Extend clinical workflows with Dragon Copilot AI Apps and Agents in Microsoft Marketplace
GA ANNOUNCEMENT Today, we're excited to announce the General Availability (GA) of Dragon Copilot AI Apps and Agents for physician workflows, a major milestone in building an open and trusted healthcare AI ecosystem. Healthcare organizations can now discover, build, deploy, and manage trusted AI-powered applications and agents that support specialized clinical and operational capabilities through Dragon Copilot. At the same time, partners can use self-service experiences to build, validate, certify, publish, and manage solutions that integrate directly into clinician workflows. Together, these capabilities bring healthcare organizations, clinicians, and partners onto a common platform designed to accelerate innovation while maintaining the trust, transparency, and governance required in healthcare. Why it matters to clinicians Clinicians need relevant insights delivered in the flow of care—not in another disconnected tool. Dragon Copilot AI Apps and Agents can surface specialized intelligence within existing workflows to support coding and documentation, medical decision-making, risk adjustment, prior authorization, diagnostic support, behavioral health screening, and preventive care. Dragon Copilot AI Apps and Agents for physicians provide capabilities for use cases such as: Coding and documentation optimization (Regard, RhythmX AI) Risk adjustment (RAAPID Inc) Prior authorization (Humata Health, Genzeon) Evidence-Based Hyper-Personalized Care Guidance (Atropos Health) Diagnostic support (Coming soon!) Cognitive & behavioral health screening (Canary Speech) Care Gaps & Preventive Health Reminders (Coming soon!) Clinician coaching (Whetstone Health) By bringing the right information into the physician’s existing workflow and using patient data from the EHR and the Dragon ambient context (audio, transcript and clinical note) to make insights more relevant, these capabilities can reduce context switching, streamline administrative work, and help physicians stay focused on patient care while maintaining clinical judgment and control workflows. Why it matters to healthcare organizations Healthcare organizations need a scalable way to expand the value of Dragon Copilot while maintaining enterprise governance. AI Apps and Agents provide access to a growing portfolio of specialized capabilities through a common platform, helping organizations increase return on their Dragon Copilot investment without adding fragmented solutions. Dragon Copilot AI Apps and Agents Helps increase ROI by extending Dragon Copilot across more clinical and operational use cases, seamlessly integrated into workflow Simplify solution discovery and purchase through Microsoft Marketplace Centralize deployment, policy, and lifecycle governance through the Dragon Admin Center Maintain clinician oversight and organizational standards for security, privacy, compliance, and responsible AI Reduce implementation burden with streamlined experiences that require minimal IT lift Build and deploy custom built apps/agents integrated with Dragon Copilot This creates a governed, scalable path to adopt new capabilities faster, making it easier to evaluate solutions, deploy them across teams, manage them consistently, and measure value without unnecessary operational complexity. Why it matters to partners Healthcare AI companies often have valuable, specialized capabilities but face significant complexity bringing them into clinical workflows and deploying them consistently across healthcare organizations. Dragon Copilot AI Apps and Agents give partners a standardized extensibility model for embedding their innovations where clinicians already work. Access ambient, patient and encounter context to deliver more relevant, timely experiences Integrate into the Dragon Copilot experience without building and maintaining bespoke workflow integrations for every customer Use self-service onboarding, technical validation, and applicable compliance certification to accelerate readiness Reach healthcare customers through Microsoft Marketplace and scale deployment through a trusted enterprise platform Build customer confidence with transparent solution information and consistent governance expectations By reducing the friction of workflow integration, distribution, and enterprise deployment, the platform helps partners focus on differentiated clinical value while expanding their reach at scale. Creating a trusted healthcare AI ecosystem Dragon Copilot connects specialized partner capabilities with clinical workflows through a trusted & scalable ecosystem. Streamlined self-service onboarding, package validation, compliance certification, and help partner innovations reach the market faster while giving healthcare organizations