APN Virtual
Match Made in Wellness
A teletherapy app that matches you with the best therapist suited for your needs.
Role
User Researcher/ Lead Designer
Timeline
12 months, 2023
Overview
APN Virtual is a teletherapy app that curates personalized therapy plans, connecting you with certified professionals for a tailored experience.
Personalized experience
AI-driven matching algorithms and data analytics enhance therapist-client compatibility by assessing client needs and virtual consultation insights, ensuring the best fit.
Flexible scheduling
Easily reschedule or cancel to accommodate busy and unpredictable schedules.
Iterative Design
I’d like to highlight a few significant explorations from the various iterations I've worked through with the feedback given from the first launch.
After
Before
Tab Bar
In the first iteration, the tab bar featured five buttons: Calendar, Messages, Home, Notifications, and Profile. While this provided multiple navigation options, we realized that having five tabs felt cluttered, especially on smaller screens.
For the second iteration, we streamlined the design by merging Home, Profile, and Calendar into a single tab called You. This new section centralized everything related to the client’s schedule, care team, and profile, making navigation more intuitive.
Through user feedback, we discovered that clients had difficulty finding all available services, so we introduced a Browse tab for better accessibility. To further enhance clarity, we also added text labels below each icon, ensuring users could easily understand their function.
Before
After
Onboarding & Virtual Consultation Booking
In the first iteration, users had to wait up to 48 hours after onboarding to schedule their virtual consultation, leading to high drop-off rates and delays. Based on user feedback, we streamlined the process, allowing clients to immediately book their consultation upon completing account setup. This flexibility led to a 60% increase in customer retention, as users could schedule their session at their own convenience.
Before
After
In the first iteration of the app launch, users were only given an assessment to help us understand their needs. In the second iteration, we integrated AI and a complimentary virtual consultation, allowing our team to speak directly with clients for a deeper understanding of their preferences and expectations. AI provides structured insights from the onboarding questionnaire, but the care advisor plays a critical role in validating, refining, and personalizing the therapist match.
Integrating the OpenAI API into the app will help analyze user responses by detecting sentiment, assessing severity levels, and categorizing users. AI will then summarize the answers and suggest potential therapy types based on the data. A care advisor will review this information to catch any flagged concerns, pick up on non-verbal cues that AI might have missed, and refine the assessment. They’ll also use these insights to personalize follow-up questions and filter user preferences, like therapy and communication styles, to ensure a better match.
Future AI Implementation Goals:
• AI therapy support chatbot – Provides 24/7 emotional support for non-crisis situations.
• AI-powered journaling assistant – Helps users reflect on their thoughts by prompting them with guided questions.
• Personalized mental health insights – AI analyzes user input over time and provides insights into mood trends.
Through user interviews, I found that the biggest challenge was finding the right therapist. Users also expressed that they value human, face-to-face connection, and adding a virtual consultation allowed us to build trust and gain better insight into what they’re looking for. By refining the assessment and matching process, we aimed to create a more personalized teletherapy experience, ensuring clients connect with the right therapist for their needs.
Finding the Right Therapist
Before
After
In the first iteration, clients had to request session reschedules, leading to frustration and unnecessary back-and-forth with admin. In the second iteration, we gave users the ability to reschedule freely at their convenience. I also implemented a feedback modal to track cancellations, reschedules, and therapist switches, allowing us to gather valuable insights and continuously improve our services and user experience.
Flexible Scheduling
Outcomes
By streamlining the onboarding, virtual consultation, and rescheduling process, we significantly reduced user frustration and administrative overhead. Additionally, we enhanced the matching process by integrating AI-driven insights during onboarding and introducing a virtual consultation, allowing users to connect with a team member for a more personalized therapist recommendation. This combination of AI and human touch significantly improved the matching process, leading to a 15% increase in customer retention and a 30% drop in onboarding abandonment rates, based on early user data and feedback.
Learnings
Designing for telehealth comes with unique challenges, especially when navigating HIPAA compliance, data security, and regulatory constraints. This project taught me how complex it can be to balance user experience, accessibility, and legal requirements while ensuring that clients feel supported and empowered throughout their journey.
