accountability, with no automated diagnosis.
For educators, it provides a practical context for comparing technical performance with workflow safety requirements.
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