Microsoft is at the forefront of this transformation, dedicated to developing and implementing responsible AI technologies. By fostering innovation and collaboration through Microsoft Cloud for Healthcare, we continue to reinforce how responsible AI can enhance healthcare delivery and improve outcomes for patients worldwide. Building on this commitment, we’re excited to introduce new features in our AI healthcare portfolio that will further drive industry efficiencies, and better patient outcomes. 

Advanced AI models and integrations for healthcare 

As medical technology advances, improvements in medical imaging are critical for better diagnosis of disease and improved patient care. In 2024, we announced the launch of healthcare AI models, a collection of cutting-edge multimodal medical imaging foundation models available in Azure AI Foundry. Designed for precise image segmentation, MedImageParse 2D model covers many imaging modalities, including x-rays, CTs, MRIs, ultrasounds, dermatology images, and pathology slides. It can be fine-tuned for specific applications such as tumor segmentation or organ delineation, allowing developers to test and validate the ability to leverage AI for highly targeted cancer and other disease detection, diagnostics, and treatment planning.  

Today, we’re excited to share the MedImageParse model is now optimized for 3D medical imaging data. MedImageParse 3D can handle complex 3D datasets produced by advanced imaging, such as MRI and CT scans, providing a more comprehensive view into patients’ conditions. The enhanced ability to visualize and interpret anatomical abnormalities and structures provides for much more accurate diagnosis that may have been missed by 2D analysis. MedImageParse can also support healthcare researchers with comprehensive image analysis and a more streamlined workflow for radiologists, improving overall efficiency and reducing human error. MedImageParse 3D can soon be found in the Azure AI Foundry model catalog.  

In partnership with Microsoft Research, the Microsoft Health and Life Sciences model catalog will also feature several new and updated multimodal medical foundation models including TamGen for protein design, Hist-ai for pathology, and ECG-FM for electrocardiogram (ECG) analysis. 

Leveraging multimodal AI for improved health insights 

Today, we are excited to announce new functionality in healthcare data solutions that allows customers to orchestrate multimodal AI insights directly into Microsoft Fabric. Now in public preview, orchestrating multiple modalities (e.g., text, image, audio, video, and other forms of sensory input) of health data within Fabric allows healthcare organizations to generate a robust set of insights that help faster decision-making and improved patient outcomes. 

Customers can leverage Fabric to orchestrate multimodal AI insights by connecting their healthcare data to a variety of AI services and models. These AI-generated insights are then integrated back into the healthcare data estate to enable various use cases like creating targeted outreach and care plans by enriching clinical conversations with social determinants of health (SDOH) and sentiments. Another possible scenario is deriving quick insights and disease progression trends for clinical research by creating image segmentations and combining it with imaging metadata through Microsoft Power BI reports. 

The orchestration capability includes five out-of-the-box examples to help customers connect and integrate to AI models: 

1. Text analytics for health in Azure AI Language to extract medical entities from unstructured data such as diagnoses and medications, and the relations between entities.  

2. MedImageInsight AI model in Azure AI Foundry to generate medical image embeddings from imaging data.  

3. MedImageParse AI model in Azure AI Foundry enables segmentation, detection, and recognition from imaging data across numerous object types and imaging modalities.  

4. Sentiment analysis with Azure OpenAI Service to score sentiment for categories such as doctors’ services, staff services, facilities, and cost from conversational data. 

5. SDOH extraction with Azure OpenAI to extract social determinants of health data from conversational data based on the Centers for Medicare and Medicaid Services’ defined categories. 

To further enhance data accessibility, we’re pleased to share the general availability of additional functionality that enhances the existing capabilities within our healthcare data solutions offering. These include:   

• Centers for Medicare and Medicaid Services Claim and Claim Line Feed Files (CMS CCLF) claims data ingestion: Customers can bring CMS CCLF claims data into Fabric where they can assess the effectiveness of their care management programs, monitor population-level trends and usage as well as measure their performance against benchmarks to reduce overall claim expenses and improve patient care.  

• Social determinants of health public datasets transformation: Now healthcare organizations can ingest, persist, harmonize, and use SDOH public datasets to identify risks and health-related social needs, ultimately creating more equitable healthcare for all patients and communities. 

• Care management analytics: By using unified healthcare data and care management analytical templates, healthcare providers can enhance patient care by identifying high-risk individuals, optimizing treatment plans, and improving care coordination. This empowers organizations to deliver personalized, efficient, and proactive care.  

