Healthcare Technology Company

UI/UX design for healthcare platform to analyze and manage the workload of radiologists

ElifTech was approached by a healthcare technology company that provides a machine learning-powered platform to analyze and manage the workload of radiologists. The project focused on enhancing the user experience (UX) and user interface (UI) of the demo version. It involved a comprehensive redesign encompassing UI/UX enhancements, developing a clickable prototype using Figma, creating a UI Kit for consistent design elements, designing new features aligned with user needs, and an overall platform redesign.

The aim was to improve the overall user experience, visual appeal, and functionality of the demo, ensuring it effectively communicates the value proposition to prospective users and stakeholders. The goal was to highlight the platform's efficiency in managing radiologist workloads effectively while presenting a clear and compelling demonstration to potential clients.

  • Industry


  • Headquarters


Web app

Services we provided

  • UI/UX Design
  • User Interface Redesign
  • Creating User Flows and Product UX
  • UI Prototyping
Web app

About the client

The client is a company specializing in cutting-edge machine learning applications tailored specifically for the healthcare sector. Their program focuses on addressing a critical need within the healthcare landscape – the management and analysis of radiologists' workloads.

Understanding the pivotal role of radiologists in diagnostic procedures and the limited resources available, the client’s company is dedicated to leveraging innovative technology to alleviate the workload pressure on these healthcare professionals. Their machine learning program serves as a strategic solution to analyze, streamline, and enhance the efficiency of radiologists' tasks, ultimately contributing to improved healthcare delivery.


The primary challenge faced by a client was the underperformance of their demo version when presented to potential clients. The existing demo lacked an appealing design and a lack of clear user flow. Another hurdle was the mixed features within the demo that were not harmoniously integrated. The absence of a clear and intuitive user flow impeded potential clients from comprehending the program's functionalities and benefits. The unclear navigation hindered users' ability to explore the capabilities of the machine-learning solution efficiently.

Web app


  • UI/UX Design
  • User Interface Enhancement
  • User Experience Optimization
  • Design System Development

The initial phase focused on conducting in-depth research into the existing product and engaging in extensive communication with the client. This involved an exhaustive analysis of the platform's functionalities, user journey, and existing design elements. Direct discussions with the client were pivotal in understanding their objectives, vision, and specific requirements for the revamped demo. The core focus was on enhancing the user interface and experience. We streamlined the user journey by eliminating redundant steps and crafting a more intuitive flow. Simplifying the process into three distinct steps allowed users to estimate their time commitment easily. A robust design system was developed, encompassing variable components, a cohesive color system, and a structured font hierarchy.

Features delivered

  • UX/UI Design
  • Clickable Prototype
  • UI Kit
  • New Features Design
  • Platform Redesign

A visually appealing and user-friendly interface that significantly improved user engagement.

Key results and business value

  • Enhanced User Engagement
  • Improved Client Perception
  • Increased Conversion Rates
  • Strengthened Market Position
  • Long-Term Growth Potential

The revamped demo version garnered notable improvements in user engagement. The updated user interface (UI) and streamlined user experience (UX) resulted in increased interaction and positive feedback from potential clients. This elevated engagement indicated a higher level of interest and receptiveness to the machine learning program's capabilities.

Technology we used


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