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Implementation of Cloud-Based Data Management and Process Automation for Multi-Clinic Healthcare Network
  1. case
  2. Implementation of Cloud-Based Data Management and Process Automation for Multi-Clinic Healthcare Network

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Implementation of Cloud-Based Data Management and Process Automation for Multi-Clinic Healthcare Network

trigent.com
Medical
Healthcare Cloud Services
Data Engineering

Challenges in Managing Fragmented Data and Manual Reporting Processes

The organization faces significant operational inefficiencies due to disparate data systems across acquired clinics, including manual data collation, fragmented information silos, data duplication errors, and delayed monthly reporting cycles. These challenges hinder real-time decision-making, resource optimization, and patient care quality across their distributed network.

About the Client

National operator of 100+ dermatology clinics and business support provider for 300+ dermatology practices specializing in medical, surgical, and cosmetic skin treatments

Key Objectives for Operational Transformation

  • Automate cross-clinic data capture and processing from heterogeneous systems
  • Transition from monthly to real-time operational and clinical insights
  • Eliminate data duplication errors through standardized normalization processes
  • Establish scalable cloud infrastructure for future clinic expansions
  • Enhance patient satisfaction through data-driven care personalization

Core System Functionalities and Features

  • Automated data ingestion from diverse clinic systems (cloud/on-premise)
  • Real-time operational dashboards with Power BI visualizations
  • Data normalization engine using conditional logic (IfThenElse) processing
  • Secure data transmission via VPN/RDP connections
  • Ad-hoc reporting with dynamic filtering capabilities

Target Technology Stack

Microsoft Power Automate
Azure Virtual Machines
SQL Server
Power BI
Azure Cloud Services

System Integration Requirements

  • Legacy clinic management systems
  • Cloud-based EHR platforms
  • On-premise databases
  • Azure analytics services
  • Power BI visualization tools

Critical Non-Functional Requirements

  • Horizontal scalability for 200+ clinics
  • Real-time data processing latency <2 seconds
  • HIPAA-compliant data encryption
  • 99.95% system uptime SLA
  • Automated failover mechanisms

Expected Business Impact of Data Management Modernization

The implementation is projected to reduce manual data processing by 200 hours/month, accelerate reporting cycles from monthly to daily, and improve clinical decision-making through real-time metrics on patient flow, treatment outcomes, and resource utilization. The solution will establish a foundation for predictive analytics while maintaining compliance with healthcare regulations, with potential for 30%+ gains in operational efficiency and patient satisfaction scores.

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