Microsoft has introduced new healthcare-specific tools in Fabric and Azure AI to help healthcare organizations unlock insights and improve patient and clinician experiences. The new products can combine data from sources such as electronic health records, images, lab systems, medical devices, and claims systems so organizations can standardize it and access it in the same place.
The new tools will help eliminate the “time-consuming” process of searching through these sources one by one. Microsoft has been trialing Fabric for healthcare with select customers including Northwestern Medicine, Arthur Health, and SingHealth, and it is available in a preview capacity starting Tuesday.
Microsoft Fabric is an end-to-end, unified analytics platform that brings together all the data and analytics tools that organizations need to unlock the potential of their data and lay the foundation for the era of AI. The healthcare data solutions in Fabric eliminate the costly, time-consuming process of stitching together a complex set of disconnected, multimodal health data sources – text, images, video, etc. – and provide a secure and governed way for organizations to access, analyze, and visualize data-driven insights across their organization. The new tools in Fabric and Azure AI will help improve patient experiences and allow clinicians to make informed decisions.
The new Azure AI health tools include the Azure AI Health Insights clinical decision support tool, which can extract insights from unstructured data via models like a patient timeline generator and report simplifier to help clinicians use generative AI to convert medical jargon into simpler language. The Dragon Ambient eXperience Copilot medical transcription service from Nuance is also available.
Microsoft’s new offerings are built on a foundation of trust-driven Microsoft’s Responsible AI principles. Healthcare organizations implementing these technologies will enable connected experiences at all points of care, foster collaboration, empower their workforce, and extract value from clinical and operational data using industry-relevant data standards.