Integrating remote patient monitoring solutions with healthcare data solutions in Microsoft Fabric
Co-Authors: Kemal Kepenek, Mustafa Al-Durra PhD, Matt Dearing, Jason Foerch, Manoj Kumar Introduction Remote patient monitoring solutions rely on connected devices, wearable technology, and advanced software platforms to collect and transmit patient health data. They facilitate monitoring of vital signs, chronic conditions, and behavioral patterns. Healthcare data solutions in Microsoft Fabric offers a secure, scalable, and interoperable data platform as part of Microsoft for Healthcare. Such a unified data platform is crucial for integrating disparate data sources and generating actionable health insights. This article provides a reference architecture and the steps to integrate remote patient monitoring solutions with healthcare data solutions in Fabric. The integration is aimed at satisfying low data resolution use cases. With low data resolution, we address infrequent (hourly, daily, or less) transfer of aggregated or point-in-time-snapshot device data into healthcare data solutions in Fabric to be used in a batch fashion to generate analytical insights. Integration steps for high data resolution use cases, which necessitate high frequency transfer of highly granular medical device data (for example, data from EKGs or ECGs) to become input to either batch or (near) real-time analytics processing and consumption, is a candidate for a future article. There are several methods, solutions and partners available in the marketplace today that will allow you to integrate a remote patient monitoring solution with the healthcare data solutions in Fabric. In this article, we leveraged the solution from Life365 (a Microsoft partner). The integration approach discussed here is applicable to most remote patient monitoring solutions whose integration logic (code) can be run inside a platform that can programmatically access (for example, through REST API calls) Microsoft Fabric. In our approach, the integration platform chosen is the Function App service within Microsoft Azure. In the subsequent sections of this article, we cover the integration approach in two phases: Interoperability phase, which illustrates how the data from medical devices (used by the remote patient monitoring solution) can be converted into format suitable for transferring into healthcare data solutions in Fabric. Analytical processing and consumption phase, which provides the steps to turn the medical device data into insights that can be easily accessed through Fabric. Integration Approach Interoperability Phase Step 1 of this phase performs the transfer of proprietary device data. As part of this step, datasets are collected from medical devices and transferred (typically, in the form of files) to an integration platform or service. In our reference architecture, the datasets are trans ferred to the Function App (inside an Azure Resource Group) that is responsible for the integration function. It is important for these datasets to contain information about (at least) three concepts or entities: Medical device(s) from which the datasets are collected. Patient(s) to whom the datasets belong. Reading(s) obtained from the medical device(s) throughout the time that the patients utilize these devices. Medical device readings data may be point-in-time data capture, metrics, measures, calculations, collections, or similar data points. Information about the entities listed above will be used in the later step of interoperability phase (discussed below) when we will convert this information into resources to be transferred to the second phase that will perform analytical processing and consumption. In step 2, to maintain mapping between proprietary device data and FHIR® resources, you can use transformation templates, or follow a programmatic approach, to convert datasets received from medical devices into appropriate FHIR® resources. Using the entities mentioned in the previous step, the conversion takes place as follows: Medical device information is converted to Device resource in FHIR® * . Patient information is converted to Patient resource in FHIR®. Device reading information is converted to Observation resource in FHIR®. * Currently, healthcare data solutions in Fabric supports FHIR® Release 4 (R4) standard. Consequently, the FHIR® resources that are created as part of this step should follow the same standard. Transformation and mapping activities are under the purview of each specific remote patient monitoring integration solution and are not reviewed in detail in this article. As an example, we provided below the high-level steps that one of the Microsoft partners (Life365) followed to integrate their remote patient monitoring solution with healthcare data solutions in Fabric: Life365 team developed a cloud-based transformation service that translates internal device data into standardized FHIR® (Fast Healthcare Interoperability Resources) Observations to enable compatibility with healthcare data solutions in Microsoft Fabric and other health data ecosystems. This service is implemented in Microsoft Azure Cloud and designed to ingest structured payloads from Life365-connected medical devices —including blood pressure monitors, weight scales, and pulse oximeters— and convert them into FHIR®-compliant formats in real time. When a reading is received: The service identifies relevant clinical metrics (e.g., systolic/diastolic blood pressure, heart rate, weight, SpO₂). These metrics are mapped to FHIR® Observation resources using industry-standard LOINC codes and units. Each Observation is enriched with references to the associated patient and device, formatted in NDJSON to meet the ingestion requirements in healthcare data solutions in Fabric. The resulting FHIR®-compliant data is securely transmitted to the Fabric instance using token-based authentication. This implementation provides a consistent, standards-aligned pathway for Life365 device data to integrate with downstream FHIR®-based platforms while abstracting the proprietary structure of the original device payloads. For examples from the public domain, you can use the following open-source projects as references: https://github.com/microsoft/fit-on-FHIR® https://github.com/microsoft/healthkit-to-FHIR® https://github.com/microsoft/FitbitOnFHIR® https://github.com/microsoft/FHIR®-Converter Please note that above open-source repositories might not be up to date. While they may not provide a complete (end to end) solution to map medical device data to FHIR®, they may still be helpful as a starting