success story

Virtusa partners with HealthFirst to enhance operational efficiencies

Our client, HealthFirst, is one of the nation’s largest not-for-profit health insurer, earning the trust of more than 1.6 million members by ensuring access to affordable and high-quality healthcare. Sponsored by downstate New York’s leading hospital systems, HealthFirst’s unique advantage is rooted in its mission to put members first by partnering closely with its broad network of providers on shared goals. It offers market-leading products to fit every life stage, including Medicaid plans, Medicare Advantage plans, Long-Term Care plans, Qualified Health plans, and individual and small group plans. To enhance the productivity of its Enrollment and Billing (E&B) division, the client decided to opt for an automated Amazon Redshift data mart. Virtusa leveraged Healthfirst’s Operational Data Store (ODS) platform, built on Amazon S3, and Amazon Aurora PostgreSQL to ingest data into an Amazon Redshift data mart using PySpark ETL.

The Challenge

The on-prem data mart, in use by HealthFirst’s E&B division, was crumbling under the pressure of increasing data volume and size. Data discrepancies had become the norm. The New York-based health insurer was quick to respond to inconsistent data with premium denials. To make matters worse, the absence of analytics translated into inaccurate reporting of rejected claims and delayed implementation of control measures.

The need for manual intervention in the data load process was one of the key challenges posed by the data mart. It slowed down the loading process, often bringing critical business processes such as invoicing, delinquency, disenrollment, and binder invoicing to a complete standstill. Not only was the manual process error-prone, but loading failures were also quite common. The client needed an automated data mart that enhanced efficiency, reduced the rate of discrepancies, and supported a more integrated data model.

The Solution

Healthfirst worked with Virtusa to bring in the technical skills and infrastructure necessary for building an Amazon Redshift data mart.

Our experience, coupled with our deep digital engineering capabilities, made us a vendor of choice as the client set out to automate data loading and eliminate operational bottlenecks.  

With the help of Healthfirst’s ODS platform (built on Amazon S3) and Amazon Aurora PostgreSQL, we ingested data into an Amazon Redshift data mart using PySpark ETL. Some of the key aspects of the solution delivered are:

  • Rearchitected the data integration layer with a parameterized, configurable open-source data transformation framework that manages end-to-end functionality
  • Developed the framework using Sqoop, PySpark, AWS Glue, AWS Glue catalog, and Amazon S3
  • Automated error detection process leading to faster root cause identification
  • Ongoing IT support for incident management with resolution SLAs
  • Content-Defined Chunking (CDC) identification based on Sha2-256 hashing technique and ability to handle type1, type2, and hybrid Slowly Changing Dimension (SCD) types.
  • Audit component built with attributes captured to the maximum details
  • Restoration from any failure point
  • Logging enabled by default for every process

AWS Services Used:

  • Amazon Redshift
  • Amazon Aurora
  • Amazon S3
  • Amazon EMR
  • AWS Glue
Virtusa partners with Healthfirst to enhance operational efficiencies
The Benefit
  • Accelerated E&B data mart delivery to help track enrollments and premium collections across four LOBs (Medicaid, Essential Plan, Quality Health Plan, and Child Health Plan)

  • Processed full load for 12 dimensions and 7 facts with the peak processing record count of 16 million records

  • Demonstrated ability to load data of approx. 90 GB in approx. 30 min in the production environment

  • Reduced effort in building new data integration jobs by 50% with a configurable framework

  • Paved the way for future cloud-native data marts to be delivered faster with the new framework model

  • Enabled designing of dashboards for identifying the root cause of enrollment and billing failure by exposing 50+ data sources directly in Amazon Redshift as persisted and external tables
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