• Patient outreach analytics: Healthcare providers communicate with their patients more effectively by orchestrating personalized journeys across patient touchpoints. This capability simplifies the process by bringing data from different sources into Fabric, transforming it into an industry data model, and serving it to a Power BI report. 

• Dragon Copilot ambient AI integration: Dragon Copilot’s AI-powered, voice-enabled capabilities reduce the administrative workload of clinicians by automatically documenting patient encounters. With integration into Fabric, this new capability brings conversational data into Fabric OneLake. This integration enables customers to access, store, and manage the raw data generated. The data is stored in a lakehouse, organized in a hierarchical structure by date, which lets customers view each file and its content. When used in conjunction with healthcare data solutions, customers can combine their conversational data with their clinical data to learn more from patient interactions. 

“There is a lot of unrealized value in patient physician interactions. OSUMC is aiming to leverage conversational data along with multimodal AI insights in healthcare data solutions such as social determinants of health extraction to improve patient outcomes.”  

—Ravi Dyta, Director of IT at Ohio State University Wexner Medical Center

Achieve more with AI you can trust

This week’s Microsoft Cloud for Healthcare announcements underscore our commitment to transforming healthcare through advanced AI models and data integrations. By leveraging these cutting-edge technologies, we’re empowering healthcare organizations to deliver better care, help improve patient outcomes, and drive innovation in the industry. 

Connect with us in the Microsoft booth #2221 at HIMSS 2025 to immerse yourself in the latest advancements in data and AI from Microsoft and our partners.  

• Meet Microsoft Dragon Copilot: Your new AI assistant for clinical workflow

• Learn more about our recent releases in healthcare data solutions in Microsoft Fabric 

• Explore the latest with healthcare AI models

Microsoft Cloud for Healthcare

Transform how your organization uses AI

Discover solutions

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.  

Generative AI does not always provide accurate or complete information. AI outputs do not reflect the opinions of Microsoft. Customers/partners will need to thoroughly test and evaluate whether an AI tool is fit for the intended use and identify and mitigate any risks to end users associated with its use. Customers/partners should thoroughly review the product documentation for each tool. 

The post Leading the charge to transform healthcare with advanced AI  appeared first on Microsoft AI Blogs.

For over two decades, we have consistently delivered front-end speech capabilities that have helped clinicians document billions of patient records and that have become a cornerstone of clinical documentation. Five years ago, we took a significant leap forward by pioneering the ambient AI category in healthcare. Today, we announce the integration of these proven technologies with fine-tuned generative AI, healthcare-adapted safeguards, and new capabilities on a scalable platform. This powerful combination brings unprecedented levels of efficiency and care, offering wide-reaching benefits for all.

Our robust voice solutions have consistently delivered outcomes for clinicians, patients, and healthcare organizations. Outcomes like 5 minutes of time-savings per encounter on average that have enabled 13 additional appointment slots per provider, per month.¹ A 70% improvement in clinician work-life balance and reduction of feelings of burnout and fatigue.¹ And the delivery of better patient experiences, where 93% of patients say their physician is more personable and conversational due to our technology.²

Dragon Copilot builds on this evolution to streamline documentation, surface information, and automate tasks across care settings. It’s an AI extensible workspace that offers a unified experience, integrates with electronic health records (EHRs) such as Epic, and supports clinicians across all stages of their workflow. Part of Microsoft Cloud for Healthcare, it’s built on a secure, modern architecture and can take clinical productivity to new heights while helping boost clinician wellbeing and the patient experience, increasing efficiency, and improving financial impact.

Streamline documentation with new levels of customization

Dragon Copilot uses the latest AI models to help produce accurate documentation efficiently and consistently.

• Create clinical documentation automatically: Captures multiparty, multilingual patient-clinician conversations and orders ambiently during the visit and converts them into high quality, comprehensive, specialty-specific notes allowing clinicians to connect with patients rather than screens.
• No internet? No problem: Recordings are captured and processed once users are reconnected. 
• Produce high quality, customizable documentation: Allows clinicians to customize documentation, save templates, AI prompts, and frequently used text.
• Talk naturally: Provides natural language speech capabilities, dictation at the cursor, custom vocabularies, and intuitive voice correction capabilities across devices.

Surface information without leaving your workflow

From querying notes and getting medical information to receiving encounter recording suggestions, Dragon Copilot gives users access to pertinent information when they need it.

