Clinical solution architecture of Healthcare data solutions in Microsoft Fabric (preview)

[This article is prerelease documentation and is subject to change.]

Given the current data and analytics ecosystem, healthcare organizations face challenges in meeting the demand for real-time access to cloud-based data while ensuring secure sharing across teams. Healthcare data solutions in Microsoft Fabric (preview) address these challenges by offering a standardized approach to manage data, and ensure consistency, accuracy, and reliability. By unifying, transforming, and enriching data according to industry standards, Healthcare data solutions (preview) facilitate seamless collaboration among teams and empower advanced analytics capabilities. This functionality enables organizations to derive insights and build next-generation applications to meet business requirements. For more information, see What is Healthcare data solutions in Microsoft Fabric (preview)?

Here's a clinical architecture diagram that helps you understand how the Healthcare data solutions (preview) ecosystem integrates within the Microsoft Azure and Microsoft Fabric platforms. This diagram provides a comprehensive overview of the components comprising the various capabilities within the current clinical pipeline flow. Each component is depicted along with its orchestration and interaction with the designated lakehouse. Upon deploying the capabilities, you can observe these notebooks and lakehouses within your Fabric workspace, illustrating their collaborative functionality.

A diagram displaying the Healthcare data solutions clinical ecosystem.

The solution contains a combination of ingestion, transformation, and enrichment activities along with standards-based data models.

  • Export Fast Healthcare Interoperability Resources (FHIR) data from an Azure Health Data Services instance or land FHIR data into OneLake. You can perform this operation through various ingestion methods such as the Fabric Data Factory or the OneLake file API.
  • Ingest activities to model FHIR data as bronze delta ingestion tables. This approach allows a consistent downstream experience while facilitating data accessibility via SQL analytics endpoints.
  • Transform data using the Healthcare data foundations, Observational Medical Outcomes Partnership (OMOP), and Dynamics 365 Customer Insights - Data preparation pipelines.
  • Utilize Azure AI Language's Text Analytics for health to extract natural language processing entities and relationships from clinical notes and store the results back into the data model.

You can create Fabric Data Factory pipelines to orchestrate and schedule the activities while also adding custom activities. After the data is ingested and transformed along the various stages of the medallion architecture lakehouses, you can use Spark SQL or T-SQL for querying and analysis. You can also create Power BI reports for visualization and utilize Fabric data science capabilities such as running machine learning experiments.

See also