Modernizing Digital Health Record Governance with Microsoft Entra Identity Governance
With Entra Identity Governance Microsoft provides cloud-driven identity lifecycle automation, application provisioning, entitlement management, and access reviews that can be applied to users, guests, agents, groups, and enterprise applications—including EHR systems like Epic, Oracle Health (Cerner), and Meditech.Bringing Organizational Knowledge into the Clinical Workflow
This blog is co-authored by Hadas Bitran, Partner GM, Health AI, Microsoft Health & Life Sciences Every day, clinicians spend valuable time looking for information that lives in different places. An email thread from a specialist colleague. A Microsoft Teams discussion about a complex case. Updated organizational processes buried in SharePoint or OneDrive. This information provides context that could be critical to their workflows or help inform their decisions. But that context is not part of their clinical workflow. The result? Clinicians are forced to break their clinical workflow, searching manually across organizational resources, and mentally combining scattered data points, all while a patient is waiting. This isn't a knowledge problem. It's a retrieval problem. And it's costing time, focus, cognitive burden and clinical confidence every single day. That's exactly the gap we're closing by bringing clinical intelligence and your organization's knowledge into one seamless, workflow-native experience. Clinical workflow, now with your organizational context Within Dragon Copilot, clinicians will be able to securely surface relevant information across Microsoft 365, without leaving the clinical workflow: Email: retrieve relevant information that was exchanged with patients, colleagues or from specialist correspondence, referral communications, or care coordination threads. find me the email from Dr. Ting that mentioned the latest research about this mutation. In this example, the chat functionality in Dragon Copilot uses the patient and encounter context to resolve the referenced mutation, then leverages Microsoft 365 Copilot behind the scenes to locate the email from Dr. Ting that mentions it. Microsoft Teams: surface information from Microsoft Teams chats that the clinician had with colleagues, discussions or group chat conversations. The patient is traveling to Florida. Identify dialysis centers near the patient’s destination based on information shared by Dr. Salomon in Microsoft Teams and provide practical travel guidelines I can share with the patient. In this example, Dragon Copilot uses trusted sources for travel guidelines and Microsoft 365 Copilot to retrieve relevant Microsoft Teams messages from Dr. Salomon, identifying nearby dialysis centers in Florida. SharePoint and OneDrive: access organizational knowledge on demand: HR policies, facility procedures, compliance guidelines, shift schedules, and more Who is on call for nephrology tonight and who is covering tomorrow morning? In this example, Dragon Copilot leverages Microsoft 365 Copilot behind the scenes to locate the most up‑to‑date Excel file with upcoming shift and coverage information from the hospital’s SharePoint, and surfaces the answer directly in the conversation, without disrupting the clinician’s workflow. With Microsoft 365 Copilot, work context is available directly inside Dragon Copilot, clinicians can choose if, and when to access their work information. Within Dragon Copilot, they can ask questions in natural language and receive the most relevant information, grounded in patient context, from trusted clinical sources and their Microsoft 365 data. One conversational flow. Full clinical and work context. No tab switching, no manual searching, no lost focus. Trusted by design, built for healthcare Security and privacy are built in from the ground up. Information is always accessed on behalf of the individual user, fully respecting existing Microsoft 365 identity and access management, compliance, and privacy controls, meaning clinicians see only what they're authorized to see, and that Dragon Copilot will only use their work context if the clinician consented to it. This also means no new security risks to manage, and no changes to how your organization governs access to information. For healthcare organizations where data sensitivity, regulatory compliance, and patient privacy are non-negotiable, this better-together experience is designed to meet that bar from day one. Join the Private Preview If you're a Dragon Copilot customer, and your organization is using Microsoft 365 Copilot, we invite you to be among the first to experience this new capability. Register now for early access to the private preview and play a role in shaping the future of clinical workflow intelligence. Register for private preview
Seamlessly manage Dragon Copilot with the new Microsoft Dragon admin center