I also learned the importance of thorough UX research and continuously validating insights through testing. While it was tempting to rely on inherent biases or jump straight into high-fidelity designs, this project reinforced that design is never an isolated process. The feedback from the first iterations proved just how essential it is to invest in research upfront, not only to create a better user experience but also to effectively communicate findings with stakeholders, ultimately saving significant time and costs.
But before all this, let's go back to the COVID times…
Lockdown left many of us feeling isolated and lonely, and the shift in mental health care was just as dramatic.
Nearly 76% of mental health professionals were offering teletherapy exclusively.
Problem
How can we make a teletherapy app that feels personal and truly supports clients' needs?
Many teletherapy services fail to match clients with the right therapist, leading to drop-off and a lack of personalized care.
In 2023, BetterHelp had grown to ~370,000 active users, while Talkspace had ~11,700. The mental health market is growing annually at 4.8%. (Everyday Health)
Research
Conversations with therapy goers
I gathered 15 users who fit the target audience, including therapy veterans with 3+ years of experience, newcomers who just started, and individuals who have never attended therapy before.
Target Audience
• Veteran therapy go-ers (1+ years)
• New therapy go-ers (6 months or less)
• Male (40%) & female (60%)
• Medium to high income group
• Ages between 25-35
After identifying our target audiences, we began designing questions to explore during the user interviews. Some main questions we asked were:
1
What therapy platforms have you used in the past?
2
What are the most important factors for you when selecting a therapy platform?
3
What's the most you'd pay per session? ($0–$25, $26–$50, $51–$100,
$101–$150, $150+)
Some feedback were:
Common pain points:
Difficulty finding the right therapist
80% of clients have used BetterHelp or Talkspace, but later switched to other options. These platforms rely on brief questionnaires during onboarding, which doesn't allow them to truly understand their clients needs and pair the with the best therapist for them.
Quality over cost-effective options
All clients who started with affordable therapy options soon realized they were willing to invest more for higher-quality therapists and services. I soon noticed a pattern with those I interviewed, that people who have been going to therapy for 1+ years are willing to pay more for their sessions than those who just started off. After talking with them, it seems like the veterans value quality over affordability.
Limited time
Clients are busy and have unpredictable schedules sometimes. They want therapy that fits seamlessly into their lives, with flexible scheduling and communication options (text/video).
Research
Business goals
Scale Operations and Expand Market Reach
Revenue Metrics:
Launch 2 new service offerings (e.g., group therapy, workshops) within 12 months.
Achieve a 30% increase in monthly active users (MAU) after each expansion phase.
Increase User Acquisition/Retention
Engagement Metrics:
Freemium + Subscription Model:
Free virtual consultation + therapist matching, pay subscription to book a session.
Maintain a user retention rate of 60% after 6 months.
Achieve a 10% month-over-month increase in new user sign-ups.
Reduce churn rate to less than 10% annually.
Leverage Technology for Better Outcomes
AI Metrics:
Improve therapist-client matching accuracy to 95% based on user feedback.
Increase client-reported therapy satisfaction scores by 15% within 6 months of implementing AI-driven tools.
The Product Triad
HYPOTHESIS
If a teletherapy app offers personalized care by matching clients with high-quality therapists who best fit their unique needs, clients will have a more positive therapy experience and no longer need to switch between multiple teletherapy apps.
Research
Mapping user emotions
Based on user interviews, I created two personas that best represent our target audience. Creating this empathy map would be helpful as I'm able to check back regularly on the users needs and wants.
Based off of Brandon's persona and empathy map, I decided that it would be extremely helpful to create a journey map that showcases his experience signing up and booking a session with a therapist.
Competitive Analysis
Who's leading the pack
From user interviews, I analyzed the three most popular teletherapy apps to see if they met key user needs. Brightside Therapy scored the highest, but lacked personalization, which was one of the most important features according to the clients.