confidence to adopt and manage these AI solutions responsibly. “Healthcare moves at the speed of trust. You look at Microsoft and Dragon Copilot, we absolutely trust the combination of Microsoft and Dragon Copilot for industry leading innovation, highly trusted, responsible AI, and delivery at scale, and also the reach with the number of clinicians that Dragon Copilot has. And if you combine all these elements of innovation, your reach, track record, and the trust, that's a perfect recipe for partnership.” — Deepthi Bathina, CEO and Founder, GW RhythmX "I use Dragon Copilot in clinic every day. The extension program let me design, build, and implement the tool I wanted to make me a better clinician. It's never been this easy to be both the user and the builder." — Aaron Reinke, MD, Family Physician and Founder, Whetstone Health Explore the ecosystem For healthcare organizations – discover and deploy Explore how Dragon Copilot AI Apps and Agents on Microsoft Marketplace can deliver specialized AI solutions tailored to your clinical and operational needs through Dragon Copilot. Discover new capabilities, purchaseand deploytrusted solutions, and deliver more value to clinicians without disrupting existing workflows in Dragon Copilot, learn more here. For partners – build and publish Bring your differentiated expertise directly into Physician workflows by creating and publishing AI Apps and Agents that help healthcare organizations address high-value clinical and operational needs. Learn how to build and publish applications and agents Review our open-source code samples and start building today with Dragon Copilot’s extensibility framework and reach healthcare customers through Microsoft Marketplace. Publishyour Dragon offer on Microsoft on Marketplace Looking ahead Dragon Copilot is becoming a trusted destination for specialized healthcare AI—bringing partner innovation directly into familiar clinician workflows. As the ecosystem grows, we will expand support for Radiology and Nursing Apps and Agents, addressing the distinct role-based needs of multiple clinical personas. We will also evolve the end-user experience to be more adaptable, enabling chat-based experiences, richer interactivity, and new ways for clinicians to engage with partner capabilities in the flow of work. Streamlined self-service, transparent governance, and Marketplace distribution will help partners scale secure solutions while giving healthcare organizations confidence to adopt them. Together, we’re helping clinicians spend less time on administration and access the right intelligence at the right moment in care.
Fabric Data Agents can choose Query Language based on Context
What if you could combine decades of historical analysis with live, real-time data in one seamless AI chat experience? Traditionally, analyzing large volumes of past data (Analytics / Business Intelligence) has been a separate architecture, or has at least required separate toolsets, from monitoring what’s happening right now (real-time, IoT, etc). With Fabric Data Agents, analytics for large volumes of historical data can be accessible with real-time data via a single AI query endpoint. Analytics can be used to gain understanding from historical data, and findings can be put into action for real-time scenarios, all through a single interface for the end users. Figure 1.0 – Fabric Data Agent can use different query languages for optimal performance with a lambda-style RTI architecture on Fabric Many of the demos I’ve seen for Fabric Data Agents will highlight the capability to connect to different types of queries and sources via a single endpoint such as Lakehouses (SQL), Warehouses (SQL), Semantic Models (DAX), Eventhouses (Kusto), and Ontologies. What I have not seen frequently discussed is adding different query engines on the same data for the purpose of optimizing query performance based upon the context of the query and the latency of the data, as per Figure 1.0 above. To be clear, I am not advocating duplication of data for the purpose of query performance. Rather, this architecture would enable Fabric Data Agents to choose the best query language for the context of the query. Hence the acronym I created for this blog article “NoDAX” which stands for “Not Only DAX.” NoSQL “Not Only SQL” is a real term referring to non-relational database systems designed for flexible schemas, horizontal scaling, and high-performance access to large, distributed datasets. NoDAX exists only within this blog post, and represents the availability of multiple query languages within a single Fabric Data Agent. The best query language can be used based on the context of the question and query. Not only DAX but also SQL, Kusto, and Ontologies can be queried to generate the most efficient contextual query. Figure 1.1 – Explaining the