Product design question

Clinicians report that an Abridge-generated note is 'usually right,' but they still review every line before signing. How would you determine whether the trust gap comes from factual errors, missing high-salience details, poor transparency, workflow friction, or medicolegal risk perception? Based on that diagnosis, what product changes would you make to increase trust without encouraging unsafe over-reliance?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Tests root cause diagnosis of a trust gap in AI generated clinical notes and designing fixes that build appropriate, not blind, trust.

Clarify the question: is the gap driven by factual errors, missing salient details, poor transparency, workflow friction, or medicolegal fear.
Pull objective error data first, for example correction rates per note by specialty, to see if perception matches actual accuracy.
Interview clinicians who review every line despite low error rates, since that gap points to transparency or liability fear, not quality.
Check whether the note shows provenance, since opacity about what is transcript versus inference drives blanket re review.
Design targeted fixes from the diagnosis, such as inline confidence highlighting for low confidence sections, while still requiring review of medication and allergy lines.

Separates the four hypotheses and proposes a distinct test for each, for example error audits for accuracy and surveys for liability fear.
Proposes provenance highlighting as a concrete fix, marking transcript derived sentences versus inferred ones so clinicians can skim confidently.
Explicitly protects against overtrust, always requiring active review of medication and diagnosis lines even as overall trust improves.
Ties the fix to a measurable outcome, for example reducing average review time per note without increasing downstream chart corrections.

Assuming the gap is purely an accuracy problem and building more QA instead of addressing transparency or liability fear.
Designing a fix that cuts review time so much it risks missing a real error in a high stakes section.

Chapters of the AI PM course that teach what this question tests.