Today, we are thrilled to announce the Microsoft Dragon admin center – a new way to manage your Microsoft Cloud for Healthcare clinical applications including Microsoft Dragon Copilot. This user-friendly platform, built upon Microsoft 365 and Microsoft’s e-commerce framework, enables healthcare administrators to control and manage their licensing, billing and organizational lifecycle with ease and efficiency. The Microsoft Dragon admin center streamlines the implementation and management of clinical applications in the health provider ecosystem, reducing time from weeks or months to days. Microsoft Dragon Copilot can be purchased and provisioned quickly with a few clicks. We are excited to have Microsoft partners and customers try it out! Benefits The Microsoft Dragon admin center provides numerous benefits to healthcare organizations and partners: Efficiency: Streamlines administration of clinical applications through a centralized and unified interface that provides consistency across all administrative functions. Partner Integration: Offers flexibility to embed Dragon Copilot in the Electronic Health Record (EHR) system of choice or resell the application out of the box. Customization: Enables high degrees of customization for administrators managing wide ranges of users. Scalability: Allows healthcare providers to scale clinical applications within a few hours. Compliance: Adheres to Microsoft standards of privacy, compliance, and security. Key Features The Microsoft Dragon admin center offers several key features that make it an indispensable tool for healthcare administrators: Simplified license management, user role assignment, and billing allows customers to easily purchase more or upgrade licenses depending on business needs. Seamless and automated provisioning of the Dragon Copilot application limits deployment delays. Customizable organization hierarchy empowers healthcare administrators to manage their organization in a few clicks. One stop shop for managing Electronic Health Record (EHR) partners and users operating in the embedded Dragon Copilot application reduces the complexity and time required to manage multiple systems and partners separately. Extensive configuration of settings and library objects of Dragon Copilot increases time-to-value. How to Get Started Getting started with the Microsoft Dragon admin center is straightforward: Purchase licenses: Identify the type of billing account you have in M365 and contact your Microsoft representative to purchase licenses. If you are a Microsoft Partner you can purchase through Partner Center. Assign licenses and conduct user role management: Assign licenses and provide different individuals the right roles to administer the Dragon admin center. Once the license and user role management is complete, navigate to the Microsoft Dragon admin center where you will be able to: Provision your Dragon Copilot application. Set up your organization hierarchy and healthcare groups, and manage your Electronic Health Record partners (EHRs). Manage and configure your Dragon Copilot application settings, features, and library objects in the context of your organization hierarchy. For a detailed step by step set-up guide for Microsoft Dragon admin center, please visit: End-to-end workflow overview | Microsoft Learn Conclusion The Microsoft Dragon admin center is a valuable tool that empowers healthcare administrators and streamlines clinical application management. By leveraging its advanced functionalities and user-friendly interface, healthcare organizations can enhance efficiency, accuracy, and customization in their workflows. Learn more about the Microsoft Dragon admin center here: Dragon admin center documentation | Microsoft LearnHealthcare Agent Orchestrator: Multi-agent Framework for Domain-Specific Decision Support
At Microsoft Build, we introduced the Healthcare Agent Orchestrator, now available in Azure AI Foundry Agent Catalog . In this blog, we unpack the science: how we structured the architecture, curated real tumor board data, and built robust agent coordination that brings AI into real healthcare workflows. Healthcare Agent Orchestrator assisting a simulated tumor board meeting. Introduction Healthcare is inherently collaborative. Critical decisions often require input from multiple specialists—radiologists, pathologists, oncologists, and geneticists—working together to deliver the best outcomes for patients. Yet most AI systems today are designed around narrow tasks or single-agent architectures, failing to reflect the real-world teamwork that defines healthcare practice. That’s why we developed the Healthcare Agent Orchestrator: an orchestrator and code sample built around Microsoft’s industry-leading healthcare AI models, designed to support reasoning and multidisciplinary collaboration -- enabling modular, interpretable AI workflows that mirror how healthcare teams actually work. The orchestrator brings together Microsoft healthcare AI models—such as MedImageParse for image recognition, CXRReportGen for automated radiology reporting, and MedImageInsight for retrieval and similarity analysis—into a unified, task-aware