Healthcare Data Access Expands on Cloud Platforms

- PurpleLab's HealthNexus platform provides access to one of the largest claims databases in the U.S., containing over 50 billion medical and pharmaceutical claims from more than 330 million patient lives. This data is enriched with Social Determinants of Health (SDOH) scores, Risk Adjustment Factors (RAFs), and Hierarchical Condition Categories (HCC). - The company has raised a total of $43 million in funding across three rounds, with a significant $40 million Series B round completed in September 2022, led by Primus Capital. This funding is aimed at accelerating investments in new data assets and product development. - For health insurance clients, real-world data is crucial for predictive modeling to set premiums, assess risk, and prevent fraud. Actuarial and risk adjustment models use factors like medical history and lifestyle to estimate future healthcare costs. - Both Databricks and Snowflake are HIPAA-compliant and provide robust security for sensitive patient data. Databricks is often favored for its advanced AI and machine learning capabilities with unstructured data, while Snowflake excels at high-performance SQL analytics on structured data. This often leads to organizations using both platforms, with Databricks for processing and model training and Snowflake for reporting and business intelligence. - PurpleLab was recently certified as a Qualified Entity by the Centers for Medicare and Medicaid Services (CMS), allowing it to receive and analyze Medicare data to measure provider performance. This enables the combination of commercial, Medicaid, and Medicare data to understand provider practices across different insurance products. - The company has been expanding its ecosystem through partnerships with companies like Datavant for data connectivity, Experian for privacy-conscious targeting, and VDX.tv for campaign measurement. Additionally, PurpleLab acquired KAID Health to integrate AI-powered analytics and gain deeper insights into the patient journey. - Real-world data is increasingly being used to support regulatory decision-making, with both the U.S. Food & Drug Administration (FDA) and the European Medicines Agency (EMA) issuing guidelines on its use for benefit-risk assessments of drugs and medical devices. - Predictive analytics in healthcare, powered by real-world data, is being used for early disease detection, personalizing treatment plans, and reducing hospital readmissions by identifying high-risk patients. AI-powered models have been shown to improve diagnostic accuracy by up to 70%.