MVP
Based on the user interviews and feedback, it has led me to focus on these for the initial launch:
• Onboarding/Assessment
• Therapist's profile
• Booking/Rescheduling sessions
Information Architecture
Mapping the anatomy of the experience
Timelapse of my team and I building the site map
The completed first round of edits
User Flow
Wireframe
Drop me a line
© 2024 Jennifer Hu. All Rights Reserved.
Made with love and Jasmine green milk tea (75% sugar, less ice).
Drop me a line
© 2024 Jennifer Hu. All Rights Reserved.
Made with love and Jasmine green milk tea (75% sugar, less ice).
APN Virtual
Match Made in Wellness
A teletherapy app that matches you with the best therapist suited for your needs.
Role
User Researcher
Lead Designer
Timeline
12 months, 2023
Overview
APN Virtual is a teletherapy app that curates personalized therapy plans, connecting you with certified professionals for a tailored experience.
Personalized experience
AI-driven matching algorithms and data analytics enhance therapist-client compatibility by assessing client needs and virtual consultation insights, ensuring the best fit.
Flexible scheduling
Easily reschedule or cancel to accommodate busy and unpredictable schedules.
Iterative Design
I’d like to highlight a few significant explorations from the various iterations I've worked through with the feedback given from the first launch.
Before
After
Tab Bar
In the first iteration, the tab bar featured five buttons: Calendar, Messages, Home, Notifications, and Profile. While this provided multiple navigation options, we realized that having five tabs felt cluttered, especially on smaller screens.
For the second iteration, we streamlined the design by merging Home, Profile, and Calendar into a single tab called You. This new section centralized everything related to the client’s schedule, care team, and profile, making navigation more intuitive.
Through user feedback, we discovered that clients had difficulty finding all available services, so we introduced a Browse tab for better accessibility. To further enhance clarity, we also added text labels below each icon, ensuring users could easily understand their function.
Before
After
Onboarding & Virtual Consultation Booking
In the first iteration, users had to wait up to 48 hours after onboarding to schedule their virtual consultation, leading to high drop-off rates and delays. Based on user feedback, we streamlined the process, allowing clients to immediately book their consultation upon completing account setup. This flexibility led to a 60% increase in customer retention, as users could schedule their session at their own convenience.
Before
After
Finding the Right Therapist
In the first iteration of the app launch, users were only given an assessment to help us understand their needs. In the second iteration, we integrated AI and a complimentary virtual consultation, allowing our team to speak directly with clients for a deeper understanding of their preferences and expectations. AI provides structured insights from the onboarding questionnaire, but the care advisor plays a critical role in validating, refining, and personalizing the therapist match.
Integrating the OpenAI API into the app will help analyze user responses by detecting sentiment, assessing severity levels, and categorizing users. AI will then summarize the answers and suggest potential therapy types based on the data. A care advisor will review this information to catch any flagged concerns, pick up on non-verbal cues that AI might have missed, and refine the assessment. They’ll also use these insights to personalize follow-up questions and filter user preferences, like therapy and communication styles, to ensure a better match.
Future AI Implementation Goals:
• AI therapy support chatbot – Provides 24/7 emotional support for non-crisis situations.
• AI-powered journaling assistant – Helps users reflect on their thoughts by prompting them with guided questions.
• Personalized mental health insights – AI analyzes user input over time and provides insights into mood trends.
Through user interviews, I found that the biggest challenge was finding the right therapist. Users also expressed that they value human, face-to-face connection, and adding a virtual consultation allowed us to build trust and gain better insight into what they’re looking for. By refining the assessment and matching process, we aimed to create a more personalized teletherapy experience, ensuring clients connect with the right therapist for their needs.
Before
After
Flexible Scheduling
In the first iteration, clients had to request session reschedules, leading to frustration and unnecessary back-and-forth with admin. In the second iteration, we gave users the ability to reschedule freely at their convenience. I also implemented a feedback modal to track cancellations, reschedules, and therapist switches, allowing us to gather valuable insights and continuously improve our services and user experience.