system that enables developers to build an agent that reflects real-word healthcare decision making pattern. This work was led by Yu (Aiden) Gu, Principal Applied Scientist at Microsoft Research, who conceived the study, defined the research direction, and led the design and development of the Healthcare Agent Orchestrator proof-of-concept. Healthcare Is Naturally Multi-Agent Healthcare decision-making often requires synthesizing diverse data types—radiologic images, pathology slides, genetic markers, and unstructured clinical narratives—while reconciling differing expert perspectives. In a molecular tumor board, for instance, a radiologist might highlight a suspicious lesion on CT imaging, a pathologist may flag discordant biopsy findings, and a geneticist could identify a mutation pointing toward an alternate treatment path. Effective collaboration in these settings hinges not on isolated analysis, but on structured dialogue—where evidence is surfaced, assumptions are challenged, and hypotheses are iteratively refined. To support the development of healthcare agent orchestrator, we partnered with a leading healthcare provider organization, who independently curated and de-identified a proprietary dataset comprising longitudinal patient records and real tumor board transcripts—capturing the complexity of multidisciplinary discussions. We provided guidance on data types most relevant for evaluating agent coordination, reasoning handoffs, and task alignment in collaborative settings. We then applied LLM-based structuring techniques to convert de-identified free-form transcripts into interpretable units, followed by expert review to ensure domain fidelity and relevance. This dataset provides a critical foundation for assessing agent coordination, reasoning handoffs, and task alignment in simulated collaborative settings. Why General-Purpose LLMs Fall Short for Healthcare Collaboration While general-purpose large language models have delivered remarkable results in many domains, they face key limitations in high-stakes healthcare environments: Precision is critical: Even small hallucinations or inconsistencies can compromise safety and decision quality Multi-modal integration is required: Many healthcare decisions involve interpreting and correlating diverse data types—images, reports, structured records—much of which is not available in public training sets Transparency and traceability matter: Users must understand how conclusions are formed and be able to audit intermediate steps The Healthcare Agent Orchestrator addresses these challenges by pairing general reasoning capabilities with specialized agents that operate over imaging, genomics, and structured EHRs—ensuring grounded, explainable results aligned with clinical expectations. Each agent contributes domain-specific expertise, while the orchestrator ensures coherence, oversight, and explainability—resulting in outputs that are both grounded and verifiable. Architecture: Coordinating Specialists Through Orchestration Healthcare Agent Orchestrator. Healthcare Agent Orchestrator’s multi-agent framework is built on modular AI infrastructure, designed for secure, scalable collaboration: Semantic Kernel: A lightweight, open-source development kit for building AI agents and integrating the latest AI models into C#, Python, or Java codebases. It acts as efficient middleware for rapidly delivering enterprise-grade solutions—modular, extensible, and designed to support responsible AI at scale. Model Context Protocol (MCP): an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. Magentic-One: Microsoft’s generalist multi-agent system for solving open-ended web and file-based tasks across domains—built on Microsoft AutoGen, our popular open-source framework for developing multi-agent applications. Each agent is orchestrated within the system and integrated via Semantic Kernel’s group chat infrastructure, with support for communication and modular deployment via Azure. This orchestration ensures that each model—whether interpreting a lung nodule, analyzing a biopsy image, or summarizing a genomic variant—is applied precisely where its expertise is most relevant, without overloading a single system with every task. The modularity of the framework also future-proofs: as new health AI models and tools emerge, they can be seamlessly incorporated into the ecosystem without disrupting existing workflows—enabling continuous innovation while maintaining clinical stability. Microsoft’s healthcare AI models at the Core Healthcare agent orchestrator also enables developers to explore the capabilities of Microsoft’s latest healthcare AI models: CXRReportGen: Integrates multimodal inputs—including current and prior X-ray images and report context—to generate grounded, interpretable radiology reports. The model has shown improved accuracy and transparency in automated chest X-ray interpretation, evaluated on both public and private data. MedImageParse 3 : A biomedical foundation model for imaging parsing that can