point. If you decide to incorporate them into your remote patient monitoring integration solution, validate their functionality and make necessary changes to meet your solution’s requirements. For the resulting FHIR® resources to be successfully consumed by the analytics processing later (within healthcare data solutions in Fabric), they need to satisfy the requisites listed below. Each FHIR® resource, in its entirety, needs to be saved as a single row into an NDJSON-formatted file. We recommend creating one NDJSON file per FHIR® resource type. That means creating Device.ndjson, Patient.ndjson, and Observation.ndjson files for the three entities we reviewed above. Each FHIR® resource needs to have a meta segment populated with inclusion of lastUpdated value. As an example: "meta":{"lastUpdated":"2025-05-15T15:35:04.218Z", "profile":["http://hl7.org/FHIR®/us/core/StructureDefinition/us-core-documentreference"]} Cross references between Observation and Patient, as well as between Observation and Device FHIR® resources need to be represented correctly, either through formal FHIR® identifiers or logical identifiers. As an example, the subject and device attributes of Observation FHIR® resource need to refer to Patient and Device FHIR® resources, respectively, in this manner: "subject":{"reference":"Patient/d3281621-1584-4631-bc82-edcaf49fda96"} "device":{"reference":"Device/5a934020-c2c4-4e92-a0c5-2116e29e757d"} For Patient FHIR® resource, if MRN is used as the identifier, it is important to represent the MRN value according to the FHIR® standard. Patient identifier is a critical attribute that it is used to establish cross-FHIR®-resource relationships throughout the analytics processing and consumption phase. We will review that phase later in this article. At a minimum, a Patient identifier, which uses MRN coding as its identifier type, needs to have its value, system, type.coding.system, and type.coding.code (with value “MR”) attributes populated correctly. See an example below. You can also refer to a Patient FHIR® resource example from hl7.org. "reference": null, "type": "Patient", "identifier": { "extension": null, "use": null, "value": "4e7e5bf8-2823-8ec1-fe37-eba9c9d69463", "system": "urn:oid: 1.2.36.146.595.217.0.1", "type": { "extension": null, "id": null, "coding": [ { "extension": null, "id": null, "system": "http://terminology.h17.org/CodeSystem/v2-0203", "version": null, "code": "MR", "display": null, "userSelected": null } "text": null }, ... With Step 3, to perform the transfer of FHIR® resource NDJSON files to healthcare data solutions in Fabric: Ensure that the integration platform (Azure Function App, in our case) has permission to transfer (upload) files to the healthcare data solutions in Fabric: Find the managed identity or the service principal that the Azure Function App is running under: Navigate to the Azure portal and find your Function App within your resource group. In the Function App's navigation pane, under "Settings," select "Identity". Identify the Managed Identity (if enabled): If System-assigned managed identity is enabled, you'll see information about the system-assigned managed identity, including its object ID and principal ID. If User-assigned managed identity is linked, the details of that identity will be displayed. You can also add user-assigned identities here if needed. Service Principal (if applicable): If the Function App is configured to use a service principal, you'll need to look for the service principal within the Azure Active Directory (a.k.a. Microsoft Entra ID). You can find this by searching for "Enterprise Applications" within Azure Active Directory and looking for the application associated with the Function App. Grant Azure Function App’s identity access to upload files: Having been logged into Fabric with an administrator account, navigate to the Fabric workspace where your healthcare data solutions instance is deployed. Click on the “Manage Access” button on the top right. Click on “Add People or Groups” Add the managed identity or the service principal, which is associated with your Azure Function App, with Contributor access by selecting “Contributor” from the dropdown list. Using a coding environment, similar to the Python example provided below, you can manage the OneLake content programmatically. This includes the ability to transfer (upload) the NDJSON-formatted files, which have been created earlier, to the destination OneLake folder. from azure.identity import DefaultAzureCredential from azure.storage.filedatalake import DataLakeFileClient, DataLakeFileSystemClient # Replace with your OneLake URI onelake_uri = "https://your-account-name.dfs.core.windows.net" # Replace with the destination path to your file file_path = "/<full path to destination folder (see below)>/<entity name>.ndjson" # Get the credential credential = DefaultAzureCredential() # Create a DataLakeFileClient file_client = DataLakeFileClient( url=f"{onelake_uri}{file_path}", credential=credential ) # Upload the file with open("<entity name>.ndjson", "rb") as f: file_client.upload_data(f, overwrite=True) print(f"File uploaded successfully: {file_path}") The destination OneLake folder to use for the remote patient monitoring solution integration into healthcare data solutions in Fabric is determined as follows: Navigate to the bronze lakehouse created with the healthcare data solutions instance inside the Fabric workspace. The lakehouse is named as “healthcare1_msft_bronze”. “healthcare1” segment in the name of the lakehouse points to the name of the healthcare data solutions instance deployed in the workspace. You might see a different name in your Fabric workspace; however, the rest of the lakehouse name (“_msft_bronze”) remains unchanged. Unified folder structure of healthcare data solutions is located inside the bronze lakehouse. Within that folder structure, create a subfolder named with the name of the remote patient monitoring solution you are integration with. See the screenshot below. This subfolder is referred to as namespace in healthcare data solutions documentation, and is used to uniquely identify the source of incoming (to-be-uploaded) data. NDJSON files, which have been generated during the previous interoperability phase, will be transferred (uploaded) into that subfolder. The full path of the destination OneLake folder to use in your file transfer (upload) code is: healthcare1_msft_bronze.Lakehouse\Files\Ingest\Clinical\FHIR®-NDJSON\<Solution-Name-as-Namespace> Analytics Processing and Consumption Phase Step 1 of this phase connects the interoperability phase