Medical Diagnosis App

Web application allows licensed medical personnel to remotely diagnose patients.

Project Stats

  • Type Client
  • Platform Web
  • Status Published

Project Roles

  • UI Designer

Project Goal

Create a web app for qualified medical personnel to diagnose a telemedicine patient in 20 minutes or less. This covers the web application used by the healthcare professionals, not the mobile app used by the patients.

Target Audience

The user for this web application is an educated, trained, and licensed nurse practitioner. This is the highest rank of nurse and they are legally sanctioned for diagnosing patients and writing limited prescriptions.

Persona card for Nancy who represents the nurse practitioners who use the web app process and diagnose patients. Persona card for William who represents users of the mobile app to be treated and older users.

Research & Key Challenges

Research information and inputs were provided by SMEs such as representative nurse practitioners and the CEO, in direct, face-to-face communication.

This web application needs to fit and work on small laptops to ensure maximum hardware compatibility. The video is fixed in place so the healthcare professional can always have the patient in view while scrolling the chart. Controls are designed to be large enough for easy, non-precise clicking. The color choices are limited to reserve high-intensity colors for important status, system, or patient-related info to gain prominence.

Initial Concepts & Wireframes

The wireframes were collaboratively designed with the CIO and CEO, using their backgrounds in medicine and informatics, to guide the placement of “widget regions”. As the digital wireframes were simple rectangles denoting widget regions, we iteratively arranged the regions until the executive team was satisfied.

Original digital wireframe of widget areas for the financials screen. Original digital wireframe of the widget areas for the messages screen.

Usability Testing

Usability testing data was recorded in a spreadsheet for each of the participants. The responses were transformed into affinity groups to find themes.

Incentivizing feature requests from medically-trained subject matter experts. Constraint feature requests from medically-trained and administrative subject matter experts.


Relief Telemed hoped the users, nurse practitioners, would be able to interact with one patient every twenty minutes. The actual rate turned out to be approximately one patient every 13 minutes.