Work / Carefully

Carefully
Phase II

From a booking app to trust infrastructure.

UX Research· Product Strategy· Behavioral Testing· 2025–26

Phase 1 proved patients wanted Carefully. It also proved I had built the wrong product. Carefully was a booking app, but the value lived in the trust layer that comes before booking. Phase 2 rebuilds it as trust infrastructure and tests one question: do structured trust signals change which provider a patient picks, or only how they feel?

Carefully provider detail screen for Dr. Jennifer Bertife, OB-GYN, showing the Trust Snapshot with listening quality, explanation clarity, and patient patterns, a clinical safety rating, and a prompt to walk in prepared
Carefully · hi-fi prototype

The problem

Stars hide what these patients need to know.

Millions of patients with chronic pain delay or avoid care because they cannot find providers who will take them seriously. A 4.6-star average says nothing about whether you will be interrupted, rushed, or dismissed.

Carefully surfaces the pattern instead of the score, so the people most likely to be dismissed can see it coming.

Built for patients with chronic pain, and bias-aware providers

Who it's for

From depleted to restored, on both sides.

Primary user Patients with chronic pain
Anxious Relieved
Before

In despair over finding a provider who fits their needs. In constant pain, and powerless to change it.

After

They can see how a provider actually treats patients before they book, walk in with their concerns already organized, and finally feel heard and in control.

Secondary user Nurses, physicians, and specialists
Burnt out Refreshed
Before

Demoralized by defensive, distrustful encounters, with their relational skill going unseen and their idealism wearing thin.

After

Patients arrive pre-matched and prepared, so visits start from trust instead of suspicion, and the relational work that drew them to medicine is what gets them chosen.

The pivot

Real problems are specific.

Phase 1 testing (n=6, SUS 81.5) showed the trust idea landed, and exposed three failures: dead navigation, low-fidelity visuals that cost credibility, and too few real options. The fix was not a polish pass. Carefully stopped being a booking app and became a layer patients use before they book elsewhere.

Phase IPhase II
Booking app with provider vetting
Trust layer used before booking
Star ratings
Behavioral trust signals
Generic review snippets
Pattern Insights and Trust Snapshot
In-app booking
External booking redirect
No post-visit loop
Pre-Visit Journal and opt-in feedback
Low-fidelity prototype
High-fidelity prototype, full design system

The build

01

Trust Signal Badges

Behavioral patterns like "listens well" and "rushed visits noted" replace stars, so patients know what to expect before they book.

02

Pre-Visit Journal with AI Translation

Patients log their concerns and get a clinically legible summary plus questions to ask, so they walk in prepared, not surprised.

03

Post-Visit Journal

A private care record that, with per-entry opt-in, feeds the trust layer, turning one patient's experience into the next patient's signal.

The demo

See it in action.

The test

Sentiment is not behavior.

Phase 1 asked whether people liked the idea. Phase 2 asks whether it changes how they choose. A randomized, unmoderated A/B study (n=30, standard listing versus trust-signal listing) measures whether patients switch providers when the signals appear, not just how they feel about them.

20%
Minimum switch rate after seeing trust signals
60%
Would consult Carefully before booking real care
82
SUS target, holding or beating Phase 1
Sessions running June 1 to June 28, 2026. Results in progress.

Next: does the data move?

The behavioral results come next. If you have ever felt dismissed at a doctor's visit and want to help shape this, I am recruiting Philadelphia-area participants.

Get involved