discussed earlier with the analytics processing and consumption phase. As part of this step, you can simply verify that the NDJSON files have been uploaded to the remote patient monitoring solution subfolder inside the unified folder structure in bronze lakehouse of healthcare data solutions in Fabric. The path to that subfolder is provided earlier in this article. After the upload of the files has been completed, you are ready to run the data pipeline that will perform data ingestion and transformation so that the device readings data may be used for analytics purposes. In the Fabric workspace, where healthcare data solutions instance is deployed, find and open the data pipeline named “healthcare1_msft_omop_analytics”. As is the case with the bronze lakehouse name, “healthcare1” segment in the name of the data pipeline points to the name of the healthcare data solutions instance deployed in the workspace. You might see a different name in your Fabric workspace depending on your own instance. This data pipeline will execute four activities, first of which will copy the transferred files into another subfolder within unified folder structure so that they can be input to the ingestion step next. Then, the subsequent pipeline activities perform steps 2 through 4 as illustrated in the analytics processing and consumption phase diagram further above. Step 2 ingests the content from the transferred (NDJSON) file(s) to the ClinicalFHIR delta table of the bronze lakehouse. Step 3 transforms the content from the ClinicalFHIR delta table of the bronze lakehouse into flattened FHIR® data model content inside silver lakehouse. Step 4 transforms the flattened FHIR® content of silver lakehouse into OMOP data model content inside gold lakehouse. As part of step 5, you can develop your own gold lakehouse(s) through transforming content from the silver lakehouse into data model(s) best suited for your custom analytics use cases. Device data, once transformed into a gold lakehouse, may be used for analytics or reporting through several ways some of which are discussed briefly below. In step 6, Power BI reports and dashboards can be built inside Fabric that offer a visual and interactive canvas to analyze the data in detail. (Overview of Power BI - Microsoft Fabric | Microsoft Learn) As part of step 7, Fabric data share feature can be used to grant teams within external organizations (that you collaborate with) access to the data (External data sharing in Microsoft Fabric - Microsoft Fabric | Microsoft Learn). Finally, step 8 enables you to utilize the discover and build cohorts capability of healthcare data solutions in Fabric. With this capability, you can submit natural language queries to explore the data and build patient cohorts that fit the criteria that your use cases are aiming for. (Build patient cohorts with generative AI in discover and build cohorts (preview) - Microsoft Cloud for Healthcare | Microsoft Learn) Conclusion When integrated with healthcare data solutions in Fabric, remote patient monitoring solutions can enable transformative potential in enhancing patient outcomes, optimizing care coordination, and streamlining healthcare system operations. If your organization would like to explore the next steps in such a journey, please contact your Microsoft account team.
Orchestrate multimodal AI insights within your healthcare data estate (Public Preview)
In today’s healthcare landscape, there is an increasing emphasis on leveraging artificial intelligence (AI) to extract meaningful insights from diverse datasets to improve patient care and drive clinical research. However, incorporating AI into your healthcare data estate often brings significant costs and challenges, especially when dealing with siloed and unstructured data. Healthcare organizations produce and consume data that is not only vast but also varied in format—ranging from structured EHR entries to unstructured clinical notes and imaging data. Traditional methods require manual effort to prepare and harmonize this data for AI, specify the AI output format, set up API calls, store the AI outputs, integrate the AI outputs, and analyze the AI outputs for each AI model or service you decide to use. Orchestrate multimodal AI insights is designed to streamline and scale healthcare AI within your data estate by building off of the data transformations in healthcare data solutions in Microsoft Fabric. This capability provides a framework to generate AI insights by connecting your multimodal healthcare data to an ecosystem of AI services and models and integrating structured AI-generated insights back into your data estate. When you combine these AI-generated insights with the existing healthcare data in your data estate, you can power advanced analytics scenarios for your organization and patient population. Key features: Metadata store lakehouse acts as a central repository for the metadata for AI orchestration to effectively capture and manage enrichment definitions, view definitions, and contextual information for traceability purposes. Execution notebooks define the enrichment view and enrichment definition based on the model configuration and input mappings. They also specify the model processor and transformer. The model processor calls the model API, and the transformer produces the standardized output while saving the output in the bronze lakehouse in the Ingest folder. Transformation pipeline to ingest AI-generated insights through the healthcare data solutions medallion lakehouse layers and persist the insights in an enrichment store within the silver layer. Conceptual architecture: The data transformations in healthcare data solutions in Microsoft Fabric allow you ingest, store, and analyze multimodal data. With the orchestrate multimodal AI insights capability, this standardized data serves as the input for healthcare AI models. The model results are stored in a standardized format and provide new insights from your data. The diagram below shows the flow of integrating AI generated insights into the data estate, starting as raw data in the bronze lakehouse and being transformed to delta tables in the silver lakehouse. This capability simplifies AI integration across modalities for data-driven research and care, currently supporting: Text Analytics for health in Azure AI Language to extract medical entities such as conditions and medications from unstructured clinical notes. This utilizes the data in the DocumentReference FHIR resource. MedImageInsight healthcare AI model in Azure AI Foundry to generate medical image embeddings from imaging