A specialty-specific approach with Microsoft Dragon Copilot
Clinicians are at the heart of patient care, and the documentation they create shapes how that care is delivered, interpreted, and continued. Nearly 70% of the global medical workforce—around 9 million practitioners, according to recent World Health Organization data—are specialists whose work spans a wide range of disciplines and care settings. As these specialties evolve, so do their documentation needs to ensure the highest quality and accurate care. Each specialty brings its own documentation requirements. Orthopedics relies heavily on imaging reports, physical exam findings, and procedural notes. Preventive Medicine, on the other hand, focuses on understanding the breadth of the patient’s conditions and proactive measures to promote health. Across care settings—from outpatient clinics to emergency departments to inpatient units—documentation also varies in its requirements. Accurate, specialty-specific documentation supports not only improved patient outcomes but also the broader healthcare ecosystem—from ensuring appropriate reimbursement to enabling clinical research and the development of more targeted treatments. When designed to meet the needs of specialists, documentation becomes more than a requirement—it becomes a tool for delivering better care. Purpose-built with clinicians Microsoft Dragon Copilot enhances the clinician experience by streamlining the creation of medical notes tailored to each specialty’s unique requirements. Powered by advanced natural language processing, Dragon Copilot recognizes and adapts to the specific needs of disparate medical fields. This enables clinicians to focus more on patient care and less on administrative work, enhancing both efficiency and satisfaction. Built for continuous learning and adaptation, Dragon Copilot helps specialists keep pace with the evolving clinical guidelines, medical standards, and billing requirements with Microsoft’s dedicated team of medical professionals including MD’s (Doctor of Medicine), RN’s (Registered Nurse), and APP’s (Advanced Practice Provider). In a field shaped by constant change, this agility helps to ensure documentation stays accurate and relevant. At the core of this innovation is Microsoft’s deep, daily engagement with clinicians using Dragon Copilot. Through a diverse network—physicians, advanced care practitioners, coders, and other healthcare professionals—Microsoft works directly with those on the front line of care. This network, and other early access participants, work alongside Microsoft’s in-house clinical experts and researchers to co-design, test, and refine Dragon Copilot. This close partnership brings real-world insight into the development process, helping us ensure Dragon Copilot aligns with the practical, specialty-specific needs of medical professionals. By embedding clinical knowledge and clinician feedback into each iteration, we deliver a solution that is not only clinically accurate but also intuitive and trusted. This is about more than building a better product experience—it is about fostering trust and ownership among clinicians with technology that fits naturally into their everyday practice, supports their expertise, and helps them deliver the highest quality care. The power of a specialty-optimized approach Dragon Copilot is built on a trusted core medical model, fine-tuned on millions of real-world patient encounters. From this core medical foundation, the model is then adapted and optimized for each specialty—integrating clinical experts’ knowledge and research, national association recommendations, and feedback from clinicians. This layered approach supports outputs that are not only medically accurate but also aligned with the documentation standards and workflows clinicians depend on in their daily practice. The system evolves with changes in clinical guidelines and inputs from practicing specialists. This iterative process keeps Dragon Copilot current, relevant, and reflective of both specialty-specific requirements and real-world practice. By aligning note content with specialty-specific standards, Dragon Copilot helps reduce cognitive load, minimize documentation errors, and shorten the time needed to complete and sign notes. The result is a more efficient workflow that enhances both the quality of care and patient data processing. “By teaming up with specialty providers, Microsoft has elevated the quality and accuracy of notes—making my documentation clearer, more robust yet concise, and significantly improving readability for both patients and fellow providers. Additionally, this update also greatly improved the capture of physical exam findings.” Eric Alford, M.D. Baylor Scott & White Health Consider ophthalmology: clinical guidelines in this specialty require documentation of complex decision-making—such as discussing lens implant options in a way that balances clinical appropriateness with individual patient preferences. Dragon Copilot helps to capture both, generating documentation that is structured and personalized to each unique patient encounter. Or take psychiatry: the mental status exam is a crucial component of the evaluation for informed decisions about the patient's treatment. Dragon Copilot supports by capturing this comprehensive assessment essential for tracking the patient's progress over time. Customization that reflects the art of medicine Specialty-specific notes are only part of the solution—clinician satisfaction and adoption rely on meaningful customization. Documentation is personal, and no two clinicians