Outcomes
By streamlining the onboarding, virtual consultation, and rescheduling process, we significantly reduced user frustration and administrative overhead. Additionally, we enhanced the matching process by integrating AI-driven insights during onboarding and introducing a virtual consultation, allowing users to connect with a team member for a more personalized therapist recommendation. This combination of AI and human touch significantly improved the matching process, leading to a 15% increase in customer retention and a 30% drop in onboarding abandonment rates, based on early user data and feedback.
Learnings
Designing for telehealth comes with unique challenges, especially when navigating HIPAA compliance, data security, and regulatory constraints. This project taught me how complex it can be to balance user experience, accessibility, and legal requirements while ensuring that clients feel supported and empowered throughout their journey.
I also learned the importance of thorough UX research and continuously validating insights through testing. While it was tempting to rely on inherent biases or jump straight into high-fidelity designs, this project reinforced that design is never an isolated process. The feedback from the first iterations proved just how essential it is to invest in research upfront, not only to create a better user experience but also to effectively communicate findings with stakeholders, ultimately saving significant time and costs.
But before all this, let's go back to the COVID times…
Lockdown left many of us feeling isolated and lonely, and the shift in mental health care was just as dramatic.
Nearly 76% of mental health professionals were offering teletherapy exclusively.
Problem
How can we make a teletherapy app that feels personal and truly supports clients' needs?
Many teletherapy services fail to match clients with the right therapist, leading to drop-off and a lack of personalized care.
In 2023, BetterHelp had grown to ~370,000 active users, while Talkspace had ~11,700. The mental health market is growing annually at 4.8%. (Everyday Health)
Research
Conversations with therapy goers
I gathered 15 users who fit the target audience, including therapy veterans with 3+ years of experience, newcomers who just started, and individuals who have never attended therapy before.
Target Audience
• Veteran therapy go-ers (1+ years)
• New therapy go-ers (6 months or less)
• Male (40%) & female (60%)
• Medium to high income group
• Ages between 25-40
APN Virtual
Match Made in Wellness
A teletherapy app that matches you with the best therapist suited for your needs.
Role
User Researcher
Lead Designer
Timeline
12 months, 2023
Overview
APN Virtual is a teletherapy app that curates personalized therapy plans, connecting you with certified professionals for a tailored experience.
Personalized experience
AI-driven matching algorithms and data analytics enhance therapist-client compatibility by assessing client needs and virtual consultation insights, ensuring the best fit.
Flexible scheduling
Easily reschedule or cancel to accommodate busy and unpredictable schedules.
Iterative Design
I’d like to highlight a few significant explorations from the various iterations I've worked through with the feedback given from the first launch.
Before
After
Tab Bar
In the first iteration, the tab bar featured five buttons: Calendar, Messages, Home, Notifications, and Profile. While this provided multiple navigation options, we realized that having five tabs felt cluttered, especially on smaller screens.
For the second iteration, we streamlined the design by merging Home, Profile, and Calendar into a single tab called You. This new section centralized everything related to the client’s schedule, care team, and profile, making navigation more intuitive.
Through user feedback, we discovered that clients had difficulty finding all available services, so we introduced a Browse tab for better accessibility. To further enhance clarity, we also added text labels below each icon, ensuring users could easily understand their function.
Before
After
Onboarding & Virtual Consultation Booking
In the first iteration, users had to wait up to 48 hours after onboarding to schedule their virtual consultation, leading to high drop-off rates and delays. Based on user feedback, we streamlined the process, allowing clients to immediately book their consultation upon completing account setup. This flexibility led to a 60% increase in customer retention, as users could schedule their session at their own convenience.
Before
After
Finding the Right Therapist
In the first iteration of the app launch, users were only given an assessment to help us understand their needs. In the second iteration, we integrated AI and a complimentary virtual consultation, allowing our team to speak directly with clients for a deeper understanding of their preferences and expectations. AI provides structured insights from the onboarding questionnaire, but the care advisor plays a critical role in validating, refining, and personalizing the therapist match.