data. This model leverages the data in the ImagingStudy FHIR resource. MedImageParse healthcare AI model in Azure AI Foundry to enable segmentation, detection, and recognition from imaging data across numerous object types and imaging modalities. This model uses the data in the ImagingStudy FHIR resource. By using orchestrate multimodal AI insights to leverage the data in healthcare data solutions for these models and integrate the results into the data estate, you can analyze your existing data alongside AI enrichments. This allows you to explore use cases such as creating image segmentations and combining with your existing imaging metadata and clinical data to enable quick insights and disease progression trends for clinical research at the patient level. Get started today! This capability is now available in public preview, and you can use the in-product sample data to test this feature with any of the three models listed above. For more information and to learn how to deploy the capability, please refer to the product documentation. We will dive deeper into more detailed aspects of the capability, such as the enrichment store and custom AI use cases, in upcoming blogs. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations. FHIR® is the registered trademark of HL7 and is used with permission of HL7.Elevating care management analytics with Copilot for Power BI
Healthcare data solutions care management analytics capability offers a comprehensive template using the medallion Lakehouse architecture to unify and analyze diverse data sets of meaningful insights. This enables enhanced care coordination, improved patient outcomes, and scalable, sustainable insights. As the healthcare industry faces rising costs and growing demand for personalized care, data and AI are becoming critical tools. Copilot for Power BI leads this shift, blending AI-driven insights with advanced visualization to revolutionize care delivery. What is Copilot for Power BI? Copilot is an AI-powered assistant embedded directly into Power BI, Microsoft's interactive data visualization platform. By leveraging natural language processing and machine learning, Copilot helps users interact with their data more intuitively whether by asking questions in plain English, generating complex calculations, or uncovering patterns that might otherwise go unnoticed. Copilot for Power BI is embedded within healthcare data solutions, allowing care management—one of its core capabilities—to harness these AI-driven insights. In the context of care management analytics, this means turning a sea of clinical, claims, and operational data into actionable insights without needing to write a single line of code. This empowers teams across all technical levels to gain value from data. Driving better outcomes through intelligent insights in care management analytics The Care Management Analytics solution, built on the Healthcare data solutions platform, leverages Power BI with Copilot embedded directly within it. Here’s how Copilot for Power BI is revolutionizing care management: Enhancing decision-making with AI Traditionally, deriving insights from healthcare data required technical expertise and hours of analysis. Copilot simplifies this by allowing care managers and clinicians to ask questions like “Analyze which medical conditions have the highest cost and prevalence in low-income regions.” The AI interprets these queries and responds with visualizations, trends, and predictions—empowering faster, data-driven decisions. Proactive care planning By analyzing historical and real-time data, Copilot helps identify at-risk patients before complications arise. This enables care teams to intervene earlier, design more personalized care plans, and ultimately improve outcomes while reducing unnecessary hospitalizations. Operational efficiency From staffing models to resource allocation, Copilot provides visibility into operational metrics that can drive significant efficiency gains. Healthcare leaders can quickly identify bottlenecks, monitor key performance indicators (KPIs) and simulate “what-if” scenarios, enabling more i nformed, data-backed decisions on care delivery models. Reducing costs without compromising quality Cost containment is a constant challenge in healthcare. By highlighting areas of high spend and correlating them with clinical outcomes, Copilot empowers organizations to optimize care pathways and eliminate inefficiencies ensuring patients receive the right care at the right time, without waste. Democratizing data access Perhaps one of the most transformative aspects of Copilot is how it democratizes access to analytics. Non-technical users from care coordinators to nurse managers can interact with dashboards, explore data, and generate insights independently. This cultural shift encourages a more data-literate workforce and fosters collaboration across teams. Real-world impact Consider a healthcare system leveraging Power BI and Copilot to manage chronic disease populations more effectively. By combining claims data, social determinants of health (SDoH) indicators, and patient-reported outcomes, care teams can gain a comprehensive view of patient needs- enabling more coordinated care and proactively identifying care gaps. With these insights, organizations can launch targeted outreach initiatives that reduce avoidable emergency department (ED) visits, improve medication adherence, and ultimately enhance outcomes. The future is here The integration of Copilot for Power BI marks a pivotal moment for healthcare analytics. It bridges the gap between data and action, bringing AI to the frontlines of care. As the industry continues to embrace value-based care models, tools like Copilot will be essential in achieving the triple aim: better care, lower costs, and improved patient experience. Copilot is more than a tool — it is a strategic partner in you care transformation journey. Deployment of care management analytics Showcasing how a Population Health Director uncovers actionable insights through Copilot Note: To fully leverage the capabilities of the solution, please follow the deployment steps provided and use the sample data included with the Healthcare Data Solution. For more information on care management analytics, please review our detailed documentation and get started with transforming your healthcare data landscape today Overview of care management analytics - Microsoft Cloud for Healthcare | Microsoft Learn Deploy and analyze using Care management analytics - Training | Microsoft Learn. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.
