Microsoft Fabric: With AI infused into every layer in Microsoft Fabric , they are committing to a future where every data professional can get more done faster. For healthcare organizations, it is empowering them with a comprehensive suite of capabilities that amalgamates data from previously segregated sources within the organization—spanning electronic health records (EHRs), Picture Archiving and Communication Systems (PACS), lab systems, claims systems, and medical devices. This solution harmonizes structured, unstructured, imaging, and medical device data into the Fabric data lake, employing open data standards like FHIR, DICOM, and MedTech services, thereby establishing a unified architecture. Further enhancing its flexibility, connectors, and converters simplify data transformation between formats and facilitate the creation of specific pipelines. This multimodal data foundation serves as a platform for standardized, scalable solutions, expediting the discovery of impactful clinical and operational insights to elevate patient care. Fabric cultivates a consolidated data environment conducive to building and deploying AI models and extracting valuable insights. Moreover, it offers standard capabilities such as Observational Medical Outcomes Partnership (OMOP) analytics, enabling clinical research, patient outreach analytics, and personalized patient engagement. Introducing a novel de-identification service, Fabric ensures the privacy of patient-protected health information (PHI) by employing machine learning models to extract, redact, or surrogate identifiers, enabling insight extraction from unstructured data like medical documents and clinical trial studies. Additionally, healthcare-specific pre-built classification rules, labels, and data glossaries within Microsoft Purview enable healthcare organizations to effectively govern, protect, and manage their entire data estate.
- Azure AI Health Insights: Azure AI Health Insights, a cognitive service, furnishes clinicians and researchers with prebuilt models designed to analyze data and offer insights pivotal for informed decision-making during critical healthcare scenarios. Among these, three new models—currently in preview—come to the forefront.
- Patient Timeline: This leverages generative AI to extract crucial events like medications, diagnoses, and procedures from unstructured data, arranging them chronologically. This meticulous timeline offers clinicians a clearer, more accurate understanding of a patient’s medical history, enhancing the precision of care plans.
- Clinical Report Simplification: Utilizing generative AI, this model transforms intricate medical jargon into easily understandable language without compromising the clinical essence. This allows clinicians to communicate complex clinical information effectively, even with patients and others, facilitating comprehensive understanding.
- Radiology Insights: Offering quality checks, this model flags errors and inconsistencies within clinical documentation, ensuring accuracy. Moreover, it identifies follow-up recommendations and clinical findings, including measurements, recorded by radiologists, streamlining the interpretation process.
- Azure AI Health Bot: Azure AI Health Bot furnishes readily available healthcare intelligence that can be tailored and seamlessly integrated into current workflows. It draws on responses sourced from a healthcare organization’s internal content while also utilizing generative AI to offer information from reputable sources like the National Institutes of Health and the U.S. Food and Drug Administration.
- Text Analytics for Health: Text Analytics for Health, a language service within Azure AI, employs machine learning capabilities to extract and categorize crucial medical data from diverse unstructured texts. Recently unveiled industry open-source templates encompass population health, patient queries and answers utilizing Azure OpenAI Service, clinical trial patient cohorts, and large-scale historical data processing.