document the same way. Microsoft Dragon Copilot is designed with that in mind, offering customizable templates and flexible styles that align with individual preferences and workflows. “I think the potential of Dragon Copilot is going to be even greater as we start to bring in local vernacular, and the ability to help each doctor tune their note to their appropriate desires because one person's note that is too brief is another one that's too long for someone else”- R. Hal Baker, MD, SVP and CIO, Wellspan Health This level of personalization preserves each clinician’s unique voice while enhancing the accuracy, completeness, and efficiency of documentation. By bridging standardized requirements with specialty-specific content and individual style, Dragon Copilot supports a more seamless and effective documentation experience. Tailoring technology to meet the diverse needs of clinicians not only enhances satisfaction and adoption but contributes to better care delivery across the healthcare system. Trustworthy AI by design Microsoft Dragon Copilot is built on a secure data estate and incorporates healthcare-adapted clinical, chat and compliance safeguards for accurate and safe AI outputs. Dragon Copilot also aligns to Microsoft’s responsible AI principles to help guide AI development and use — transparency, reliability and safety, fairness, inclusiveness, accountability, privacy, and security. We invest in technical performance through regular assessments, building trust with medical professionals. This process looks for potential biases and errors, enabling timely corrections and continuous improvements across specialties. With a strong focus on inclusiveness, Dragon Copilot supports a wide range of medical practices and specialties, reflecting the diverse needs of clinicians and patients. By upholding these principles, Microsoft drives innovation while helping to safeguard the interests of both patients and healthcare providers. These commitments set a high standard for trustworthy AI in healthcare. Looking ahead Clinical documentation should tell the complete story of a patient’s care—clearly and comprehensively—for the stakeholders involved. We are excited to keep innovating around specialty-specific clinical documentation and beyond—and we want you to be part of it. Your feedback fuels our progress. Together, we can improve clinician well-being and keep the focus where it belongs: on patient care. Learn more Watch an on-demand demo Take a deeper look at Dragon Copilot Explore the latest with our new health AI models and integrations
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 LearnManaging data sharing and access in healthcare systems
I am looking for general guidance on how healthcare teams manage data sharing and user access across different systems. I am interested in understanding common approaches for keeping data secure while still allowing the right staff to access what they need. This is more about best practices and real-world experience rather than a specific product issue. Any insights from similar healthcare environments would be helpful.Are Denial Management Solutions Ready for 2026 Healthcare Challenges?
As healthcare reimbursement continues to evolve, denial management is becoming a growing concern for providers, billing teams, and revenue cycle leaders. With stricter payer policies, higher claim volumes, and tighter appeal timelines expected in 2026, many organizations are re-evaluating how prepared they truly are. One of the biggest challenges remains preventable denials caused by eligibility errors, coding issues, and incomplete documentation. While manual reviews still exist, they often struggle to keep pace with changing payer requirements. This is where modern denial management solutions are gaining attention. Looking ahead, a few key questions stand out: Are current denial workflows proactive or still largely reactive? Do teams have real-time visibility into denial trends and appeal deadlines? How effectively are analytics being used to reduce repeat denials? Can automation support faster, more accurate claims denial management without increasing administrative burden? In 2026, success in denial management may depend less on fixing denials after they occur and more on preventing them through smarter systems, better data insights, and streamlined appeals processes. Organizations adopting flexible denial management software seem better positioned to adapt to payer changes and protect revenue. I’m curious to hear from others in the community: What changes are you seeing in denial management today, and how are you preparing for what’s coming next?Healthcare 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, 2023Agentic AI in Healthcare
Healthcare organizations are at a crossroads where rising patient loads, complex data, and administrative burdens demand new solutions. Agentic AI – AI systems capable of autonomous action – is emerging as a catalyst for transformation, promising to act not just as tools but as collaborative digital team members. Microsoft’s ecosystem of AI technologies provides a robust foundation to harness agentic AI in healthcare. This report offers a comprehensive overview of agentic AI, distinguishes it from traditional AI, and explores its role in clinical workflows, administrative efficiency, patient engagement, and data governance. It also examines how Microsoft’s offerings (Microsoft 365 Copilot, Azure Health Data Services, Microsoft Fabric, Copilot Studio, and more) enable these advances responsibly and in compliance with healthcare regulations like HIPAA.