Building Healthcare Research Data Platform using Microsoft Fabric
Co-Authors: Manoj Kumar, Mustafa Al-Durra PhD, Kemal Kepenek, Matt Dearing, Praneeth Sanapathi, Naveen Valluri Overview Research data platforms in healthcare providers, academic medical centers (AMCs), and research institutes support research, clinical decision making, and innovation. They consolidate data from various sources, making it accessible for comprehensive analysis and fostering collaboration among research teams. These platforms automate data collection, processing, and delivery, reducing time and effort needed for data management. This allows researchers to focus on their core activities while ensuring data security and regulatory compliance. The ability to work with multimodal data encourages interdisciplinary and interorganizational collaboration, uniting experts to address complex healthcare challenges. Current challenges Researchers face many common challenges as they work with multimodal healthcare data: Data integration and curation: The process of integrating various data types, such as clinical notes, imaging data, genomic information, and sensor data, presents significant challenges due to differences in formats, standards, and sources. Each AMC employs unique methods for data curation, with some utilizing on-premises solutions and others adopting hybrid cloud systems. No standardized approach currently exists for data curation, necessitating considerable organizational efforts to ensure data consistency and quality. Furthermore, data deidentification is often required to safeguard patient privacy. Data discovery and building cohorts: The lack of a unified multimodal data platform leads to the segregation of data across different modalities. Cohort discovery for each modality is performed separately and often lacks a self-service option, necessitating additional human resources to assist researchers in the data discovery process. This issue is particularly significant because researchers who require Institutional Review Board (IRB) approval cannot access the data beforehand but still need an effective method to identify and explore cohorts. Data delivery: Sensitive patient data, after institutional review board approval, must comply with privacy regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), requiring secure transfer to prevent breaches. The data, sourced from various systems, needs processing for research readiness. Delivering unified data from modalities like imaging, genomics, and health records is challenging. Typically, research IT teams curate cohort data and deliver it to an SQL database or a file share, accessed by researchers via secure virtual machines. This method often leads to data duplication, creating significant overhead due to numerous ongoing research projects. Cost management: Research projects are funded by government grants and private organizations. Managing these costs is challenging. Research IT departments often implement chargebacks for transparency and accountability in resource use. However, there is a disconnect between funding models and operations. Research teams favor capital expenditure (CapEx) with upfront funding for long-term resources, while cloud platforms operate on operational expenditure (OpEx), incurring ongoing costs based on usage. This shift can lead to concerns about unpredictable costs and budgeting difficulties. Bridging this gap requires careful planning, communication, and hybrid financial strategies to align research needs with cloud-based systems. Compliance with regulations: Healthcare research uses sensitive patient data, requiring strict adherence to HIPAA and GDPR. Transparency in data handling is essential but complex. Researchers must document disclosures thoroughly, detailing who accessed the data and for what purpose. However, tracking and auditing are often fragmented due to inconsistent systems. Variability in disclosure requirements from different agencies adds to compliance challenges. Balancing an auditable trail with privacy and manageable administrative tasks is crucial. Research data platform requirements Ability to curate multi modal data into the research data platform Ability for researchers to identify cohorts (without seeing data) to submit data requests to IRB Automated data delivery after IRB workflow approves the request to access relevant data Tools for researchers as part of the same platform Secure and regulatory-compliant environment for research. An approach to building a research data platform using Microsoft Fabric This article serves as a guide to healthcare organizations, offering a point of view and a prescriptive guidance on building a research data platform using Microsoft Fabric. The solution uses several features from healthcare data solutions in Microsoft Fabric, including its discover and build cohorts capability, and features from the Fabric platform. Microsoft Fabric: is a unified, AI-powered data platform designed to simplify data management and analytics. It integrates various tools and services to handle every stage of the data lifecycle, including ingestion, preparation, storage, analysis, and visualization. Fabric is built on a Software as a Service (SaaS) foundation, offering seamless experience for organizations to make data-driven decisions. For additional details, refer to the following link: What is Microsoft Fabric - Microsoft Fabric | Microsoft Learn Healthcare data solutions in Fabric: Healthcare data solutions in Fabric help you accelerate time to value by addressing the critical need to efficiently transform healthcare data into a suitable format for analysis. With these solutions, you can conduct exploratory analysis, run large-scale analytics, and power generative AI with your healthcare data. By using intuitive tools such as data pipelines and transformations, you can easily navigate and process complex datasets, overcoming the inherent challenges associated with unstructured data formats. For additional details, refer to the following links: Healthcare data solutions in Microsoft Fabric - Microsoft Cloud for Healthcare | Microsoft Learn Discover and build cohorts: Discover and build cohorts (preview) capability in healthcare data solutions enables healthcare organizations to efficiently analyze and query healthcare data from multiple sources and formats. It simplifies the preparation of data for health trend studies, clinical trials, quality assessments, historical research, and AI development. It supports natural language queries for multimodal data exploration and cohort building, making it ideal for research and AI-driven projects. For additional details, refer to the following link: Overview of discover and build cohorts (preview) - Microsoft Cloud for Healthcare | Microsoft Learn The proposal for research data platform architecture builds upon the following foundational premises: Recognition of Fabric as the all-in-one data storage, processing, management and analytics platform with enterprise-level features around security, availability and self-service. Adoption of Fabric Workspace(s) as the security boundary (a secure logical container) for maintaining data platform items (data storage and processing assets). Fabric workspaces may be provisioned for and used by different research data platform stakeholders (groups of users) with different requirements around use cases, data