Clinical and operational analytics in the data cloud

Healthcare organizations move analytics to the cloud for flexibility, agility, and scalability. Microsoft customers leverage Azure Data Factory or Azure Synapse Analytics Data Flows to batch ingest on-premises data to the cloud and persist it with Azure Synapse Analytics, or in a raw Azure Data Lake.

Managing data isn't a new challenge, but it's increasingly more difficult. Since data is in different schemas, metadata, and relationship, analyzing it requires tremendous standardization efforts. The key to success lies in building systems of intelligence based on rich and standards-based data models. The Healthcare database templates provide you with information blueprints for Healthcare Services (Provider), Healthcare Insurance (Payor), R&D and Clinical Trials, Genomics, Pharmaceuticals, and Retail (Pharmacy) to enable data consortia. Lastly, healthcare organizations interested in building methods to “learn” from data are adopting machine learning operations to deploy and maintain models in production reliably and efficiently.

To improve health data insights, customers adopt Azure services to gather, store, process, and visualize data. This data can be used upstream for machine learning and to provide artificial intelligence capabilities.

A diagram showing sample healthcare data visualization flow.

Download a printable PDF of this reference architecture diagram.

Clinical and operational insights

After the Data Lake ingests the data in a standard shape, you can use services such as Azure Machine Learning to build and train AI models for data enrichment. The AI generated from Azure Machine Learning can be leveraged within services such as Dynamics 365 Customer Insights or Power BI to generate additional insights over the data.