
DR. Match
A tool for finding healthcare providers who meet your needs and preferences - UX/UI Design
Timeframe
January – April, 2019
My Role
- Team Lead (Team of 4)
- UX/UI Designer
Platform
Mobile
Methods
- Competitive Analysis
- User Interview
- Affinity Mapping
- Persona
- User Flow
- Lo-Fi and Hi-Fi Prototype (Figma)

Finding a new doctor is like dating. There are an overwhelming number of options, but the process of choosing a doctor can still be intimidating, challenging, and stressful.
OVERVIEW
Problem
People are now more than ever empowered and equipped to browse and choose their healthcare providers. Doctor information is abundant, yet scattered across different sources, and in many cases written using jargon. In addition, doctor information providers often curate information without considering what patients are looking for, making the processing of selecting a doctor more challenging.
Solution
As part of a Rochester Institute of Technology interaction design project, my team and I designed DR. Match, a mobile app that supports the process of selecting the right healthcare providers by:
- Allowing patients to customize search results in great detail.
- Matching doctor search results to patients’ specific needs and preferences.
- Comparing doctors and booking appointments that work with patients’ schedules.
My Roles
I led a team of 4 graduate students in the design of DR. Match and contributed to every step of the project. Specifically, I organized and ran group work sessions, performed a competitive analysis, conducted 3 out of 9 user interviews, analyzed research data, and created parts of the personas, user flow, and prototypes.
PROCESS
I. Research
Competitive Analysis
- RRH MyCare – a health management tool for Rochester Regional Health patients to find a doctor, communicate with providers, and access medical information.
- Aetna Health – an app for Aetna® members to find a doctor and see what procedures may cost.
- Zocdoc – a tool to compare local medical professionals and book appointments.
User Interview
We conducted one-on-one interviews with 9 target users who had previous experience searching for healthcare providers. We aimed to understand:
- How they looked for a new doctor.
- What factors they considered when looking for a new doctor.
- What their expectations of the doctor were during their first visit and how they decided if they would come back.
- What challenges they faced while finding a new doctor.
Data Analysis
We organized the interview data into an affinity map to uncover themes and identify user needs.
Key Insights
- Patients generally do their own research and/or consider opinions of others to gather as much information about healthcare providers as possible.
- Patients care the most about a doctor’s medical skills (credentials, specialty, approach to medicine, and techniques) and soft skills (personality, attitude, and communication).
- Other factors that influence patients’ decision in choosing a doctor include: availability of the doctor, location, facilities, and staff of the doctor’s office, time (travel time and wait time), and insurance coverage.
- Patients want to know more about a doctor’s education and training background, community involvement, and reputation at work to gain a complete picture of the doctor.
I wish I can sit and see what it’s like to sit and talk to this doctor.
P4

I wish someone would just tell me which doctor is perfect for me.
P8

Personas
Based on our research findings, we created 3 user personas to gain empathy towards our end users and help shape our design strategy.
II. Ideation
Strategy

We need to help users make informed decisions in healthcare provider selection by providing information that users care about.
In addition, we should streamline the patient appointment scheduling process to increase patient experience.
Key Features
With the design strategy above in mind, we decided to prioritize our solution to the following key features:
- A basic and/or customized questionnaire to initiate a search.
- Including user preferences of characteristics of a doctor in the matching process.
- Bio videos for patients to know more about a doctor.
- Preview of a doctor’s office.
- Comparison of doctors’ availabilities.
- In-app appointment scheduling.
User Flow
To keep our design on track, we created a user flow depicting the path a user (new or returning) takes when using our design to find a new doctor. The user flow maps out the user’s movement – from entry point to successfully scheduling an appointment.
III. Lo-Fi Prototyping
Role Playing
We then conducted a role play to refine the flow and interactions in the app.
My teammate Trinh played the role of a user using our app to find a new doctor for the first time. She continuously thought out loud as she moved through the interface sketch.
Meanwhile, I acted as the app to manipulate the prototype and provide feedback based on Trinh’s interaction.

IV. Hi-Fi Prototyping
Visual Design
We created a mood board to guide the style and aesthetic of our design. We defined the style of our design as caring, trustworthy, and clean.

We then discussed and built a style guide to align the team around our design direction and to keep components consistent and uniform across the app.
Our style guide was inspired by the mood board above and referred to Google Material Design guidelines.

Hi-Fi Prototype
We created an interactive hi-fidelity prototype using Figma. This prototype incorporated all the key features in our design.
OUTCOME
We released the final design at the course showcase in April 2019. DR. Match supports the process of choosing a doctor in the following ways:
Customizable Onboarding Questionnaire
Once sign up an account, users will need to fill out a basic onboarding questionnaire to capture their information and needs such as demographic information, insurer, and health concerns.
In addition, users have the option to answer more questions regarding their preferences to improve match results.
TAKEAWAYS
From this project I learned:
- Selecting appropriate tools may significantly improve data analysis efficiency. For example, it is more practical and effective to analyze qualitative data using digital tools such as Miro when the data size is large.
- Stepping up to lead when others are reluctant to take charge is necessary for the team to stay on track and reach goals.
- Keeping design components organized and properly naming them make future design revisions easy and convenient.