Integrating the OpenAI API into the app will help analyze user responses by detecting sentiment, assessing severity levels, and categorizing users. AI will then summarize the answers and suggest potential therapy types based on the data. A care advisor will review this information to catch any flagged concerns, pick up on non-verbal cues that AI might have missed, and refine the assessment. They’ll also use these insights to personalize follow-up questions and filter user preferences, like therapy and communication styles, to ensure a better match.
Future AI Implementation Goals:
• AI therapy support chatbot – Provides 24/7 emotional support for non-crisis situations.
• AI-powered journaling assistant – Helps users reflect on their thoughts by prompting them with guided questions.
• Personalized mental health insights – AI analyzes user input over time and provides insights into mood trends.
Through user interviews, I found that the biggest challenge was finding the right therapist. Users also expressed that they value human, face-to-face connection, and adding a virtual consultation allowed us to build trust and gain better insight into what they’re looking for. By refining the assessment and matching process, we aimed to create a more personalized teletherapy experience, ensuring clients connect with the right therapist for their needs.
Before
After
Flexible Scheduling
In the first iteration, clients had to request session reschedules, leading to frustration and unnecessary back-and-forth with admin. In the second iteration, we gave users the ability to reschedule freely at their convenience. I also implemented a feedback modal to track cancellations, reschedules, and therapist switches, allowing us to gather valuable insights and continuously improve our services and user experience.
Outcomes
By streamlining the onboarding, virtual consultation, and rescheduling process, we significantly reduced user frustration and administrative overhead. Additionally, we enhanced the matching process by integrating AI-driven insights during onboarding and introducing a virtual consultation, allowing users to connect with a team member for a more personalized therapist recommendation. This combination of AI and human touch significantly improved the matching process, leading to a 15% increase in customer retention and a 30% drop in onboarding abandonment rates, based on early user data and feedback.
Learnings
Designing for telehealth comes with unique challenges, especially when navigating HIPAA compliance, data security, and regulatory constraints. This project taught me how complex it can be to balance user experience, accessibility, and legal requirements while ensuring that clients feel supported and empowered throughout their journey.
I also learned the importance of thorough UX research and continuously validating insights through testing. While it was tempting to rely on inherent biases or jump straight into high-fidelity designs, this project reinforced that design is never an isolated process. The feedback from the first iterations proved just how essential it is to invest in research upfront, not only to create a better user experience but also to effectively communicate findings with stakeholders, ultimately saving significant time and costs.
But before all this, let's go back to the COVID times…
Lockdown left many of us feeling isolated and lonely, and the shift in mental health care was just as dramatic.
Nearly 76% of mental health professionals were offering teletherapy exclusively.
Problem
Many teletherapy services fail to match clients with the right therapist, leading to drop-off and a lack of personalized care.
In 2023, BetterHelp had grown to ~370,000 active users, while Talkspace had ~11,700. The mental health market is growing annually at 4.8%. (Everyday Health)
How can we create a teletherapy app that connects clients with the right therapists for personalized care?
Research
Conversations with therapy goers
I gathered 15 users who fit the target audience, including therapy veterans with 3+ years of experience, newcomers who just started, and individuals who have never attended therapy before.
Target Audience
• Veteran therapy go-ers (1+ years)
• New therapy go-ers (6 months or less)
• Male (40%) & female (60%)
• Medium to high income group
• Ages between 25-40
After identifying our target audiences, I began designing questions to explore during the user interviews.
Some main questions I asked were:
1
What therapy platforms have you used in the past?
2
What are the most important factors for you when selecting a therapy platform?
3
What's the most you'd pay per session? ($0–$25, $26–$50, $51–$100,
$101–$150, $150+)
Some feedback were:
Common pain points:
Difficulty finding the right therapist
80% of clients have used BetterHelp or Talkspace, but later switched to other options. These platforms rely on brief questionnaires during onboarding, which doesn't allow them to truly understand their clients needs and pair the with the best therapist for them.
80% of clients have used BetterHelp or Talkspace, but later switched to other options. These platforms rely on brief questionnaires during onboarding, which doesn't allow them to truly understand their clients needs and pair the with the best therapist for them.
