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Development of Adaptive Web Interfaces for Longitudinal Health Impact Study
  1. case
  2. Development of Adaptive Web Interfaces for Longitudinal Health Impact Study

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Development of Adaptive Web Interfaces for Longitudinal Health Impact Study

osedea.com
Health & Fitness
Medical
Research & Development

Legacy System Limitations and Manual Data Challenges

Existing web interface lacked source code ownership, couldn't manage complex family cluster data relationships, required extensive manual Excel data entry, and needed urgent modernization to support dynamic questionnaire logic and automated data processing for evolving pandemic research requirements.

About the Client

Collaborative epidemiological research project between academic institutions and public health authorities, focused on tracking viral infections and community wellbeing.

Modernization Goals for Public Health Research

  • Replace legacy system with fully-owned, scalable web solution
  • Implement dynamic questionnaire engine with conditional logic
  • Enable automated data collection and analysis workflows
  • Support complex household relationship modeling
  • Ensure adaptability for future pandemic/vaccination tracking

Core System Capabilities

  • Role-based interfaces for parents/educators/administrators
  • Smart questionnaire builder with branching logic
  • Family cluster relationship management
  • Automated email notifications and reminders
  • Data export and visualization dashboard
  • Mobile-responsive design for cross-device access

Technology Stack

React.js
Node.js
PostgreSQL

System Interoperability

  • Email service API (transactional emails)
  • Data analytics API for researchers
  • Legacy data migration pipeline

Operational Requirements

  • High availability during peak data collection periods
  • GDPR-compliant data security measures
  • Rapid iteration capability for questionnaire updates
  • Performance under concurrent user load
  • Cross-browser compatibility

Research Enablement Outcomes

Enables efficient longitudinal tracking of viral spread patterns and vaccination impacts through automated data collection, reduces manual processing time by 70%, supports evidence-based public health policy decisions, and provides flexible infrastructure for future infectious disease monitoring initiatives.

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