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User-Centered Redesign of Medical AI Data Platform to Enhance User Retention and Satisfaction

darly solutions

Medical

Healthcare

Insurance

Pharmaceuticals

Current Challenges with Platform Usability and User Engagement

The existing platform suffers from poor user satisfaction due to complex interface design, including overloaded Summary Display Module layouts, inadequate search functionality, clunky scrolling, missing UI controls (e.g., Reset buttons), and unclear form validation indicators. These issues have resulted in subpar KPIs for user satisfaction, retention, and feature adoption despite strong core NLP capabilities.

About the Client

AI solution provider specializing in healthcare data analytics and NLP-powered platforms for medical professionals and researchers

Key Objectives for Platform Redesign

Increase user satisfaction rates to ≥90%
Improve user retention by ≥15%
Enhance platform performance by ≥10%
Boost new feature adoption rates by ≥10%
Achieve rapid deployment of redesigned platform

Core System Requirements for Enhanced User Experience

Redesigned Summary Display Module with collapsible sections and pill elements
Integrated JSON viewer component for data-intensive workflows
Enhanced Table Display Module with pagination, custom display options, and expandable rows
Action links in Explore Actions section
Clear status indicators and error handling mechanisms
Improved patient navigation and document display features

Technology Stack Preferences

React.js

Node.js

RESTful APIs

JSON

SQL

Critical System Integrations

NLP engine APIs
Third-party authentication systems
Healthcare data source connectors

Performance and Quality Requirements

≥50% reduction in ignored application exceptions
≥10% improvement in API call efficiency
Scalable architecture for healthcare data volumes
HIPAA-compliant security measures

Expected Business Impact of the Redesign

The redesign is projected to increase user retention by 23% within six months, achieve 94% user satisfaction rates, reduce workflow friction by 18%, and improve user productivity by 16% through enhanced data presentation and streamlined navigation. Error handling improvements will significantly reduce ignored exceptions while maintaining core NLP functionality performance.