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CASE STUDY

Building a Cloud-native Data Analytics Platform on AWS

 
 

Mactores empowered integrated healthcare services with scalable, real-time data insights for Modivcare.

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About_Customer_Modivcare

About 
The Customer

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Modivcare is a leading US-based healthcare services provider focused on addressing social determinants of health (SDoH). With a portfolio that includes non-emergency medical transportation (NEMT), personal care services, remote monitoring, and nutritional support, Modivcare serves both public and private payors. Their solutions improve care accessibility, reduce system inefficiencies, and deliver better health outcomes for vulnerable populations.

Customer_Situation_Modivcare

 

Customer Situation

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  • Fragmented Data Landscape: Business-critical data was siloed across legacy systems for each service line; transportation, care, and monitoring, resulting in operational inefficiencies.
  • Delayed Insights: Generating consolidated reports required significant manual effort, with data ingestion, transformation, and presentation cycles taking days.
  • Compliance Pressures: Regulatory frameworks mandated transparent, auditable data operations with detailed reporting, which were difficult to sustain with their legacy infrastructure.
  • Scaling Bottlenecks: Their existing on-premise and hybrid environments lacked the agility to support data growth or new analytical use cases like predictive modeling and near real-time decision support.

 

Our Approach

Modivcare partnered with AWS and Mactores to design and implement a Cloud-native Data Analytics Platform tailored to healthcare needs. The multi-phase approach included:

  • Data Strategy Workshop: Conducted stakeholder interviews to align business goals with data architecture, define KPIs, and prioritize use cases across NEMT and SDoH programs.
  • Data Ingestion & Processing:
    • Legacy databases and flat files were ingested using AWS Glue Jobs, orchestrated with AWS Step Functions.
    • Amazon S3 served as the central data lake, hosting both raw and curated datasets.
  • Transformation Layer:
    • Built a semantic data model using Amazon Redshift and Redshift Spectrum for SQL-based exploration of structured and semi-structured data.
    • Implemented CDC (Change Data Capture) from source systems for near real-time updates.
  • Analytics & Visualization:
    • Created self-service dashboards in Amazon QuickSight, enabling business users to monitor care coordination KPIs, patient adherence, and NEMT dispatch efficiency.
  • Security & Compliance:
    • Fine-grained access control via AWS Lake Formation and IAM.
    • Data lineage and audit logs integrated for HIPAA compliance.

 

Business Outcomes

  • Data Democratization: Enabled 300+ business users across care operations, logistics, and finance to access dashboards without IT dependency.
  • Faster Time-to-Insight: Reduced reporting timelines from 3 days to under 30 minutes with automated data pipelines.
  • Cost Optimization: Migrating ETL and storage to serverless and object-based platforms resulted in a 40% reduction in infrastructure costs.
  • Improved Patient Outcomes: Real-time alerts and care coordination analytics empowered field teams to act faster and more accurately.

Technical Outcomes

  • Scalable Storage: Amazon S3 lake scaled from 2 TB to 25+ TB without performance degradation.
  • Elastic Compute: Amazon Redshift’s RA3 nodes optimized performance and cost via intelligent workload management.
  • Orchestration: Data workflows ran 5x faster post-optimization with dependency-driven triggers using AWS Step Functions.
  • Monitoring: Integrated CloudWatch dashboards provided real-time pipeline health and SLA tracking.
Getting_Started_Modivcare

Getting 
Started

Healthcare organizations like Modivcare looking to modernize their analytics infrastructure can begin by:

  • Identifying Critical Use Cases: Focus on where data impacts access, cost, and outcomes most directly.
  • Engaging a Cloud Partner: Leverage AWS and experienced partners like Mactores for domain-specific platform design.
  • Piloting High-Impact Dashboards: Use agile sprints to build, test, and iterate quickly on core dashboards.
  • Planning for Scale: Establish a roadmap for incorporating AI/ML, real-time decisioning, and broader data integration.

 

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