Introducing the Dragon Copilot experience for nurses
Nursing is at a crossroads. Rising patient acuity, persistent staffing shortages, and high turnover have pushed health systems to draw a hard line against digital workflows that distract nurses from care delivery. The solution isn’t layering AI onto outdated workflows—it’s reinventing how nurses work, making technology invisible and care effortless. Just as the shift from paper to digital introduced a generational opportunity for redesign, bringing ambient AI to nursing introduces a rare window for fundamental transformation: one that aligns technology with high value care delivery and gives nurses the same efficiency gains physicians have realized. Microsoft Dragon Copilot brings that vision to life—empowering nurses to “care out loud” and capturing documentation seamlessly while technology fades into the background. Nurses aren’t just the most trusted professionals; they deliver most of the direct patient care. Every interaction is a chance to engage, apply critical thinking, and improve outcomes. By reducing time spent navigating screens and freeing nurses to focus on presence and compassion, Dragon Copilot unlocks their full potential—creating powerful opportunities to elevate care at the bedside. How Dragon Copilot supports nursing staff Dragon Copilot turns real-time bedside conversations and observations into the clinical documentation nurses need—without pulling focus from the patient. Nurses speak and the interaction is ambiently recorded on a mobile EHR app; Dragon Copilot structures, summarizes, and surfaces what matters next, reducing clicks and cognitive load. What it delivers: Flowsheet documentation drafted from captured interactions and mapped to each hospital’s unique schema without needing to specify the template up front, making it easy for nurses to review in workflow, edit as needed, and file into the EHR. Nurse notes for incidents and status changes, ready to copy into the EHR or communication systems, saves nurses time on narrative documentation. Concise summaries of major findings and next steps to keep nurses organized amid constant interruptions. Beyond documentation: Query transcripts for details not typically captured in EHR documentation. Ask clinical questions and get answers from organization-trusted sources like FDA and MedlinePlus—supporting patient education at the bedside. Integrating into complex nursing workflows Transforming nursing workflow isn’t just about adding new technology—it’s about addressing the realities of nursing practice. That means supporting diverse workflows and preferences, managing the complexity of structured flowsheet documentation, and navigating the human side of change for an already strained workforce. Real impact requires more than layering large language models onto existing applications or repurposing physician-focused solutions—it demands a transformation that reduces administrative burden and aligns with how nurses deliver their best care. Here’s how we’ve met these challenges—and continue to refine the experience: Flexible interaction styles Every nurse works differently, and patient needs vary. Dragon Copilot adapts seamlessly by capturing documentation during patient conversations or when nurses record thoughts outside the room—without requiring extra steps or mode selection. Through the Epic Rover mobile app, Dragon Copilot supports: Conversational recordings captured naturally during patient interactions Stream-of-consciousness voice recordings captured outside the room This flexibility supports nurses in capturing observations in the way that fits their workflow without adding friction. Advanced AI purpose-built for nursing Dragon Copilot converts even ambiguous voice recordings into accurate, structured flowsheet entries--correctly mapping to the appropriate row and value out of thousands of possibilities. Our AI is adapted to nursing vocabulary and optimized for speed and accuracy. The result? Performance far beyond what generic frontier models can deliver. Seamless integration with existing flowsheet templates No need to migrate to a standard flowsheet format. Dragon Copilot works with your organization’s existing flowsheet schema. In addition, during the setup process, the solution: Automatically surfaces flowsheet optimization opportunities—such as duplicate rows—within the Dragon Admin Center, helping administrators enhance usability and improve AI accuracy during implementation. Enables rapid administrator validation of AI outputs, before go-live or after template change, so potential problems are resolved proactively without disrupting workflows In-app support for documentation integrity For every AI-extracted flowsheet value, nurses can: See the exact transcript section that supports it Access organization-configured guides during recording Review a preview of AI results upon pausing, with gaps highlighted, so they can complete documentation before sending values to the EHR Nurses stay in control Only AI-generated flowsheet values reviewed and accepted by the nurse become part of the medical record, giving nurses control of their EHR documentation. Scalable deployment through automation The Dragon admin center now supports the Dragon Copilot experience for nurses, providing a unified platform to manage deployments, configure product settings, create EHR instances, and access analytics for adoption tracking and change management. Administrators can define which flowsheet templates and rows are ambiently enabled, as well as manage user access to them. The platform also includes licensing and organization lifecycle management, giving organizations a comprehensive oversight of their Dragon Copilot implementation Transformation services Transformation isn’t just about technology—it’s about enabling change. Our services approach supports organizations in implementing change management practices proven by early customers to drive results. Designing with nurses at the helm Our collaboration with nurse leaders