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Healthcare System Modernization for Enhanced Patient Data Management and Workflow Automation
  1. case
  2. Healthcare System Modernization for Enhanced Patient Data Management and Workflow Automation

Healthcare System Modernization for Enhanced Patient Data Management and Workflow Automation

ventionteams.com
Medical
Business services

Identifying Challenges in Healthcare Data Management and Operational Efficiency

A healthcare advocacy organization faces difficulties with outdated CRM systems that hinder strategic decision-making, inconsistent patient data across legacy systems, manual workflows that reduce operational efficiency, and challenges in processing large volumes of patient data rapidly, especially during increased demand periods such as a pandemic. These issues impact the organization’s ability to deliver timely, accurate support and to scale operations effectively.

About the Client

A mid-sized healthcare advocacy organization providing clinical guidance, second opinions, and data-driven decision support to patients and caregivers, aiming to streamline operations and improve patient outcomes.

Goals for Improving Healthcare Data Processing and Workflow Automation

  • Migrate and unify patient and customer data into a flexible, scalable data architecture to improve data consistency and accessibility.
  • Automate customer relationship management workflows across multiple cloud-based CRM platforms to enhance operational efficiency.
  • Implement real-time KPI tracking and reporting mechanisms to support strategic decision-making.
  • Develop a mobile application and web portal for onsite engagements and patient interactions with seamless data synchronization.
  • Ensure the system can process hundreds of files and millions of records daily to support growth and increased data throughput.
  • Enhance data security, scalability, and performance to reliably serve healthcare support operations during high-demand periods.

Core Functional System Capabilities for Healthcare Data and Workflow Optimization

  • Data migration and merging from legacy systems into a flexible, cloud-compatible data lake architecture.
  • Automated workflows across multiple CRM clouds to streamline patient engagement and support processes.
  • A reporting module for tracking key performance indicators (KPIs) and generating analytics dashboards.
  • Development of a mobile application for onsite events and patient interactions with synchronized data updates.
  • Web portal for patients and caregivers providing access to health data, support resources, and appointment management.
  • Integration capabilities with external systems such as data storage, telehealth platforms, and communication channels.

Technology Stack and Architectural Preferences for Healthcare Data Project

Cloud platforms such as Azure for data storage, processing, and hosting
Data integration and pipeline tools like Azure Data Factory
Frontend frameworks including Vue.js for web applications
Backend development with .NET/.NET Core
Databases such as MongoDB and MS SQL for structured and unstructured data storage
Business intelligence tools for analytics, e.g., Tableau Analytics integrated with Salesforce Einstein Analytics

Essential System Integrations for Seamless Healthcare Operations

  • Healthcare data management systems and EHR/EMR platforms
  • Communication channels like telehealth support (e.g., conferencing, messaging)
  • CRM platforms for customer engagement automation
  • Mobile app synchronization services

Non-Functional System Attributes for Healthcare Data Platform

  • Ability to process hundreds of files and millions of records daily with high throughput
  • System scalability to support organizational growth and increased data volume
  • Robust security protocols to protect sensitive health information
  • High system availability and reliability during peak operational hours
  • Responsive performance to support real-time analytics and user interactions

Projected Business Benefits and Outcomes of System Modernization

The modernization initiative is expected to significantly improve data accuracy and consistency, automate workflows to reduce manual effort, and enhance decision-making processes through real-time KPI tracking. The system’s capacity to handle large volumes of data will enable the organization to scale operations efficiently during periods of high demand, such as pandemics. Overall, the project aims to enable the organization to deliver faster, more reliable healthcare support and support growth in patient engagement and data-driven services.

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