— Door 04 · Capacity, honestly shown
One strategist. A team's throughput.
The honest pitch: AI handles the routine half — market & literature scanning, data tabulation, evaluation tagging, first-draft analysis, KPI dashboards — so the human half (clinical-safety judgement, stakeholder alignment, accuracy, what the data means) gets all of me. The panel below is an illustrative concept, not live data.
- Scan the market & the evidence basescanned
- Tabulate & clean programme datastructured
- Tag outputs against the evaluation rubricscored
- Draft first-pass analysis & KPI viewsdrafted
- Format the strategy deliverabletemplated
- What the data actually meanshuman
- Is every claim evidence-backed?human
- Clinical safety & risk decisionswith clinical SMEs
- Stakeholder alignment & judgementhuman
- Final strategy sign-offhuman
Workstream
End-to-end
research → eval → strategy
Readiness model
5 levels
place every initiative
Eval rubric
Weighted
accuracy + safety first
Unverified claims
0
the only acceptable number
AI evaluation feed · illustrative
"The monitoring feed has a 12% false-alert rate against the rubric — I've drafted the trust-gate fix and held the affected alerts out of the clinical view."
"Two recommendations in the draft touch on clinical thresholds — flagged for clinical SME confirmation and held out of the strategy until verified."
"Executive summary first pass is drafted with the KPI model — ready for your edit and the strategic judgement only you can add."
How the engagement runs
A line I won't blur
AI accelerates research and evaluation. It never decides what's clinically safe, it never overrides a clinician, and it never substitutes for domain expertise. Every recommendation, every data-quality call that touches patient care, is verified by a human — me — in a tight loop with clinical and technical subject-matter experts. This dashboard is a concept to show the workflow, not a live system.