Accessible AI-powered skin checks
Building trust in a skin diagnosis app from onboarding to first use.
Background
What is Helfie?
Helfie is an AI-powered app for health checks. As Australians are at a higher risk of developing skin cancer, the app aimed to provide a quick and easy diagnosis on a spot/mole, at $3 per diagnosis from a registered doctor.
Problem
Most people weren’t willing to pay $3 for a diagnosis.
The app aimed to provide easy, affordable access to skin cancer checks.
It only cost $3 to send a photo a spot/mole to a registered doctor for a quick diagnosis, but not many people were using this service.
As a very early-stage product, there were plain gaps in the client’s knowledge:
– They didn’t know their users
– The app wasn’t tested with real people
It only cost $3 to send a photo a spot/mole to a registered doctor for a quick diagnosis, but not many people were using this service.
As a very early-stage product, there were plain gaps in the client’s knowledge:
– They didn’t know their users
– The app wasn’t tested with real people
My Role
This was a live client project I did as part of a design course. I conducted user interviews, usability testing, rapid prototyping and created high-fidelity wireframes.
I worked with two other students, one of the Co-Founders of Helfie, and received guidance from the course instructor.
The project was remote and spanned 2 weeks in Jun 2020.
I worked with two other students, one of the Co-Founders of Helfie, and received guidance from the course instructor.
The project was remote and spanned 2 weeks in Jun 2020.
Testing the current state on a user. Fake mole was drawn with a Sharpie.
PROCESS
Research, define, ideate, prototype and test
Understanding the users
Survey. From a survey with 79 responses recruited via Facebook Ads and a health/fitness group I was part of, we found:
– 52% of people see a GP for skin checks
– 50% go to a skin clinic
– 58% of people go routinely every 6 or 12 months
‍User Interviews. We interviewed 11 people to learn more about Australians’ attitudes towards skin checks. We found:
– A generational gap. Older Australians were more vigilant with going to the GP or a clinic, while younger Australians were less concerned.
– Motivators. Reasons for getting a skin check were noticing a change in skin, having lots of moles, or having family history of skin cancer.
‍
‍Current state usability testing. The main problems were:
– People ignored the AI risk profile because they didn’t understand it
– People didn’t know who the doctors were or if they were qualified
– 52% of people see a GP for skin checks
– 50% go to a skin clinic
– 58% of people go routinely every 6 or 12 months
‍User Interviews. We interviewed 11 people to learn more about Australians’ attitudes towards skin checks. We found:
– A generational gap. Older Australians were more vigilant with going to the GP or a clinic, while younger Australians were less concerned.
– Motivators. Reasons for getting a skin check were noticing a change in skin, having lots of moles, or having family history of skin cancer.
‍
‍Current state usability testing. The main problems were:
– People ignored the AI risk profile because they didn’t understand it
– People didn’t know who the doctors were or if they were qualified
Synthesising findings from our user interviews.
Creating personas and journey maps representing older and younger demographics.
HMW create more trust in Helfie for Theresa and Ray?
Together with the client, we shared the research and workshopped different ideas to solve the problem. We landed on:
– Showing doctors’ credentials up front
– Educating users during key points in the user journey (onboarding, risk profile, connecting with a doctor)
– Showing doctors’ credentials up front
– Educating users during key points in the user journey (onboarding, risk profile, connecting with a doctor)
Solution
Help users understand how the app and the AI worked.
Usability
People didn’t know how to use the app.
To increase usability, we redesigned the onboarding experience and provided clear instructions at key points in the user journey. We also redesigned the risk assessment and made sure users understood what it meant. Â
To increase usability, we redesigned the onboarding experience and provided clear instructions at key points in the user journey. We also redesigned the risk assessment and made sure users understood what it meant. Â
Trust
People didn’t trust the AI risk assessment or the doctors’ credibility.
To increase trust, we showed what the AI was calculating and doctors’ credentials. We also redesigned the chat experience of connecting with a doctor.
To increase trust, we showed what the AI was calculating and doctors’ credentials. We also redesigned the chat experience of connecting with a doctor.
Prototyping and testing
Over 4 days, we took the designs from low-fidelity to high-fidelity. We did:
– 3 rounds of design iterations and testing
– 21 usability tests in total
– 3 rounds of design iterations and testing
– 21 usability tests in total
Testing onboarding in mid-fidelity
Onboarding
AI risk assessment
Connect with a doctor
“.
Outcome
Increased trust in the app.
Results
Qualitative. We received qualitative feedback that the new designs increased user trust.
– Testing the current state, one user said “Is it a real doctor or a bulls*#t doctor?”
– Testing the redesign, one user said “On doing it once, I now have no hesitation of doing it again. And it’d be the sort of thing you’d recommend to other people.”
‍
Quantitative. We asked users at the end of each usability test a simple survey question: “On a scale out of 10, how much do you trust the app?”
– On the first iteration, 5 users gave an average rating of 5.8/10
– On the third iteration, 5 different users gave an average rating of 8.4/10
You could say we increased trust in the app by 45%, but a sample size this small won’t give you reliable quantitative results so...ignore that :)
– Testing the current state, one user said “Is it a real doctor or a bulls*#t doctor?”
– Testing the redesign, one user said “On doing it once, I now have no hesitation of doing it again. And it’d be the sort of thing you’d recommend to other people.”
‍
Quantitative. We asked users at the end of each usability test a simple survey question: “On a scale out of 10, how much do you trust the app?”
– On the first iteration, 5 users gave an average rating of 5.8/10
– On the third iteration, 5 different users gave an average rating of 8.4/10
You could say we increased trust in the app by 45%, but a sample size this small won’t give you reliable quantitative results so...ignore that :)
Client feedback
After we presented to the Co-Founder, they said:
‍”I’m extremely impressed... I can say with pretty high confidence that I’m going to be employing some of these things that you guys have put together...you have already made a difference to the company”
‍”I’m extremely impressed... I can say with pretty high confidence that I’m going to be employing some of these things that you guys have put together...you have already made a difference to the company”
Call the cobs! This person reached the end! Suspicious...