NoDAX acronym created for this article Why does this use case matter? The ability to query both the past and the present in one interface isn’t just a novel technical capability. Many analytic solutions can benefit from a pattern of [analysis + action]. Analytics by itself is great at understanding the past. We can look at years of historical data to figure out patterns, drivers of performance, and what went right or wrong. Historical context is incredibly valuable, but understanding the past doesn’t improve outcomes in the future. The real value comes when we connect findings from the past to decisions we make right now and in the future. If you discover historical data patterns that lead to an inventory shortage, a fraud event, or a patient risk score increasing, you can apply that insight to the latest operational data and act on ongoing workflows. Analytics and action have to reinforce each other. Analytics without action doesn’t create value. But action without understanding can easily make things worse. I once had a veteran analytics manager say to me “Without carefully considered and vetted KPIs you are flying blind. But be careful, because a KPI without proper context and understanding will quickly become a blunt object that an empty suit uses to whack somebody over the head.” Figure 1.2 – The purpose of this architecture is to learn from the past to improve the present and future Different Query Languages without data duplication A Fabric real-time architecture can be part of a design pattern that is similar to if not a version of a Lambda architecture. With a Lambda architecture, hot path data is available for real-time alerting and analytics while cold path data is stored for deep and complete historical analytics and data science. Figure 1.3 – NoDAX architecture can query a lambda-style architecture via multiple query endpoints Per the diagram above, real-time data is available in Fabric ASAP and cycled through an Eventhouse. Historical data can either be batched into a Fabric Lakehouse / Warehouse or copied over from the Eventhouse. A Fabric Data Agent can then generate Kusto queries against the Eventhouse, SQL queries against the Lakehouse / Warehouse, or DAX queries against the Warehouse / Lakehouse via the Direct Lake Semantic Model. DAX is often the best query language for data having deep history with complext analytic logic. SQL can be the best query language for retrieving historical row-level information from a robust relational database. Kusto can be used to query what’s happening right now via a real-time Eventhouse. Lambda architectures have been around for years, so why is this architecture a new option? Past lambda architectures would have hot path data available in a streaming toolset such as Azure Eventhub, and then store historical cold path data in a tool such as Azure Data Lake. Hot path alerting and reporting was usually disparate from historical cold path analytics. Per the diagram 3.4 below, with Microsoft Fabric, you can now: implement both the hot and cold path in a single Fabric environment (Eventstream, Eventhouse, Lakehouse / Warehouse). Ontologies are also an option. query the hot and cold path data via a single agentic endpoint using a Fabric Data agent Query either the hot or cold path using the optimal query language for the context of the question (DAX, SQL, Kusto, Ontologies) Figure 1.4 – Fabric not only unifies components of lambda-style architecture, but Data Agent also unites the query endpoints for AI unification Example use case for Healthcare Here’s an example of a Healthcare use case: A user might ask a question “Show me the percentage of patients who had their pain scores checked every hour for gall bladder removals on floor 5 over the last 3 years.” This will ideally filter three years worth of data for patients with specific procedure codes, filtered for specific rooms, and calculate the pain score check compliance for those visits. This query is ideal for the DAX language with a Semantic Model. The user might then want to see details for a specific time period, and ask “Show me the pain score results for patients who had their gall bladder removed on July 3 2024 on floor 5.” A SQL query might be the best option here against the Fabric Warehouse or Lakehouse, since SQL is better than DAX at retrieving row-level information. Then the user might want to know what is happening today. “Show me the pain score checks for inpatients right now who had their gall bladder removed on floor 5.” The Kusto language can retrieve the information that streams into a Fabric Eventhouse via an Eventstream. Based on the findings, the user may take an action. With the example above, a user was able to query deep history with analytic logic, retrieve historical row-level information from a robust relational database, and then