privacy, data sensitivity and access security. Use of healthcare data solutions in Fabric, as the core capability to maintain healthcare data assets in a standard (interoperable) manner. Healthcare data solutions enables the storage and processing of several healthcare data modalities and formats that follow industry standards (for example, clinical modality in FHIR® NDJSON format and Clinical-Imaging modality’s DICOM® format). Industry standards make it easier for research data platform stakeholders to share (exchange) data and insights within their own organization as well as (when needed) with other organizations that they collaborate with. Use of Fabric native capabilities to address requirements that may not (yet) have been implemented for healthcare specific needs. This provides the research data platform stakeholders with the flexibility to develop various data storage and processing workloads easily in a low (or no) code manner. Fig – Conceptual architecture of research data platform in Microsoft Fabric Note: This diagram is an architectural pattern and does not constitute one to one mapping of existing Microsoft products. Organizing source data in data workspace (One Data Hub in the above diagram) Organize your enterprise data into a data workspace that could be leveraged for research purposes. This acts as a ‘One Data Hub’ for the research data platform. Multiple Lakehouse can be present in this workspace. There should be at least one Lakehouse that organizes data using ‘unified folder structure’ best practice. Convert data from non-supported format to healthcare data solutions supported format to leverage out of the box transformation for multimodal data: For healthcare data solutions supported modalities: Implement custom transformations to convert data to supported modalities/format. For unsupported modalities: Implement extensions to bronze Lakehouse to accommodate additional data modalities. Epic data availability: Epic supports FHIR data export using Bulk FHIR APIs. If your dataset meets the use cases of Epic Bulk Data, you can store the resulting FHIR resources into One Data Hub for further transformation. Avoid data content duplication: Data duplication cannot be totally avoided. However, the same file and same content are never duplicated. There will be situations when data needs to be transformed to suit the needs of existing transformation pipelines for accelerating research data platform development. Additionally, OneLake in Fabric storage, where Lakehouse is maintained, uses file compression. Healthcare data solutions in Fabric has functionality to compress raw files to zip and always writes structured data to delta parquet which is a higher compressed format. More information can be found here - Data architecture and management in healthcare data solutions - Microsoft Cloud for Healthcare | Microsoft Learn Curating data for research (One Analytics workspace in the above diagram) Implement and extend Silver Lakehouse: A flattened FHIR® data model is provided by healthcare data solutions out of the box within the Silver Lakehouse. Extending the existing data model is possible through adding new columns to existing tables or through adding new tables in the Silver Lakehouse. If there is a need to introduce a different data model altogether, it is best to implement it using a different Lakehouse. Implement and extend Gold Lakehouse: Deploy and extend Observational Medical Outcomes Partnership Common Data Model (OMOP CDM): Deploy OMOP CDM 5.4 out of the box with healthcare data solutions deployment. Extend OMOP CDM to accommodate additional modalities. For example, implement Gene sequencing, Variant occurrence and Variant annotation tables to add genomics modality into OMOP CDM or implement medical imaging data on OMOP CDM as described here - Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension - PubMed Implement custom Gold Lakehouse(s): Implement other custom Gold Lakehouse using Fabric tools that run your transformation logic from Silver to Gold. These Lakehouse cannot be connected to discover and build cohorts capability within healthcare data solutions. Customers that need access to custom data can connect their custom cohort browsers to the SQL Analytics Endpoint(s) of their custom Gold Lakehouse(s). Enable data de-identification: Microsoft provides several solutions that can be used to implement a comprehensive de-identification solution that customers expect. Refer to the articles below for details. Dynamic data masking in Fabric Data Warehouse - Microsoft Fabric | Microsoft Learn Row-level security in Fabric data warehousing - Microsoft Fabric | Microsoft Learn Column-level security in Fabric data warehousing - Microsoft Fabric | Microsoft Learn Announcing a de-identification service for Health and Life Sciences | Microsoft Community Hub Cohort discovery using cohort builder tool Microsoft’s cohort browser: Today Discovery and Build Cohort supports eyes-on cohort discovery. This is an out of the box solution that is part of healthcare data solutions in Fabric. When eyes off discovery is supported, researchers as well as research IT can benefit from both eyes off and eyes on discovery and cohort building. 3rd-party cohort browser (e.g., OHDSI Atlas): Most 3rd party cohort browsers (E.g. OHDSI Atlas) and home-grown cohort browsers typically support connection to a SQL endpoint. Microsoft Fabric platform provides the capability of exposing SQL endpoint from a Lakehouse that can be connected to a 3rd party cohort browser to perform cohort discovery. Automated data delivery Creating research workspaces with cohort needed for research: Create separate workspaces for different research projects to keep Fabric items distinct and project specific using Fabric APIs. Assign workspaces to a Fabric capacity: Note: When needed, and if the organization has more than one Fabric capacity provisioned, workspace assignment can be spread across different capacities to help manage cost and performance. Next, set up a Lakehouse and provide access for team members (as per IRB approval list). This ensures both access and security at the workspace level. Export data to research workspace (format desired by researchers): Currently, DBC exports data as CSV/JSON files stored in a Lakehouse within the same workspace. Shortcut the destination Lakehouse into research workspace to keep the sanity of cohort data. Tools for researchers: Fabric provides several data engineering and data science tools out of the box that researchers can leverage to perform research. The following are some of the documents that customers can use to enable researchers with the tools of choice. Data science in Microsoft Fabric - Microsoft Fabric | Microsoft Learn Create, configure, and use an environment in Fabric - Microsoft Fabric | Microsoft Learn Migrate libraries and properties to a default environment - Microsoft Fabric | Microsoft Learn Charge back: Fabric compute pricing depends on the chosen Fabric capacity SKU. Assigning different Fabric capacities to different projects or groups within the same cost center can facilitate chargeback. See the step mentioned above on assigning a workspace to a Fabric capacity during workspace creation. Manage historic data migration to the research data platform on Fabric In most instances, customers already possess a research data platform. They seek to transition