Putting patients' health first with data and AI

Healthdirect Australia, which runs the National Health Services Directory, had data quality and governance problems across isolated silos and couldn't process over 1 billion monthly data points (bookings, appointments, pricing, eHealth transactions) within its 24-hour batch window; Chief Architect Peter James said they were 'starting to get batch overruns.' Healthdirect deployed Databricks and Delta Lake with a Landing/Raw/Staging/Gold zone architecture and Structured Streaming to replace batch ETL. Processing improved 6x (30,000 automated updates/month vs. the prior 6 months' worth), loading reached 1 million records/minute (20-million-record datasets in 20 minutes, down from 24+ hours), and a fuzzy name-matching algorithm's accuracy rose from under 80% (with manual verification) to 95% with no manual intervention, alongside improved HIPAA compliance and security monitoring.

Databricks is a unified data-and-AI lakehouse platform, used across pharma R&D for everything from early prototyping to production model governance via Unity Catalog.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.