• integrated_82#Q4.9

RAIDT can be used in healthcare as an operational governance method for specific generative-AI uses such as clinical note summarisation, triage preparation, discharge communication, and safety-case review. Across the papers, the central move is to treat the run as the unit of governance, so that each clinically significant output is accompanied by a run-level evidence pack rather than being justified only by policy statements or model cards. In practice, this means logging the note version, prompt/template IDs, model and tool settings, retrieved context where used, output text, safety flags, reviewer comments, and any escalation or refusal decisions. The resulting evidence can then be scored through the five pillars (Responsibility, Auditability, Interpretability, Dependability, Traceability), producing a score profile for each run.

This matters particularly in healthcare because fluent output alone is not a sufficient assurance signal. The healthcare playbook shows that baseline summarisation may appear useful while still missing red flags, weakly supporting review, or lacking reconstruction evidence. RAIDT therefore uses anchors 1=missing / 3=partial / 5=audit-ready to assess whether a run can actually be checked, challenged, and overseen. The empirical paper further shows that influence methods as governance interventions alter different parts of the profile: structured prompting can improve interpretability and responsibility; RAG can strengthen auditability and traceability when retrieval snapshots are retained; and stacked configurations can improve evidence coverage, though with added complexity. In ageing-sensitive healthcare settings, this run-level approach also supports inclusion, contestability, and human oversight for vulnerable patients, because outputs can be reviewed, explained, and escalated before they shape care decisions.

A hospital deploys a model to summarise outpatient cardiology notes into four fields: symptoms, diagnosis, treatment, and red flags. Under RAIDT, each summary run is logged with the pseudonymised record ID, note version, prompt version, model deployment ID, any retrieved reference passages, the generated summary, and the reviewer?s comments. A clinician then checks whether pulmonary hypertension, medication risks, or other urgent concerns were surfaced and whether the output should be accepted, amended, or escalated.

The team scores every sampled run against the fixed RAIDT rubric and compares the score profile across baseline prompting, structured prompting, and RAG. If RAG improves traceability and auditability but a prompt-only setup continues to omit red flags, the organisation has concrete evidence for changing configuration rather than relying on vendor claims. Over time, the run-level evidence pack also supports a healthcare safety case: reviewers can reconstruct what happened, show why a summary was accepted, and identify where human oversight remains mandatory.

• 09-RAIDT_Empirical_M_V50.docx
• 20-RAIDT_AgeingSoc_M_V50
• 21-RAIDT_Sector_Playbook_Healthcare_V2