CareAI: Healthcare Assistant
A healthcare assistant app designed to simplify the process of finding and booking providers based on user symptoms. It helps users navigate care decisions with AI-guided recommendations, insurance aware results, and a seamless booking experience.
Role: UX/UI Designer & Researcher.
Duration: 6 weeks.
Tools: Figma, Miro.
Problem
Navigating complex health care systems often lead users to feeling overwhelmed when trying to triage the type of care they need. Users need a guided and streamed line approach to identifying current issues and finding care rather than fragmented tools like Google or insurance portals.
Who am I designing for?
This experience is designed for individuals who are seeking medical care but may feel unsure about where to start. These users often rely on quick online searches and want clear, trustworthy guidance to help them make decisions. They value simplicity, speed, and confidence—especially when navigating something as stressful and important as their health.
Listening Before Solving
I aimed to understand how people navigate healthcare decisions when experiencing unfamiliar or urgent symptoms, what challenges and emotional stress they face during this process, and how they determine where to seek care.
The goal was to uncover insights that would guide the design of a more supportive and intuitive experience, one that reduces confusion, helps users make informed decisions, and increases confidence when choosing the right type of care.
This plan included:
User Interviews.
Secondary Research
User Interviews
I recruited 5 participants with different levels of experience navigating healthcare, including individuals who rely on insurance apps, online searches, and personal judgment when seeking care. The goal was to understand their behaviors, frustrations, and decision-making process when dealing with symptoms, as well as what would make finding the right care feel easier, faster, and more trustworthy.
Competitive Analysis
After conducting user interviews, I explored the tools participants currently use to find care, such as Zocdoc, insurance apps, and online search tools. This helped me understand the existing solutions in the market and identify where they succeed and where they fall short.
Key Insights
Users rely on multiple tools instead of one solution
Platforms focus on search, not guidance
Insurance apps provide data, but lack clarity
AI tools provide answers, but lack trust and real integration
Opportunity
This revealed an opportunity to design a more guided, all-in-one experience that supports users from symptoms to provider selection in a clear and confident way.
Affinity Map
After conducting user interviews, I organized the findings into an affinity map to identify patterns and uncover key themes. This process helped me move from raw data to clear, actionable insights by grouping responses around areas such as decision-making challenges, insurance concerns, trust, and the need for guidance. Visualizing these patterns made it clear that users were not struggling with access to information, but with making confident decisions in highlighting the importance of clarity, simplicity, and support in the design.
Challenges
Users rely on multiple tools like Google, insurance apps, and reviews to make healthcare decisions
Confusion around choosing the right type of care (ER, urgent care, specialist)
Too much scattered information makes the process time-consuming and overwhelming
What Users Wanted
Clear, step-by-step guidance on where to go
Providers that match their insurance without extra effort
Fast, simple access to relevant and trustworthy information
Emotional Motivations
Users want to feel confident they are making the right healthcare decision
They seek reassurance, especially in stressful or urgent situations
Avoiding unexpected costs and mistakes is a major emotional driver
Privacy Concerns
Users want reassurance that their personal health data is secure
They expect transparency in how their information is used
Trust is essential before relying on any digital healthcare tool
User Personas
From these insights, I developed two user personas that captured the core characteristics, goals, and pain points of my primary users people who want to make informed, confident healthcare decisions but are held back by confusing information, opaque insurance systems, and a lack of trustworthy guidance when they need it most.
Anna Johnson, 28, Administrative Coordinator from Clifton, NJ, represents the user who wants to take charge of her health but gets paralyzed by conflicting information, too many platform options, and uncertainty about what her insurance covers. She doesn't need more data — she needs a guide she can trust.
Shawn Ward, 30, Logistics Lead from NYC, represents the user who treats healthcare like a system to optimize but keeps running into walls of confusing terminology and unclear coverage. He avoids doctors unless necessary and just wants fast, transparent answers before things become urgent.
User Flow
After defining the core features and understanding user needs, I created a user flow to map how users would navigate the CareAI experience from entering their symptoms to receiving a provider recommendation and booking an appointment.
The goal of this flow was to ensure the path from confusion to clarity felt simple and trustworthy. It helped identify key decision points before moving into wireframing and refining the interface.
Lo-Fi Wireframes
After defining the user flow, I moved into creating low-fidelity wireframes to visualize the structure of the CareAI experience.
At this stage, my focus was on functionality, hierarchy, and usability not aesthetics. I developed simple wireframes to explore different layouts for key screens such as the symptom input, AI-generated provider recommendations, insurance validation, and appointment booking flow.
Moodboard
For CareAI's visual direction, I wanted every design decision to feel intentional and human. The moodboard was guided by five core principles: clarity, trust & safety, accessibility, calm confidence, and empathy. The overall vibe is clean, soft, and reassuring — a space that feels less like a clinical tool and more like a knowledgeable friend walking you through your health journey with honesty and care.
Logo
Designing the CareAI logo went through several iterations, exploring different icon combinations, wordmarks, and symbol styles before landing on the final direction. I experimented with various ways to blend the idea of health and AI together, but ultimately chose the wordmark with the heartbeat line, as it best captured the balance between medical trust and intelligent technology, simple, clean, and instantly recognizable.
Mid-Fidelity Wireframes
After testing initial ideas through low-fidelity wireframes, I moved into mid-fidelity designs to refine the structure, layout, and interactions of the CareAI experience.
At this stage, I focused on improving clarity and usability across key flows, such as entering symptoms, receiving AI-generated provider recommendations, and booking an appointment. The mid-fidelity designs allowed me to better define hierarchy, spacing, and interaction patterns while still iterating quickly before moving into high-fidelity designs.
