Designing Trust into AI-Assisted Expert Work
Digitising Wound Care for Elderly Patients
Digitising Wound Care for Elderly Patients
Digitising Wound Care for Elderly Patients
Digitising Wound Care for Elderly Patients
AI concept exploration across two shipped 0→1 products — Statsland (ESG platform) Zonder (healthcare)
Transforming paper-based workflows into an intuitive digital solution that helps clinicians provide better care for elderly patients with chronic ulcer wounds.
"What happens when you hand a complex, high-stakes task to AI, and the person still has to trust the result?"
"What happens when you hand a complex, high-stakes task to AI, and the person still has to trust the result?"
AI flags what it can't verify, instead of quietly presenting it as fact.
TL;DR
Two products I designed in unrelated domains, an ESG data platform for financial analysts and a wound-assessment tool for elderly clinicians, taught me the same thing about AI: neither user can tolerate it being confidently wrong. I designed against one rule: speed the expert up, without ever quietly taking the decision away from them.
Principles applied to trustworthy AI
Draft, don't decide
Show the seam
Fail loud, not silent
Project 1
Statsland AI-assisted data workflows
What I shipped
ESG data platform. On time, 20+ partners, 100+ companies, fewer errors.
How I reimagined it using AI
Manual entry, manual filtering. Now AI drafts, human confirms.
Project 1
Statsland AI-assisted data workflows
What I shipped
ESG data platform. On time, 20+ partners, 100+ companies, fewer errors.
How I reimagined it using AI
Manual entry, manual filtering. Now AI drafts, human confirms.
Entry point
The provider decides; nothing happens without this choice.
Draft, don't decide
AI works alone here, but commits nothing on its own.
Show the seam during success
Every AI-drafted field is visibly provisional until someone says otherwise.
Fail loud, not silent, upload
A gap AI admits to is safer than a guess it doesn't.
Show the seam, then draft, don't decide
Overriding a filter manually turns it from AI-set to human-confirmed.
Fail loud, not silent, discovery
One flagged number doesn't cast doubt on the rest of the page.
Show the seam, after the fact
Trust is a record, not just a moment. This outlives the review itself.
Same three principles. A different domain, a different failure mode, and one new proof point: trust has to survive a human changing its mind, not just a human agreeing with it.
Project 2
Zonder AI-assisted wound assessment
What I shipped
Digital assessment tool. 1.5hrs to ~14min, 92% preferred it, 59% more throughput.
How I reimagined it using AI
Manual documentation mid-treatment. Now AI drafts from the visit, clinician confirms.
Entry point
Clinicians can switch to AI-assisted recording anytime
Show the seam during success
AI-suggested answers are confirmed or overridden by the clinician
Fail loud, not silent in visual interface
When AI isn't confident in a photo, it flags it for review instead of guessing
Draft, don't decide
Recording is in progress & nothing is saved yet
Fail loud, not silent in voice interface
When AI can't confidently answer, it says so instead of guessing
Next steps (if I cont'd)
1st Open question: does drafting from ambient audio or an extracted report actually save time, or does reviewing and correcting AI's draft just replace typing time with editing time? That's a usability study, not an assumption, and it applies to both flows equally.
2nd open question: AI can be confidently wrong, not just honestly uncertain. Both concepts only design for the second failure mode. Where's the check for the first?
