July 15, 2024

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Introducing healthcare data solutions in Microsoft Fabric: A game-changer for healthcare data analysis

6 min read

Learn how you can leverage the power of healthcare data solutions in Microsoft Fabric to break down data silos.

Industry-specific capabilities withinMicrosoft Fabric play a pivotal role in addressing the diverse business challenges faced by organizations across various sectors. By tailoring data solutions to specific industries, we empower our customers to achieve optimal outcomes. Customers in healthcare, retail and sustainability among others are interested in leveraging Microsoft Fabric with industry specific capabilities so they can collaborate and build next-generation applications finely tuned to vertical high-value use cases. Microsoft aims to foster a robust partner ecosystem centered on industry standards through strategic investments in vertical capabilities. 

Each industry contends with different data and regulatory frameworks. At HIMSS 2024, we are outlining some of these vertical investments. Healthcare data is complex, sensitive, and highly regulated. It requires a robust and secure platform that can handle the diverse and evolving needs of the healthcare industry. That’s why we are excited to announce the public preview of offerings that will help you analyze healthcare data with ease and confidence. Today we are announcing the public preview of healthcare data solutions in Microsoft Fabric.

Support multi-modal data management with healthcare data solutions

Healthcare data solutions in Microsoft Fabric is an end-to-end, analytics SaaS platform that enables you to ingest, store, and analyze healthcare data from various sources, such as electronic health records, picture archiving and communication systems, and more. The healthcare data solutions provide data models and transformation activities, which helps customers create a multi modal warehouse. It enables customers to align with industry standards, such as FHIR (Fast Healthcare Interoperability Resources) and DICOM, and supports compliance with regulations, such as HIPAA and GDPR. With healthcare data solutions in Microsoft Fabric, you can access and query your healthcare data using familiar experiences, such as Azure Synapse Analytics, Azure Data Factory, and Microsoft Power BI.

Public preview for ingesting clinical data available now

In October 2023, we announced the private preview for healthcare data solutions in Microsoft Fabric. Since then, customers have been testing it and giving us feedback. For example, the University of Wisconsin Madison School of Medicine and Public Health, which holds $524 million in annual total extramural research support and is home to more than 2,000 full-time faculty members, is exploring healthcare data solutions in Microsoft Fabric to power their Colorectal Cancer Multi-Modal Data Commons. Leveraging the capabilities of healthcare data solutions in Microsoft Fabric accelerates UW Madison’s path for unifying data across a variety of difference sources to create an ecosystem for secure, ethical, and reproducible data management and analytics that will further drive innovative clinical and translational research to promote health in Wisconsin and beyond.

Today we are excited to announce that the healthcare data solutions focused on ingesting clinical data is available in public preview and available in the Industry Solutions workload in Microsoft Fabric. Previews designated as production-ready employ privacy and security measures typically present in generally available online services. It can be used to process data that is subject to HIPAA regulatory compliance requirements. The public preview enables customers to create a medallion architecture aligned with the FHIR standard by providing data models and transformation pipelines. These are the current capabilities: 

  • Healthcare data foundations—Set up your healthcare data estate to run solution capabilities and configure it to structure data for analytics and AI/ML modeling. 
  • FHIR data ingestion—Bring your FHIR data to OneLake from a FHIR service such as Azure Health Data Services. 
  • Unstructured clinical notes enrichment—Use Azure AI’s Text analytics for health services to add structured to unstructured clinical notes for analytics. 
  • OMOP Analytics—Prepare data for standardized analytics through OMOP (Observational Medical Outcomes Partnership) open community standards. 
  • Data preparation for Dynamics 365 Customer Insights—Connect Dynamics 365 Customer Insights to your OneLake on Fabric for creating patient or member lists for your outreach. 

Imaging support for healthcare data solutions in Microsoft Fabric now available in private preview

Delivering on our vision for building a multi-model healthcare data estate and energized by the feedback from customers on our healthcare data solutions preview, we are extending our capabilities to support the complex and sensitive nature of imaging data.

With imaging support for healthcare data solutions in Microsoft Fabric, you will be able to ingest, store, and analyze imaging meta data across various modalities, such as X-rays, CT scans, and MRIs. The new imaging capabilities will enable collaboration, R&D and AI innovation for wide range of healthcare and life science use cases. Our customers and partners will be able to take DICOM images and bring them together with the clinical data stored in FHIR already. Making imaging pixel and metadata available alongside the clinical history and laboratory data enables clinicians and researchers to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes.

  • Unify your medical imaging and clinical data estate for analytics—Establish a regulated hub to centralize and organize all your multi-model healthcare data, creating a foundation for predictive and clinical analytics. Built natively on well-established industry data models, including DICOM, FHIR, and OMOP.​ 
  • Build fit-for-purpose analytics models​—Start constructing ML and AI models on a connected foundation of EHR (Electronic Health Record) and pixel-data. Enable researchers, data scientists, and health informaticians to perform analysis on large volumes of multi-model datasets to achieve higher accuracy in diagnosis, prognosis, and improved patient outcomes. 
  • Advance research, collaboration and sharing of de-identified imaging​—Build longitudinal views of patients’ clinical history and related imaging studies with the ability to apply complex queries to identify patient cohorts for research and collaboration. Apply text and imaging de-identification to enable in-place sharing of research datasets with role-based access control. 
  • Reduce the cost of archival storage and recovery​—Take advantage of the cost-effective, and reliable cloud-based storage to help back up your medical imaging data from the redundant storage of on-prem PACS (Picture archiving and communication system) and VNA systems (Vendor Neutral archive), and enhance your HIPAA compliance efforts. Improve your security posture with a 100% off-site cloud archival of your imaging datasets in case of unplanned data loss. 
Screenshot of DICOM data ingestion deployment page, describes the feature and describes two notebook artifacts to be deployed to the Fabric workspace

Try healthcare data solutions today

Try out the public preview for our clinical data by going to the industry solutions switcher in Fabric and select the healthcare data solutions tile and explore all the capabilities you need from there.

Screenshot of the healthcare data solutions workload in Fabric, showing a sample data and six capabilities than can be deployed in the public preview

This preview for imaging is available to a select group of customers who are interested in exploring the potential of imaging data in Microsoft Fabric. If you are interested in participating, please contact your account executive for more information. 

We look forward to hearing your feedback and seeing the amazing solutions you will create with these offerings. To learn more, visit our documentation.

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