Quality over cost-effective options
All clients who started with affordable therapy options soon realized they were willing to invest more for higher-quality therapists and services. I soon noticed a pattern with those I interviewed, that people who have been going to therapy for 1+ years are willing to pay more for their sessions than those who just started off. After talking with them, it seems like the veterans value quality over affordability.
Limited time
Clients are busy and have unpredictable schedules sometimes. They want therapy that fits seamlessly into their lives, with flexible scheduling and communication options (text/video).
Research
Business goals
Scale Operations and Expand Market Reach
Revenue Metrics:
Launch 2 new service offerings (e.g., group therapy, workshops) within 12 months.
Achieve a 30% increase in monthly active users (MAU) after each expansion phase.
Increase User Acquisition/Retention
Engagement Metrics:
Freemium + Subscription Model:
Free virtual consultation + therapist matching, pay subscription to book a session.
Maintain a user retention rate of 60% after 6 months.
Achieve a 10% month-over-month increase in new user sign-ups.
Reduce churn rate to less than 10% annually.
Launch 2 new service offerings (e.g., group therapy, workshops) within 12 months.
Achieve a 30% increase in monthly active users (MAU) after each expansion phase.
Leverage Technology for Better Outcomes
AI Metrics:
Improve therapist-client matching accuracy to 95% based on user feedback.
Increase client-reported therapy satisfaction scores by 15% within 6 months of implementing AI-driven tools.
The Product Triad
HYPOTHESIS
If a teletherapy app offers personalized care by matching clients with high-quality therapists who best fit their unique needs, clients will have a more positive therapy experience and no longer need to switch between multiple teletherapy apps.
Research
Business goals
Increase User Acquisition/Retention
Engagement Metrics:
Freemium + Subscription Model:
Free virtual consultation + therapist matching, pay subscription to book a session.
Maintain a user retention rate of 60% after 6 months.
Achieve a 10% month-over-month increase in new user sign-ups.
Reduce churn rate to less than 10% annually.
Scale Operations and Expand Market Reach
Revenue Metrics:
Launch 2 new service offerings (e.g., group therapy, workshops) within 12 months.
Achieve a 30% increase in monthly active users (MAU) after each expansion phase.
Leverage Technology for Better Outcomes
AI Metrics:
Improve therapist-client matching accuracy to 95% based on user feedback.
Increase client-reported therapy satisfaction scores by 15% within 6 months of implementing AI-driven tools.
The Product Triad
HYPOTHESIS
If a teletherapy app offers personalized care by matching clients with high-quality therapists who best fit their unique needs, clients will have a more positive therapy experience and no longer need to switch between multiple teletherapy apps.
Research
Mapping user emotions
Based on user interviews, I created two personas that best represent our target audience. Creating this empathy map would be helpful as I'm able to check back regularly on the users needs and wants.
Click the image above to view the enlarged map
Based off of Brandon's persona and empathy map, I decided that it would be extremely helpful to create a journey map that showcases his experience signing up and booking a session with a therapist.
Click the image above to view the enlarged chart
Competitive Analysis
Who's leading the pack
From user interviews, I analyzed the three most popular teletherapy apps to see if they met key user needs. Brightside Therapy scored the highest, but lacked personalization, which was one of the most important features according to the clients.
Click the image above to view the enlarged comparison chart
MVP
Based on the user interviews and feedback, it has led me to focus on these for the initial launch:
• Onboarding/Assessment
• Therapist's profile
• Booking/Rescheduling sessions
Information Architecture
Mapping the anatomy of the experience
Timelapse of my team and I building out the site map
The completed first round of edits
User Flow
Wireframe
Wireframe
Drop me a line
© 2024 Jennifer Hu. All Rights Reserved.
Made with love and Jasmine green milk tea (75% sugar, less ice).
Drop me a line
© 2024 Jennifer Hu. All Rights Reserved.
Made with love and Jasmine green milk tea (75% sugar, less ice).
Drop me a line
© 2024 Jennifer Hu. All Rights Reserved.
Made with love and Jasmine green milk tea (75% sugar, less ice).