• Query notes: Provides details and answers questions like whether a patient is taking a certain medication, has a relevant family history, or mentioned something specific during the conversation. Copilot uses conversation transcripts and notes to address your requests.
• Get credible medical information: Clinicians can access a broad range of medical information and clinical topics, allowing them to check the latest protocols for managing a condition or check drug interactions, for example. Dragon Copilot uses grounded AI with citations to ensure trust in its responses.
• Receive suggestions: Create more complete notes. Dragon Copilot analyzes the transcript and makes suggestions to help clinicians capture specific information, such as temperature, BMI, and family history.
• Put conversational data to good use: Get insights at scale with Microsoft Fabric and Dragon Copilot. Tap into point-of-care data to better analyze usage and adoption and help improve research, patient care, engagement, outreach, and more.

“Using all these tools together is going to be a great in-room, in-office assistant for taking care of the patient. It’s just remarkable.”

Dr. Lance Owens, Chief Medical Information Officer, University of Michigan Health-West

Automate tasks with a single click

Dragon Copilot helps clinicians automate clinical and non-clinical tasks. From summarizing notes and evidence, to prepping orders and drafting referral letters and after visit summaries, it saves time and increases clinician productivity and efficiency.

• Make orders easy: Automatically capture over a dozen order types during clinician-patient conversations. With supported EHRs, orders are directly entered into the EHR order module. 
• Summarize notes: Get an instant synopsis of each encounter, including key facts and details, streamlining workflow and reducing cognitive load. Dragon Copilot makes it easy to get a quick refresher on the patient before finalizing a note.
• Summarize evidence: Receive more than just linked notes to transcripts. Dragon Copilot curates diagnosis evidence from subjective elements such as symptoms, objective elements such as labs and imaging, and other relevant information shared during the encounter.
• Create referral letters: Have Dragon Copilot quickly draft a referral letter from clinical notes by using the information gathered during an encounter. It automatically extracts key details—including medical history, requested services, and pertinent test or imaging results—and repurposes them for the letter.
• Generate after visit summaries: Dragon Copilot converts clinical documentation from encounter visits into written patient-friendly after-visit summaries, providing an easy reference for key clinical highlights and important directions.

“This is a complete transformation… it’s going to make it easier, more efficient, and help us take better quality care of patients.”

Dr. Anthony Mazzarelli, Co-President and Chief Executive Officer, Cooper University Health Care

True anywhere access

From a full-featured web app with no client installation, to dedicated mobile and desktop apps with added functionality, Dragon Copilot goes wherever you go. And for even greater workflow efficiency, Dragon Copilot is natively embedded in supported EHRs.

Access in-app training and support—whenever and wherever it’s most convenient

Dragon Copilot offers some of the best product support and insights to help healthcare organizations get the most out of their AI investment.

• On-demand training: In-app training videos and on-demand content provide access to training content.
• Integrated live chat and virtual support room: Need help? Dragon Copilot comes with immediate support without leaving the app and a virtual support room staffed by experts.
• Product and AI feedback: In-app feedback and dedicated channels enables clinicians to rate Dragon Copilot and provide feedback that helps improve the product experience, note quality and AI responses.

Our expansive partner ecosystem

Dragon Copilot is bolstered by our collaboration with healthcare industry experts across our global ecosystem of trusted partners. We work with leading independent software vendors (ISVs), system integrators (SIs), and cloud service providers (CSPs) so our customers in every region can access the healthcare solutions and offerings they need.

Dragon Copilot is coming to you

Dragon Copilot will be generally available in the United States and Canada in May 2025, followed by the United Kingdom, Germany, France, and the Netherlands. Microsoft is also committed to bringing a new Dragon experience to other key markets that are using Dragon Medical today.

Built on a foundation of trust

We are dedicated to helping customers use and build AI that is trustworthy, secure, safe, and private. By using the Microsoft Secure Future Initiative, we support the highest standards of security, privacy, and compliance. Our AI aligns with Microsoft’s responsible AI practices and incorporates healthcare-specific clinical, chat, and compliance safeguards to ensure accurate and safe outputs. Additionally, our data is grounded in privacy principles, backed by transparent policies, and protected by rigorous safeguards.

“Microsoft has invested a lot in security. It gives me peace of mind that Dragon Copilot is part of the whole Microsoft suite.”

Novlet Mattis, Senior Vice President, Chief Digital and Information Officer, Orlando Health

Ready to take the next step?

• Visit us at HIMSS Booth #2221.
• Not attending HIMSS 2025? Join us for our livestream event.
• Learn more on Dragon Copilot.
• Explore the latest with our new healthcare AI models.

