Quinn wird geladen...
Research
Evaluation design for review-first AI care documentation.
Quinn research means clear boundaries, pilot metrics and traceable evidence. We do not claim finished results; we measure whether Quinn creates demand, usage, draft quality and relief in care workflows.
What we measure
Pilot metrics instead of hype numbers.
These metrics show whether Quinn is more than a good demo: minutes per case, edit rate, completeness, review effort and team acceptance have to fit together.
Minutes per case
Time from conversation, audio or notes to a reviewable draft.
Edit rate
How much needs to be corrected, completed or rejected before a draft fits?
Completeness
How reliably do open facts, risks, wishes and gaps become visible?
Care/QM review effort
How does review effort change for care leadership, quality teams or professionals?
Team acceptance
Do teams use Quinn repeatedly and experience the workflow as relief?
seed-ready evidence
What needs to be credible before a seed round.
The measurement logic separates hypothesis from evidence: Quinn should not sound louder, but get to verifiable pilot data faster.
Demand: qualified conversations with care providers, care leadership, quality teams or operators.
Usage: repeated usage in one bounded intake, SIS-style or review workflow.
Quality: drafts mostly need light edits rather than complete rewriting.
ROI signal: measurable reduction in minutes per case or review effort, without a generic promise.
Seed-ready evidence: paid pilots, clear buyer learnings, processing path and repeatable pilot structure.