Case Study

Redesigning Data Platform Architecture for Real-time Healthcare Analytics

A renal care provider revolutionizes patient outcomes through data platform modernization, leveraging real-time analytics for better decision-making.

Redesigning Data Platform Architecture for Healthcare Analytics

A provider of in-home renal care services redesigns its data platform architecture for real-time analytics and enhanced performance, improving patient experience and achieving cost optimization.

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Industry & Region: Healthcare Technology, US

Tech Stack: 

Cloud Computing Platform: Google Cloud Platform; Open–source DBMS: PostgreSQL; Web API:
.NET Core; Frontend JavaScript Framework: Angular with NgRx; DevOps: GitLab; JavaScript
Library: SurveyJS; Infrastructure as Code (IaC): Terraform

Client Overview

Our client is a leading healthcare provider specializing in renal care. They offer unique in-home care services for patients with polychronic conditions, with their expertise in Chronic Kidney Disease (CKD) and End-stage Renal Disease (ESRD).

Business Challenge

The client’s existing data platform, hosted on the Azure cloud, faced limitations that hindered its ability to effectively support the organization’s growing data processing and analytics needs. The primary challenges included:

  • Performance and Availability Issues: The existing data platform experienced performance bottlenecks and occasional downtime, impacting the data processing speed and hindering timely access to critical insights.
  • Sequential Processing: Based on traditional SQL-based workflows, the platform relied on sequential data processing, leading to slowdowns and inefficiencies.
  • Data Silos: Data resided in disparate sources across the organization, creating isolated data pockets that limited comprehensive analytics and hindered the ability to identify valuable trends across datasets.
  • Manual Data Source Integration: Adding new data sources was a time-consuming process that required significant technical effort, limiting the flexibility and scalability of the platform.

These limitations hampered the client’s ability to leverage the full potential of data for optimizing patient care, streamlining operations, and making data-driven decisions.

Solution Offered

To address these challenges, the client partnered with us to embark on a data platform architecture modernization initiative. The solution encompassed the following phases:

  • Platform Rearchitecting: The existing data platform was rearchitected to leverage the power of parallel processing. This involved implementing Apache Spark, a distributed processing framework, enabling simultaneous data processing tasks across multiple machines, significantly improving processing speed and overall platform performance. This implementation would reduce the high maintenance costs and frequent downtimes of their older platform.
  • Unified Data Model: A central and standardized data model, also known as a canonical data model, was established. This model served as a blueprint for integrating data from disparate sources, ensuring consistency for seamless data processing.
  • Automated Data Ingestion: Robust data pipelines were built to automate the ingestion and processing of data from various sources. This streamlined data flow, eliminated manual intervention, and ensured timely access to the latest information for analysis.
  • Medallion Data Architecture: A data pipeline was implemented in Azure Synapse, leveraging a medallion architecture. Raw data is stored in the Bronze layer, transformed data resides in the Silver layer, and the curated, business-ready data is available in the Gold layer.
  • Enhanced Analytics Capabilities: By overcoming data silos and optimizing performance, data platform modernization paved the way for near real-time analytics. This enabled the company to gain valuable insights into patient care trends and operational performance and identify areas for improvement. Also, the solution was made scalable to accommodate the integration of other AI capabilities in the future with the growing business needs.
Value Delivered
  • Enhanced data platform performance 
  • Enabled real-time analytics 
  • Optimized data management costs 
  • Increased compatibility with other AI services 
  • Improved overall care delivery and patient experience

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