Dragon Copilot

A new groundbreaking solution that transforms the way clinicians work

Discover more

¹ Microsoft survey of 879 clinicians across 340 healthcare organizations using DAX Copilot; July 2024.

² Survey of 413 patients conducted by multiple healthcare organizations whose clinicians use DAX Copilot; June 2024.

The post Meet Microsoft Dragon Copilot: Your new AI assistant for clinical workflow appeared first on Microsoft AI Blogs.

This unintended technological distraction has also had a negative impact in healthcare. Over the last decade, increasing regulations and mounting administrative burdens placed upon doctors, nurses, and radiologists, have come at a high cost to those who had dedicated their lives to caring for others. The effects of this have been well documented, with rising job dissatisfaction and burnout rates, increasing staffing shortages as clinicians leave the workforce, and the continued erosion of doctor-patient connection.¹

As a technologist who has been working on cracking some of the thorniest problems in healthcare, it’s painful to know that for years, despite our best efforts, technology has seemed one step behind in being able to restore the joy of caring for patients while simultaneously providing a more connected digital experience. 

That is, until the introduction of GPT. With generative AI, we’ve seen an incredibly positive and disrupting force in healthcare, and these gains will only increase as this critical innovation is applied to some of the most complex problems in healthcare. In fact, over the next three years, we will begin to see a tectonic shift in the entire user experience, moving from technology that is injected into various use cases to the pervasive infusion of AI that is seamlessly embedded into the ways we live and work.   

In healthcare, ambient intelligence will be the driving force for restoring the joy of practicing medicine and providing a better experience for patients. 

The real story of ambient intelligence  

There’s a lot written about technology curves and AI in healthcare, but I want to tell you the story that isn’t in the history books. The real story of how ambient intelligence was born. 

Some of us are old enough to remember the original Star Trek from the 1960’s where there was a computer that would be listening to the crew have a conversation and then weigh in with any guidance related to the situation at hand. It wasn’t trying to take over, it wasn’t replacing the captain and officers on the bridge, it was just supporting the team by adding insights in real time to augment the decision-making process.   

Most of us saw this as a cool sci-fi idea until one day, during a meeting with Epic, we talked about finding a way to make healthcare more intuitive, like the AI in Star Trek. The gauntlet had been thrown, and we were in.

Charting a new course in healthcare technology 

Inherent in ambient intelligence are two equally important variables, accurately transcribing a conversation between the doctor and patient into a text, and then turning that transcript into a clinical note.  

That was back in 2014, when there were no large language models, patient data wasn’t widely available, systems were extremely siloed, there wasn’t a way to even capture the recording and, even if those other aspects were possible, speech recognition for clinical conversations were running at about 50% word error rate (WER). This meant that the speech recognition system was getting only correctly capturing about half of the words spoken. That was essentially the state-of-the-art for ambient medical speech recognition and simply put, it didn’t work.

We weren’t sure if and when we’d ultimately be successful, but we knew the first challenge that we needed to tackle was getting more data to feed our models so that we could understand this emerging ambient workflow. We started a research program to boost recognition performance for ambient conversational medical speech because at that time, the major breakthroughs were being made in neural computing.

We then turned our attention to abstractive summarization, or essentially trying to figure out how to convert the conversational transcript between the doctor and patient into a structured clinical note, which is subject to a variety of constraints and requirements necessary for appropriate documentation.

Back then summarization was in its infancy, but the new neural summarization technology showed a lot of promise when large in-domain data sets comprised of millions of input and summarized output pairs were available. Although these data sets didn’t exist yet, there were virtual scribing workflows, where doctor-patient conversations were recorded and manually processed by human scribes. So, we made the decision to use clinical scribes to train the increasingly powerful models that were tailored to the task and then observe how their application accelerated clinical documentation. Essentially, the scribes were generating in-domain data that was then used by neural summarization machine learning to develop ambient summarization.

Given the complexities of a clinical encounter, we started with medical specialties that had highly-repetitive scenarios, like orthopedics, and then expanded to cover all ambulatory specialties across a larger population of doctors.

While we were making gains, they were incremental. To give you a sense of what this looked like, here is a chart that shows each new model revision as a plot point and you can see the percent of clinical encounters processed by AI and resulting human-in-the-loop edit rates, versus our forecast of where those figures would be.

The dawn of a new era  

It’s inevitable that anyone who’s tried to tackle an extremely thorny problem at some point will hit a wall where they ask themselves the question: Are we beating the problem or is the problem beating us? Although we had parity in converting a doctor-patient conversation to text, converting transcripts into customized clinical notes across specialties was challenging, and progress was slower than we would have liked.  We were using a human-in-the-loop to improve the quality of our model output, which wasn’t a scalable long-term solution, and we had stalled at an error rate that would not produce automation. We didn’t know the exact formula to make the problem yield.