jointly conduct segmentation, detection, and recognition across 9 imaging modalities. MedImageInsight 4 : Facilitates fast retrieval of clinically similar cases, supports disease classification across broad range of medical image modalities, accelerating second opinion generation and diagnostic review workflows. Each model has the ability to act as a specialized agent within the system, contributing focused expertise while allowing flexible, context-aware collaboration orchestrated at the system level. CXRReportGen is included in the initial release and supports the development and testing of grounded radiology report generation. Other Microsoft healthcare models such as MedImageParse and MedImageInsight are being explored in internal prototypes to expand the orchestrator’s capabilities across segmentation, detection, and image retrieval tasks. Seamless Integration with Microsoft Teams Rather than creating new silos, Healthcare Agent Orchestrator integrates directly into the tools clinicians already use—specifically Microsoft Teams. Developers are investigating how clinicians can engage with agents through natural conversation, asking questions, requesting second opinions, or cross-validating findings—all without leaving their primary collaboration environment. This approach minimizes friction, improves user experience, and brings cutting-edge AI into real-world care settings. Building Toward Robust, Trustworthy Multi-Agent Collaboration Think of the orchestrator as managing a secure, structured group chat. Each participant is a specialized AI agent—such as a ‘Radiology’ agent, ‘PatientHistory’ agent, or 'ClinicalTrials‘ agent. At the center is the ‘Orchestrator’ agent, which moderates the interaction: assigning tasks, maintaining shared context, and resolving conflicting outputs. Agents can also communicate directly with one another, exchanging intermediate results or clarifying inputs. Meanwhile, the user can engage either with the orchestrator or with specific agents as needed. Each agent is configured with instructions (the system prompt that guides its reasoning), and a description (used by both the UI and the orchestrator to determine when the agent should be activated). For example, the Radiology agent is paired with the cxr_report_gen tool, which wraps Microsoft’s CXRReportGen model for generating findings from chest X-ray images. Tools like this are declared under the agent’s tools field and allow it to call foundation models or other capabilities on demand—such as the clinical_trials tool 5 for querying ClinicalTrials.gov. Only one agent is marked as facilitator, designating it as the moderator of the conversation; in this scenario, the Orchestrator agent fills that role. Early observations highlight that multi-agent orchestration introduces new complexities—even as it improves specialization and task alignment. To address these emergent challenges, we are actively evolving the framework across several dimensions: Mitigating Error Propagation Across Agents: Ensuring that early-stage errors by one agent do not cascade unchecked through subsequent reasoning steps. This includes introducing critical checkpoints where outputs from key agents are verified before being consumed by others. Optimizing Agent Selection and Specialization: Recognizing that more agents are not always better. Adding unnecessary or redundant agents can introduce noise and confusion. We’ve implemented a systematic framework that emphasizes a few highly suited agents per task —dynamically selected based on case complexity and domain needs—while continuously tracking performance gains and catching regressions early. Improving Transparency and Hand-off Clarity: Structuring agent interactions to make intermediate outputs and rationales visible, enabling developers (and the system itself) to trace how conclusions were reached, catch inconsistencies early, and intervene when necessary. Adapting General Frameworks for Healthcare Complexity Generic orchestration frameworks like Semantic Kernel provide a strong foundation—but healthcare demands more. The stakes are higher, the data more nuanced, and the workflows require precision, traceability, and regulatory compliance. Here’s how we’ve extended and adapted these systems to help address healthcare demands: Precision and Safety: We introduced domain-aware verification checkpoints and task-specific agent constraints to reduce inappropriate tool usage—supporting more reliable reasoning. To help uphold the high standards required in healthcare, we defined two complementary metric systems (Check Healthcare Agent Orchestrator Evaluation for more details): Core Metrics: monitor health agents selection accuracy, intent resolution, contextual relevance, and information aggregation RoughMetric: a composite score based on ROUGE that helps quantify the precision of generated outputs and conversation reliability. TBFact: A modified version of RadFact 2 that measures factuality of claims in agents' messages and helps identifying omissions and