Lakehouse Empowers CareSource to Streamline Data for Enhanced Healthcare Delivery
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Buildings
- Healthcare & Hospitals
Services
- Data Science Services
About The Customer
CareSource is a nationally recognized healthcare organization that provides member-centric health care coverage. Founded in 1989, CareSource is one of the nation’s largest Medicaid managed care plans. The company serves more than 2 million members across six states, supported by a growing workforce of 4,500. CareSource’s holistic model of care integrates insights into how to improve the health and overall well-being of its members and the populations it serves. The company's regional, community-based multi-disciplinary care management teams comb through the data and social aspects that could affect physical, mental, and psychosocial health.
The Challenge
CareSource, a nationally recognized healthcare organization, has been experiencing exponential growth over the past 30 years. This growth has led to an influx of new members, which the company's legacy data systems were unable to handle efficiently. The company had to resort to temporary solutions such as running jobs designed for monthly execution on a daily basis, which the systems were not designed to handle. This resulted in the expenditure of significant resources to maintain the systems. The company needed a modern data platform that could scale, perform efficiently, and be future-proof. The platform needed to be cloud-based and capable of serving as a single source of truth for all incoming data.
The Solution
CareSource deployed the Databricks Lakehouse Platform on Azure to fast-track its data analytics journey. This platform helped remove data silos and create a single source of truth for all incoming data. The company implemented a three-layer architecture with the help of Databricks experts. The first layer served as a way to ingest large amounts of raw data. Transformation logic and processing logic were written in Databricks notebooks to feed into an integrated layer that leverages an industry-standard data model: the IBM Universal Health Care Data Model. The second level of transformation occurred in the final layer to get the data into an easily consumable structure primed for downstream analytics use cases. Analysts at CareSource also used Databricks SQL to analyze the data in a more intuitive and visual way. Some analysts even used SQL directly within the Databricks SQL interface and planned to rewrite existing predictive models in a more efficient way within the Databricks environment.
Operational Impact
Quantitative Benefit
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