to this proposed solution without disrupting their current research data flow and obligations. Follow this approach to migrate or use data from the existing platform to the new one: Use your current research data platform as a Lakehouse or a Data Warehouse in Fabric (take advantage of Shortcut and Mirroring features available in Fabric). Fabric offers cross-database query, i.e. allowing to query and join multiple Lakehouse and data warehouses in a single query. Customers can choose how and where to implement such queries to augment the healthcare data solutions datasets with their existing datasets, all natively in Fabric. A bridge/mapping layer can be built to link the old and the new in a cross-relational way. Conceptually, such an approach has also ties to Bring Your Own Database (BYO-DB) requirement, which is the ability to bring custom defined format and still be able to easily convert to healthcare data solutions specific format. Other workflow integration Integrate research data platform with IRB workflow: IRB workflows are dependent on the tools utilized. For instance, eIRB solution from Huron. While there is currently no direct integration between IRB workflows and the research data platform on Fabric, it is possible to develop a connector using Power Platform integration with Fabric. Specific details are not available at this time as this remains an exploratory initiative. Another approach will be to use Fabric REST APIs (as a pro-code method) that can enable richer integration between Fabric and the 3 rd -party system, and a better consuming user experience at the end. Capture logs necessary for “accounting of disclosures”: Logs in Fabric can be captured at event level. It’s up to the customer to decide the level and type of logs that need to be captured for accounting of disclosure. This will need some custom implementation. One such capability of Fabric that can be used is: Track user activities in Microsoft Fabric - Microsoft Fabric | Microsoft Learn FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office and is used with their permission. DICOM® is the registered trademark of the National Electrical Manufacturers Association (NEMA) for its Standards publications relating to digital communications of medical information. If you are a Microsoft customer needing further information, support, or guidance related to the content in this blog, we recommend you reach out to your Microsoft account team in order to set up a discussion with the authors.Data Science & Engineering Copilot - Leverage Advanced AI Machine Learning Models
Are you looking to harness the power of AI to streamline your data science and engineering workflows in healthcare? In our upcoming webinar episode, " Data Science & Engineering Copilot - Leverage Advanced AI Machine Learning Models for Healthcare," we’ll showcase how Data Science and Data Engineering Copilot from Microsoft Fabric are transforming the way healthcare organizations manage, analyze, and derive insights from data. These AI-powered copilots enable healthcare teams to streamline complex data engineering pipelines and accelerate the development of advanced data science models.Driving Better Patient Outcomes with Care Management Analytics in Healthcare data solutions
In today's rapidly evolving healthcare landscape, effective data driven decision is more crucial than ever. The ability to analyse, manage, and optimize patient care processes relies on the seamless integration of diverse data sources like clinical, claims, social determinants of health etc. Leveraging the innovative medallion Lakehouse architecture, care management analytical template capabilities provide a robust platform for organizations to derive actionable insights and drive better patient outcomes. The Medallion Lakehouse for Care Management analytics Built on the foundation of the healthcare data solutions in Microsoft Fabric which utilizes the medallion Lakehouse architecture. This architecture consists of three foundational layers, each playing a critical role in transforming raw data into actionable insights: Bronze: The Raw Zone The Bronze layer serves as the raw data zone, storing all data in its original format. This data includes various sources such as patient encounters, conditions, treatment adherence records, and other relevant care management information. By maintaining this data in its raw form, organizations ensure the integrity and completeness of the dataset, providing a solid foundation for subsequent processing and analysis. Silver: The Enriched Zone In the Silver layer, data from the Bronze Lakehouse is enriched and transformed into a standardized format for analysis. This layer stores metadata and file references based on healthcare interoperability standards such as FHIR (Fast Healthcare Interoperability Resources). The enriched data provides a holistic view of the patient record, integrating different modalities in healthcare data solutions which are critical for comprehensive care analysis. Gold: The Curated Zone The Gold layer represents the curated zone, where data is refined and structured for advanced analytics and reporting. By building a comprehensive data model, the data is optimized for, predictive analytics, and reporting dashboards that can provide deep insights into care quality, patient outcomes, and operational efficiency. Conceptual Architecture Care management analytics involves integrating and analysing diverse datasets, including clinical, claims and social determinants of health data. The medallion Lakehouse architecture in Microsoft Fabric offers the flexibility to ingest and process these data types at scale. The data flows from raw data ingestion to transformation into the Gold Lakehouse format. End to End execution steps Step 1: Create a workspace and add health solutions capability. Step 2: Set up healthcare data solutions on your Fabric workspace. Follow the guidance from the deployment wizard and add sample data if needed. Step 3: Select the Care Management analytics capability and click on Deploy. Step 4: Copy the sample data downloaded into the bronze lakehouse under Process\Clinical\FHIR-HDS folder Step 5: Run the care management analytical data pipeline to transform the data from the bronze lakehouse to gold lakehouse. Step 6: Access the Power BI dashboards once the above steps are completed to view detailed visualization on Clinical and Claims data. Transforming Care Management analytics with healthcare data solutions Healthcare data solutions care management analytics capability provides a comprehensive template solution for customers and partners to unify and analyze diverse data. By leveraging the medallion Lakehouse architecture, healthcare organizations can unlock the potential of their data, enhance care coordination, and drive better patient outcomes. The seamless integration of raw, enriched, and curated data layers ensures that insights are not only actionable but also scalable and sustainable. For more information on how Healthcare data solutions can revolutionize your care management analytics, please review our detailed documentation and get started with transforming your healthcare data landscape today. https://go.microsoft.com/fwlink/?linkid=2284603 FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office, and is used with their permission. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.