hallucination Domain-Specific Tool Planning: Healthcare agents must reason across multimodal inputs—such as chest X-rays, CT slices, pathology images, and structured EHRs. We’ve customized Semantic Kernel’s tool invocation and planning modules to reflect clinical workflows, not generic task chains. These infrastructure-level adaptations are designed to complement Microsoft Healthcare AI models—such as CXRReportGen, MedImageParse, and MedImageInsight—working together to enable coordinated, domain-aware reasoning across complex healthcare tasks. Enabling Collaborative, Trustworthy AI in Healthcare Healthcare demands AI systems that are as collaborative, adaptive, and trustworthy as the clinical teams they aim to support. The Healthcare Agent Orchestrator is a concrete step toward that vision—pairing specialized health AI models with a flexible, multi-agent coordination framework, purpose-built to reflect the complexity of real clinical decision-making. By aligning with existing healthcare workflows and enabling transparent, role-specific collaboration, this system shows promise to empower clinicians to work more effectively—with AI as a partner, not a replacement. Healthcare Multi-Agent Orchestrator and the Microsoft healthcare AI models are intended for research and development use. Healthcare Multi-Agent Orchestrator and the healthcare AI models not designed or intended to be deployed in clinical settings as-is nor is it intended for use in the diagnosis or treatment of any health or medical condition, and its performance for such purposes has not been established. You bear sole responsibility and liability for any use of Healthcare Multi-Agent Orchestrator or the healthcare AI models, including verification of outputs and incorporation into any product or service intended for a medical purpose or to inform clinical decision-making, compliance with applicable healthcare laws and regulations, and obtaining any necessary clearances or approvals. 1 arXiv, Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale, February 2, 2025 2 arXiv, MAIRA-2: Grounded Radiology Report Generation, June 6, 2024 3 Nature Method, A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities, Nov 18, 2024 4 arXiv, Medimageinsight: An open-source embedding model for general domain medical imaging, Oct 9, 2024 5 Machine Learning for Healthcare Conference, Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology, August 4, 2023
Copilot Chat: Downloads
On PC and Mac: Follow the download links below to install the Copilot Chat desktop app. Double-click the installer when prompted, and you're in. Windows: Microsoft 365 Copilot - Free download and install on Windows | Microsoft Store MacOS: Microsoft 365 Copilot on the App Store On Mobile: Scan the QR code to download the app to your device. In Your Browser: Prefer not to download anything? You can also access Copilot Chat from Microsoft 365 Copilot Chat. Once you're in, try starting a conversation in the prompt box. Not sure where to begin? No worries—use or tweak one of the suggested prompts to get going. Here are a few other handy entry points:Microsoft deployment blueprint - Address oversharing concerns for your M365 Copilot deployment
Optimized deployment leverages advanced compliance and automation capabilities available in Microsoft 365 E5. This episode outlines how E5 customers can proactively secure data and enhance Copilot performance.How Copilot Can Save Us Energy
Let’s face it! Our homes are getting smarter, but our energy bills are getting dumber. If you’ve ever asked Alexa to dim the lights while binge watching your favorite show or told Google Home to crank up the AC during a heatwave, congratulations, you’ve officially joined the AI-powered energy club. But before you start blaming your smart speaker for your rising electricity costs, let’s talk about how Copilot can actually help you save energy (and maybe even your sanity).😁 First, the good news. Devices like Amazon Alexa and Google Home are not just glorified trivia machines, they’re energy-saving ninjas when used correctly. According to Tom’s Guide and SmartHomeMuse, setting up routines like "Alexa, I’m leaving, can you automatically turn off lights, lower thermostats, and shut down unnecessary devices?" Google Home can do the same, adjusting smart thermostats based on occupancy and weather forecasts. It’s like having a personal energy butler who never complains. And then there’s the Alexa Energy Dashboard. A nifty tool that tracks the power usage of connected devices. It’s like Fitbit for your fridge, letting you see which gadgets are guzzling electricity and which ones are behaving. Pair that with smart plugs and solar panel integration, and you’ve got a recipe for serious savings. Even Alexa’s 'Hunches' feature can detect when you’re away and shut things down automatically. Smart, right? 