The Databricks Lakehouse Platform is an open architecture that combines the best elements of data lakes and data warehouses. In this demo, we’ll show you how to build an end-to-end healthcare data platform to ingest patient information.

We will focus on predicting and explaining patient readmission risk to improve care quality.

This demo covers the end-to-end lakehouse platform:

• Ingest healthcare data (from Synthea), and then transform them to the OMOP data model using Delta Live Tables (DLT), a declarative ETL framework for building reliable, maintainable and testable data processing pipelines
• Secure your ingested data to ensure governance and security on top of PII data
• Build patient cohorts and leverage Databricks SQL and the warehouse endpoints to visualize your population
• Build a machine learning model with Databricks AutoML to predict 30-day patient readmission risk
• Orchestrate all these steps with Databricks Workflows

To run the demo, get a free Databricks workspace and execute the following two commands in a Python notebook:

%pip install dbdemos

import dbdemos
dbdemos.install('lakehouse-hls-readmission')