Seamlessly use social determinants of health data in healthcare data solutions in Microsoft Fabric
Social determinants of health are the social conditions that contribute to an individual’s or a population group’s health outcomes, like place of birth, median household income, and access to transportation. Research and real-world evidence have established that SDOH information can complement medical information. This helps healthcare organizations understand their patients’ health profile more comprehensively and facilitate tailored care interventions. However, a fundamental challenge in leveraging SDOH data arises due to the lack of a standard data collection and exchange mechanism. To simplify this process, we are thrilled to announce the public preview of SDOH datasets- transformations (SDOH) in healthcare data solutions in Microsoft Fabric. It fuels large-scale analytics by enabling the unification of social determinants of health data with core healthcare domains like clinical & claims. Key features SDOH information can be seen in two forms- Public datasets that contain social determinant details aggregated at a geographic level, and patient-level SDOH data that depict those characteristics of an individual that might pose health risks. This release focuses on the public SDOH datasets, which comes with, A simple and intuitive data preparation mechanism to ready the datasets for ingestion into healthcare data solutions. The supported data formats are .csv and .xlsx. A set of powerful pipelines and notebooks that allow effortless transformation of the datasets into tabular shapes. Eight sample datasets across various SDOH domains that you can readily leverage for your use cases. As the data progresses through the medallion Lakehouse, it gets persisted within a robust data model, custom-built for the SDOH modality. This eases the process of combining SDOH data with other modalities, unlocking use cases such as Care management analytics, Risk stratification, and Population health. How it works The SDOH capability follows three simple steps to transform the disparate datasets into a unified data model, Data preparation and ingestion- As there are no established standards to collect and exchange the information captured in these datasets, it is necessary to unify them into a common shape before they can be ingested. This step requires you to add three sheets in your original dataset to capture key details like publisher information, description of the data columns, and location information. The shipped sample datasets are pre-populated with all the necessary information. Landing zone to bronze- Once the datasets are prepared, they can be uploaded into the landing zone. The bronze notebook will then populate all the key details in the bronze lake in delta table format. Bronze to silver- This notebook normalizes the data from the bronze lake into the custom SDOH data model in the silver lake by creating dedicated tables and establishing relationships between them. It preserves the context of the source tables to help you easily identify or query the data. You can trigger the SDOH pipeline to run all the steps after data preparation at one go and thereafter utilize the normalized silver lake data to build your analytical scenarios. Get started today The SDOH public preview is available in healthcare data solutions for teams to start using today. For a more detailed overview of the capability and the necessary configurations needed to deploy it, please check out the official documentation. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.Unlocking the Potential of Claims Data Insights in Healthcare data solutions in Microsoft Fabric
As part of the continuous innovation within healthcare data solutions in Microsoft Fabric, we are excited to introduce our CMS Claims data transformations capability. This feature specifically tailored to handle CMS CCLF formats. By ingesting, CMS Claims data into healthcare data solutions enables customers to assess the effectiveness of their care management programs, monitor population-level trends and utilization, also and measure their performance against benchmarks to reduce overall claim expenses and improve patient care. Leveraging the Medallion Lakehouse architecture, this feature allows healthcare organizations to seamlessly integrate claims data into the unified data platform provided in Microsoft Fabric. This solution supports scalable data ingestion and transformation workflows that convert claims data into tabular shapes, promoting efficient healthcare delivery and decision-making. The Medallion Lakehouse for Claims Data The Medallion Lakehouse architecture for claims data is built on the foundational layers of Microsoft Fabric’s healthcare data solutions. This architecture is designed to support the ingestion, transformation, and analysis of healthcare claims data. It comprises three fundamental layers: Bronze (Raw Zone): The first layer stores raw claims data in its original CCLF format. This raw zone acts as a staging area where files are ingested directly into the Lakehouse, maintaining the data’s original integrity. It supports native and compressed file types of the claims format, ensuring compatibility and flexibility in data processing. Silver (Enriched Zone): This intermediate layer focuses on processing and enriching the raw claims data by transforming it into structured formats based on the FHIR specifications. It leverages data transformation tools to parse and map the claims data into FHIR financial resources, storing the transformed data in a format optimized for querying and analysis. Gold (Curated Zone): The final layer aggregates the enriched data to create a highly curated dataset, optimized for reporting and analytics. In this zone, the data is further transformed into OMOP CDM (Observational Medical Outcomes Partnership Common Data Model) or a custom data model. The mapping ensures compatibility with various analytical and machine-learning models that healthcare organizations might deploy for deeper insights. Conceptual architecture The volume and complexity of claims data often require robust solutions to manage and extract valuable insights efficiently. Microsoft Fabric’s Medallion Lakehouse provides a comprehensive approach to handling these needs, offering three distinct ingestion patterns based on organizational requirements and existing data infrastructure: End to End execution steps Step 1: Create a workspace and add health solutions capability. Step 2: Setup healthcare data solutions on your Fabric workspace. Follow the guidance from the deployment wizard and add sample data if needed. Step 3: Select the CMS claims data transformations capability and click on Deploy. Step 4: Upload the CCLF files in following folder structure Ingest\Claims\CCLF\<namespace> Ingest folder structure to upload the CCLF files Step 5: Run the claims data pipeline to transform the data from the Bronze Lakehouse to Silver Lakehouse. Step 6: To validate the ingested and transformed data, check the ExplanationOfBenefits table to view the data The CMS claims data transformations capability within Microsoft Fabric’s Medallion Lakehouse architecture offers a powerful and scalable solution for healthcare organizations to integrate, manage, and analyse claims data effectively. By transforming raw claims data into FHIR, healthcare data solutions in Microsoft Fabric enables seamless interoperability and supports advanced analytics, providing a robust foundation for enhanced healthcare delivery and operational efficiency. For further details and documentation, Overview of CMS claims data transformations(preview) FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office, and is used with their permission. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.