👍 But here’s the plot twist: these devices can also be energy vampires. According to Harvard Magazine and SFGATE, the 'always-on' nature of smart assistants means they’re constantly listening, syncing, and updating. Even when you’re not talking to them. That persistent power draw adds up, especially in homes with multiple devices. The Amazon Echo, for example, has no battery and must be plugged in 24/7. It’s like having a roommate who never sleeps and always leaves the lights on. Internal reports like the Amazon 2020 Sustainability Report and Alexa usage studies show that frequent users often have entire ecosystems of smart devices lights, thermostats, speakers, and more, all connected and consuming energy. Without proper optimization, your smart home could become a not-so-smart drain on your wallet. So, what’s the solution? Enter Copilot. By leveraging AI to automate energy-saving routines, monitor device usage, and suggest optimizations, Copilot can help you strike the perfect balance between convenience and conservation. Think of it as your energy-saving sidekick. Always watching, always learning, and never judging you for asking Alexa to play 'Eye of the Tiger' at 2 a.m. In conclusion, smart assistants are a double-edged sword. They can save you energy if used wisely or sneakily inflate your bills if left unchecked. With Copilot in your corner, you can harness the power of AI to make your home smarter, greener, and a little less expensive. And hey, if it also helps you win trivia night, that’s just a bonus. 😉Microsoft deployment blueprint - Address oversharing concerns for your M365 Copilot deployment
In regulated industries, internal oversharing can compromise data integrity and Copilot effectiveness. This episode defines what “Foundational” means for Microsoft 365 E3 customers and outlines actionable steps to mitigate oversharing risks during Copilot deployment.Mastering Agent Governance in Microsoft 365
The "Mastering Agent Governance in Microsoft 365" series is based on the Administering and Governing Agents whitepaper published by Microsoft and designed to educate IT leaders, compliance officers, and decision-makers about the importance of governance for AI agents in Microsoft 365, particularly in highly regulated industries like Healthcare and Life Sciences (HLS). The six-episode series cover the growing role of agents, the risks of unmanaged agents, and the strategic importance of governance frameworks. Empowering innovation while protecting patient data and ensuring compliance In the age of AI-powered productivity, agents—automated digital assistants built with tools like Microsoft 365 Copilot, SharePoint, and Copilot Studio—are transforming how work gets done. From streamlining clinical documentation to automating regulatory reporting, agents are becoming indispensable in Healthcare and Life Sciences (HLS). But with great power comes great responsibility. Why Governance Can’t Be an Afterthought In highly regulated industries like HLS, where data sensitivity and compliance are paramount, the rise of autonomous agents introduces new risks: Unauthorized data access could expose protected health information (PHI). Unmonitored agent behavior could lead to regulatory violations. Lack of lifecycle controls could result in outdated or insecure agents operating in production environments. Agent governance isn’t just an IT concern—it’s a business imperative. It ensures that innovation doesn’t outpace compliance, and that every agent deployed aligns with organizational policies, security standards, and regulatory frameworks like HIPAA, GDPR, and FDA 21 CFR Part 11. Understanding the Agent Landscape Microsoft 365 supports a spectrum of agent creators: End Users using SharePoint or Copilot templates to automate simple tasks. Makers building more complex agents in Copilot Studio. Developers crafting sophisticated, enterprise-grade agents with Azure AI and Teams Toolkit. Each persona requires a different level of oversight. For example, a clinical researcher using SharePoint to build a data retrieval agent may need minimal governance, while a developer building a patient-facing chatbot must adhere to strict data protection and validation protocols. Governance in Action Microsoft provides a layered governance model: Tool Controls: Define what agent creators can do within tools like Copilot Studio and SharePoint. Content Controls: Ensure agents only access data they’re authorized to use, leveraging Microsoft Purview for sensitivity labeling and DLP. Agent Management: Monitor usage, enforce lifecycle policies, and block non-compliant agents via the Microsoft 365 Admin Center. This framework allows organizations to empower innovation while maintaining control—critical in environments where patient safety and regulatory compliance are non-negotiable. The Business Case for Governance For HLS organizations, agent governance delivers tangible benefits: Reduced compliance risk through proactive policy enforcement. Improved operational efficiency by enabling safe automation. Greater trust from patients, regulators, and internal stakeholders. In short, governance is the foundation that allows agents to scale safely and sustainably.