Then, GPT happened.

Overnight, the scaling laws of AI changed. Major technological gains went from happening every one-and-a-half years to happening four times a year. While at the time, it had felt like we were hitting a wall, in hindsight, that time allowed us to deeply understand the requirements of how this technology would show up in the doctors’ workflow, and we partnered with the EHR companies to work through the technical details and optimize the user experience.

We immediately put a stake in the ground and began leveraging this new AI.

We used GPT as a shortcut to fine tune models and customize output, which allowed us to move faster while dramatically improving outcomes. We were also getting real-time feedback from clinicians who let us know what was working well and, most importantly, where the experience wasn’t optimized. It’s that latter feedback that is always the most helpful, because it enables us to triangulate the problems and work on ways to fine tune and improve the experience.

Based on the foundational models, we could see we would have a prototype in six months, but the challenge was that out-of-the-box GPT—while good—was not as performant as our bespoke models. That’s when we decided to combine generative AI and our unique training corpus. Within six months of a blistering R&D cycle, the team delivered a level of automation that had previously been unachievable in the prior six years. It was one of the first times in history that GPT-4 had been fine tuned for healthcare.   

The new scaling laws were bending the curve of innovation. We were at the dawn of a new era: The ambient AI market.

Over the course of 11 months, we went from zero users to creating the first clinical ambient intelligence experience for doctors that is trusted by more than 600 major healthcare systems, and producing more than 3 million episodes of care per month and growing. 

We achieved human parity, and had achieved a level of performance that enabled automation that provided doctors with a draft clinical note that required minimal editing, the automation problem had begun to yield. 

The future is now 

The future that we had classified as science fiction is here today, and ambient listening has already become table stakes. In fact, we release AI improvements weekly to our speech and listening technologies, which have been trusted and used by hundreds of thousands of clinicians for years.   

But more than that, we are witnessing a massive pivot unlike anything we’ve seen before: a new form of user experience—the combination of natural interaction and the infusion of real-time intelligence. 

As exciting as this all is, the true promise of addressing clinician burnout, improving the patient experience, and delivering better health outcomes hinges on collaboration and partnership. Every company operating in this space is limited by the laws of single company physics, which is why it’s an exciting time to be at a partner-led company. By opening up our ecosystem, we are harnessing the power of the Microsoft platform and extending it to thousands of companies worldwide that are focused on building applications and capabilities to improve the doctor-patient experience and positively impact the episode of care.   

We are enabling partners in the ecosystem to publish their capabilities directly into our ambient dial tone—the power of thousands of incredible minds all working to help clinicians, and solving for high-value use cases ranging from clinical condition diagnosis, autonomous clinical coding, and automating outbound healthcare consumer messaging, to enhancing data analytics and interpretation, medical literature discovery, autogenerating personalized patient educational materials, and automating clinical trial patient identification. These are just a few of the thousands of areas of innovation that are being actively worked on by healthcare companies worldwide. And this is the power of the platform. This is the ecosystem that will transform the way care is delivered, enhance patient experiences, support better outcomes across the health and life science ecosystem, and restore the joy of practicing medicine to clinicians around the world.   

Trust above all else 

No conversation about generative AI should happen without talking about responsibility, and no technology should be deployed without a detailed examination around what is contained in the data and how it is being used. Key responsible AI standards around fairness, reliability and safety, privacy and security, inclusiveness, and transparency must take the center stage in every discussion. AI is like a massive power tool, and data is the current powering it—so everyone handling it needs to be trained properly and aware of any unintended consequences or potential harm it could cause.  

Creating high-value use cases that deliver real outcomes 

In the end, the real testament to building outcomes-based technology comes down to one simple fact: does using it empower the person to do and be the best version of themselves? To that end, we carefully track the performance of all our solutions to make sure we’re building technology that is living up to its promise and exceeding expectations. I recommend that anyone who is advancing an AI agenda should do the same, because this is the real path to advancing human abilities and improving the healthcare ecosystem.   