Symptom Input
The AI greets the user and offers guided prompts to reduce decision fatigue from the start.
Quick-select prompts for common needs
Open text field for custom questions
Voice input option
AI Follow-Up Questions
The AI gathers more context to provide accurate recommendations without overwhelming the user.
Conversational tone keeps users engaged
Smart follow-up questions based on symptom
Reduces misdiagnosis risk
Insurance & Location Collection
The AI collects insurance and location information to filter relevant in-network providers nearby.
Insurance validation built into the conversation
Location permission or zip code input
Immediate provider search triggered
Provider Details
A detailed view of the selected provider giving users everything they need to feel confident before booking.
Distance, availability, and in-network status shown clearly
Real patient reviews build trust
One-tap "Book an appointment" button to move forward seamlessly
Appointment Confirmation
A clear confirmation screen that gives users full confidence their appointment was successfully booked.
Date, provider, and location summarized in one place
Option to add to calendar for easy reference
Proactive next steps like reviewing insurance details and learning more about their condition
Home / Conversation Starter
The AI greets the user and offers guided prompts to reduce decision fatigue from the start.
Quick-select prompts for common needs
Open text field for custom questions
Voice input option
AI Processing / Loading State
A subtle typing indicator reassures the user that the AI is actively working on their response.
Reduces anxiety during wait time
Familiar chat-style loading pattern
Keeps the experience feeling conversational and human
AI Follow-Up & User Response
AI asks targeted follow-up questions based on the symptom
User responds freely without filling out forms
Feels like a natural back-and-forth conversation
Provider Results
A clear list of in-network providers matched to the user's symptom, location, and insurance.
Distance and availability shown upfront
In-network badge for trust
"See details" option for more info
Appointment Scheduling
The user selects a preferred time slot directly within the conversation, keeping the booking process seamless and fast.
Multiple days and times displayed clearly
User confirms preference in natural language
No need to leave the app or visit a separate booking platform
Hi-Fidelity Wireframes
Symptom Input
The AI greets the user and offers guided prompts to reduce decision fatigue from the start.
Quick-select prompts for common needs
Open text field for custom questions
Voice input option
AI Follow-Up Questions
The AI gathers more context to provide accurate recommendations without overwhelming the user.
Conversational tone keeps users engaged
Smart follow-up questions based on symptom
Reduces misdiagnosis risk
Insurance & Location Collection
The AI collects insurance and location information to filter relevant in-network providers nearby.
Insurance validation built into the conversation
Location permission or zip code input.
Immediate provider search triggered.
Provider Details- Modal View
A detailed view of the selected provider giving users everything they need to feel confident before booking.
Distance, availability, and in-network status shown clearly
Real patient reviews build trust
One-tap "Book an appointment" button to move forward seamlessly
Appointment Confirmation
A clear confirmation screen that gives users full confidence their appointment was successfully booked.
Date, provider, and location summarized in one place
Option to add to calendar for easy reference
Proactive next steps like reviewing insurance details and learning more about their condition
Home / Conversation Starter
The AI greets the user and offers guided prompts to reduce decision fatigue from the start.
Quick-select prompts for common needs
Open text field for custom questions
Voice input option
AI Processing / Loading State
A subtle typing indicator reassures the user that the AI is actively working on their response.
Reduces anxiety during wait time
Familiar chat-style loading pattern
Keeps the experience feeling conversational and human
AI Follow-Up & User Response
AI asks targeted follow-up questions based on the symptom
User responds freely without filling out forms
Feels like a natural back-and-forth conversation
Provider Results
A clear list of in-network providers matched to the user's symptom, location, and insurance.
Distance and availability shown upfront
In-network badge for trust
"See details" option for more info
Appointment Scheduling
The user selects a preferred time slot directly within the conversation, keeping the booking process seamless and fast.
Multiple days and times displayed clearly
User confirms preference in natural language
No need to leave the app or visit a separate booking platform
Usability Testing Results
I conducted usability testing with five participants to evaluate the CareAI prototype. The goal was to observe how easily users could complete key tasks such as entering symptoms, receiving AI-generated provider recommendations, and booking an appointment while assessing the clarity and trustworthiness of the overall experience.
Key Learnings
All 5 participants rated both tasks a perfect 5/5 for ease, zero friction across the board
The symptom-based AI flow was a standout users loved not having to know what type of doctor to search for
The booking flow felt seamless and transparent, with clear time slots and confirmation
Multiple participants compared it favorably to Google, Zocdoc, and Aetna, calling it faster and less overwhelming
Iteration
While no major usability issues were identified, I used this as an opportunity to think critically about what could make the experience even better, drawing from research and user insights.
Before
After
Based on Nielsen Norman Group research on trust in AI, I identified an opportunity to introduce personalized provider recommendations based on search history, preferences, and location, making the experience feel more tailored and human.
If I had more time…
Deeper personalization — building out recommendation logic based on user history, preferences, and past appointments
Cost & insurance transparency — surfacing estimated costs and coverage breakdowns earlier in the flow to reduce financial anxiety
Accessibility improvements — ensuring the experience works seamlessly for users with different abilities and varying levels of health literacy
Conclusion
CareAI was one of the most meaningful projects I've worked on because it tackled a problem that genuinely affects people, the overwhelming and confusing experience of navigating healthcare. From research to high-fidelity screens, every decision was driven by the goal of making users feel confident, supported, and less alone when dealing with their health. Seeing five participants move through the prototype with ease and express that they would use it in real life was the biggest validation I could have asked for.