view what’s happening right now for those patients. Action can then be taken in the here and now. Here are some additional use cases for Finance, Supply Chain, and Manufacturing in addition to Healthcare: Figure 1.5 – Industry use cases for Fabric Data Agent with multiple endpoints Video Summary Below is my video summary and demo of the Fabric Data Agent NoDAX architecture: Configure Fabric Data Agent for NoDAX query patterns When more than one source is added to a Fabric Data Agent, by default the source used for a specific query will be chosen based on interpreting available metadata. The Data Agents have a field called "Agent instructions" which can be used to provide detailed instructions about choosing the right source for the right question. Here’s a screenshot of the Agent instructions: Figure 1.6 – Agent instructions will guide the Fabric Data Agent to the best query endpoint I would recommend extensive unit testing and iterative improvements to the Agent instructions based upon your own data and use cases. Here’s a few examples that worked for my initial testing. I would recommend much more robust and carefully designed prompts for a production solution, but this is a baseline of an approach I found to work based on the demo in the video above: The KQL database named SeattleFireEventHouse is a live stream of 911 calls to the fire department in the city of Seattle. Whenever someone asks for “most recent” or “newest” or “latest” use SeattleFireEventHouse The lakehouse SeattleFireLakehouse should be queried with a SQL statement when someone asks for a list of incidents before the year 2026. Use SQL to retrieve row level requests for historical data. The semantic model SeattleFireSemantic Model should be queried when questions ask about historical analytic trends such as call volume averages, Year over year changes, and queries that aggregate data for analytic queries.Webinar Series for Microsoft AI Agents
Join us for an exciting and insightful webinar series where we delve into the revolutionary world of Microsoft Copilot Agents in SharePoint, Agent builder, Copilot Studio and Azure AI Foundry! Discover how the integration of AI and intelligent agents is set to transform the future of business processes, making them more efficient, intelligent, and adaptive. In this webinar series, we will explore: The Power of Microsoft Copilot Agents: Learn how these advanced AI-driven agents can assist you in automating routine tasks, providing intelligent insights, and enhancing collaboration within your organization. Seamless Integration with Microsoft Graph: See how Copilot Agents work seamlessly with Microsoft Graph data to improve information retrieval, boost productivity, and automate mundane tasks. Real-World Applications: See real-world examples of how businesses are leveraging Copilot Agents to drive innovation and achieve their goals. Future Trends and Innovations: Get a glimpse into the future of AI in business processes and how it will continue to evolve and shape the way we work. Join us for the Webinars every week, at 11:30am PST/1:30pm CST/2:30 EST: (Click on the webinar name to join the live meeting on the actual date/time or use the .ics file at the bottom of the page to save the date on your calendar) April 2nd: Agents with SharePoint - Watch this Webinar recording for an overview of SharePoint Agents and its key capabilities to enable your organization with powerful Agents helping you search for information within seconds in large SharePoint libraries with 100's of documents. April 9th: Agents with Agent Builder - Watch this Webinar recording for an overview of Agent Builder and its key capabilities to enable organization with "No code" Agents that can be created by any business user within minutes. April 16th: Agents with Copilot Studio- Join us for an overview of Copilot Studio and its key capabilities to enable organization with "Low code" Agents that can help create efficiency with existing business processes. We will feature a few real-life demo examples and answer any questions. April 24th: Agents with Azure AI Foundry - Join us for an overview of Azure AI Foundry and its key capabilities to enable your organization with AI Agents. We will feature a demo of AI agents for prior authorization and provide resources to accelerate your next project. Don't miss this opportunity to stay ahead of the curve and unlock the full potential of AI and Copilot Agents in your organization. Register now and be part of the future of business transformation! Speakers: Jaspreet Dhamija, Sr. MW Copilot Specialist - Linkedin Michael Gannotti, Principal MW Copilot Specialist - LinkedIn Melissa Nelli, Sr. Biz Apps Technical Specialist - LinkedIn Matthew Anderson, Director Azure Apps - LinkedIn Marcin Jimenez, Sr. Cloud Solution Architect - LinkedIn Thank you!