and frontline teams, paired with continuous investment in nurse-driven design, remains central to our approach to delivering meaningful transformation. From innovation to impact The Dragon Copilot experience for nurses is part of our Dragon Copilot AI clinical assistant—developed based on decades of clinical expertise with Microsoft’s scale, security, and relentless commitment to innovation. Microsoft was first to bring ambient solutions to physicians, and now we’re delivering the first ambient solution purpose-built for nursing—shaped through years of collaboration with nurse leaders and frontline teams. Our mission is simple yet bold: empower nurses to deliver exceptional care by reducing administrative burden and honoring human connection at the heart of care. Fulfilling this mission demands collaboration, relentless innovation, and a shared commitment to getting it right—because nurses deserve nothing less. Join us on this journey in shaping the future of nursing. Learn more at aka.ms/dragoncopilot andMicrosoft Learn.Ushering in the Next Era of Cloud-Native AI Capabilities for Radiology
Introducing Dragon Copilot, your AI companion for PowerScribe One For radiologists, the reporting workflow of the future is here. At RSNA 2025, in Chicago, we’re showcasing Dragon Copilot, a cloud-native companion for PowerScribe One. Currently in preview, Dragon Copilot builds on the trusted capabilities of PowerScribe One to accelerate innovation and modernize reporting workflows while unlocking extensibility for radiology teams and partners. Why we built it: Technical drivers for a new era With growing demand for imaging services coupled with a workforce shortage, healthcare professionals face increased workloads and burnout while patients experience greater wait times. With our breadth of healthcare industry experience combined with our AI expertise and development at Microsoft, we immediately understood how we could help address these challenges. For radiologists, we sought to plugin into existing reporting workflows with rapid innovation, scalable AI, and open extensibility. How we built it: Modern architecture and extensibility By delivering Dragon Copilot as cloud-native solution built on Azure, we can enable new services globally. We apply the full capabilities of Azure for compute, storage, and security for high availability and compliance. Our modular architecture enables fast delivery of new features with APIs at the core to allow seamless integration, extensibility, and partner innovation. To imbue the workflow with AI through our platform, we harness the latest generative, multimodal, and agentic AI (both internal and through our partners) to support clinical reporting, workflow automation, and decision support. Key architectural highlights: AI services: Integrated large language models (LLMs) and vision-language models (VLMs) for multimodal data processing. API-first design: RESTful APIs expose core functions (draft report content generation, prior summarization, quality checks and chat) enabling partners and developers to build extensions and custom workflows. Extensibility framework: Open platform for 1st- and 3rd-party extensions, supporting everything from custom AI models to workflow agents. Inside the innovation Dragon Copilot alongside PowerScribe provides a unified AI experience. Radiologists can take advantage of the latest AI advancements without disruption to their workflows. They do not need another widget taking up room on their desktop. Instead, they need AI that fits seamlessly into existing workflows connecting their data to the cloud. Our cloud-first approach brings increased reliability, stability, and performance to a radiologists’ workflow. I’m thrilled to highlight the key capabilities of this dynamic duo: PowerScribe One with Dragon Copilot. Prior report summary: Automatically summarizes relevant prior reports, surfacing key findings, and context for the current study. AI-generated draft reports and quality checks: The most transformative aspect of Dragon Copilot is its open, extensible architecture for AI integration. We don’t limit radiology teams to a single set of AI tools. We enable seamless plug-ins for AI apps & agents from both Microsoft and our growing ecosystem of 3rd-parties. We provide a single surface for all your AI needs. This approach will enable radiology departments to discover, acquire, & deploy new AI-powered extensions. We’re enthusiastic about embarking on this journey with partners. We're also excited about collaborations with developers and academic innovators to bring their own AI models and services directly into the Dragon Copilot experience. Integrated chat experience with credible knowledge sources and medical safeguards: This chat interface connects radiologists to credible, clinically validated sources from Radiopedia and Radiology Assistant. It enables agentic orchestration and safeguards provided by Azure's Healthcare Agent Services for PHI and clinical accuracy. In the future, we expect to have a variety of other sources for radiology customers to choose from as well as the ability for organizations to add their own approved policies and protocols. This chat is designed to route questions to the right agent, provide evidence for claims, and filter responses for clinical validity. Over time, it will include extensions with custom agents powered by Copilot Studio. Help us shape what’s next As we continue to evolve Dragon Copilot alongside PowerScribe One, we invite innovators, developer partners, and academics to join us in shaping the future of radiology workflow. Dragon Copilot is more than a product; it’s a solution for rapid, responsible innovation in radiology. By combining cloud-native architecture, advanced AI capabilities, and open extensibility, we’re enabling radiology teams to work smarter, faster, and with greater confidence. Ready to see it in action? Visit us at RSNA 2025 (November 30–December 4), booth #1311 South Hall. Or contact our team to join the journey.