Not every day is a win, and that’s okay—this is a marathon, not a sprint—but we continue to see powerful outcomes reported back by the people we serve. We’re seeing:  

• 70% improvement in work-life balance for clinicians and reduced feeling of burnout and fatigue.²
• 80% feel it reduces cognitive burden.³
• 5 minutes save per clinician per encounter (on average).⁴
• 93% of patients say their physician is more personable and conversational.⁵

Hear what clinicians have to say about Dragon Copilot:

As great as these results are, we’re not settling. We’re going to keep pushing ahead, refining our models, working with doctors, nurses, radiologists, and leaders across the health care and life sciences ecosystem to deliver the best technologies for those who continue to dedicate their lives to helping others. We’re just at the beginning of our journey, and we will continue to relentlessly innovate, and find new ways to streamline documentation, surface information, and automate tasks for clinicians worldwide. 

Learn more 

• Learn more at Microsoft Cloud for Healthcare. 

Microsoft Cloud for Healthcare

Accelerate innovation and improve healthcare experiences

Discover solutions

¹ AMA, Burnout benchmark: 28% unhappy with current health care job, May 17, 2022.

² Microsoft survey of 879 clinicians across 340 healthcare organizations using DAX Copilot; July 2024.

³ Microsoft survey of 879 clinicians across 340 healthcare organizations using DAX Copilot; July 2024.

⁴ Microsoft survey of 879 clinicians across 340 healthcare organizations using DAX Copilot; July 2024.

⁵ Survey of 413 patients conducted by multiple healthcare organizations whose clinicians use DAX Copilot; June 2024.

The post A new era of ambient intelligence in healthcare appeared first on Microsoft AI Blogs.

Epic® on Azure

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Azure for mission-critical workloads

Migrating EHR systems to Microsoft Azure provides healthcare organizations with a robust platform for mission-critical workloads, ensuring optimized performance, fast data access, built-in disaster recovery, and enhanced security features, such as AI-powered threat detection and automated compliance monitoring. On top of that, Azure maximizes cloud investments, offering new possibilities to harness data to springboard AI innovations.

Data is at the heart of healthcare. Hospitals produce more than 50 petabytes of data across more than 10 siloed systems every year. As the healthcare industry faces the dual challenges of managing vast amounts of unstructured data and a shortage of workforce, up to 97% of healthcare data goes unused, highlighting a significant missed opportunity for operational excellence and better patient insights.¹ One of the biggest benefits for healthcare customers on Azure is the ability to unify their multi-modal healthcare data for analytics and AI with healthcare data solutions in Microsoft Fabric that lets them ingest, store, and analyze data from various sources and modalities. While Fabric unifies your data, Microsoft Purview delivers the data governance service that helps you classify the data across your data estate, including identification for sensitive data. Integrating Microsoft Purview with healthcare data solutions in Fabric not only strengthens security but also help you ensure compliance, enabling healthcare organizations to govern their data with confidence. We are acutely aware of the industry expectations in which our technology is utilized, and this is one of the many reasons why our healthcare customers trust Azure for mission-critical workloads.

As we continue to deliver data innovations, we see our customers use their connected data on a wide spectrum of AI capabilities. With Azure AI, healthcare organizations can accelerate innovation through predictive analytics, automate clinical tasks, and improve patient interactions with the help of ambient AI solutions like DAX Copilot (directly embedded in EHR systems), as well as take advantage of Microsoft healthcare AI models in Azure AI Foundry and GitHub, a collection of cutting-edge multi-modal generative AI models that benefit imaging and radiology workflows.

Enhanced support for mission-critical

Mission-critical workloads demand comprehensive support. In 2024, Microsoft Unified enhanced its support for mission-critical workloads in healthcare through its Mission Critical Offerings. This initiative provides proactive support to improve the health, resiliency, and performance of healthcare systems via regular assessments, guidance, and optimization recommendations, ensuring business continuity and addressing unique healthcare challenges.

Collaborating for technology excellence: A strategic partnership that stands out

Our commitment to mission-critical is reflected in our collaborations with leading EHR providers such as Epic®. This long-standing relationship of more than 20 years has yielded an optimized solution for Epic® on Azure, offering a robust, purpose-built platform backed by joint-reference architecture. Recently, Microsoft announced expanded scalability on Azure for healthcare organizations, specifically for running Epic®’s Chronicles* Operational Database (ODB), increasing its capacity to 65 million global references per second (GRefs/s), a 171% enhancement from 2023 on the new Mbv3 VM series.

The collaboration with Epic® extends well beyond the cloud infrastructure—to several products and capabilities part of Microsoft Cloud for Healthcare. Epic® and Microsoft have expanded their collaboration to integrate advanced AI technologies such as Microsoft Azure OpenAI Service and the DAX Copilot into Epic®’s EHR system. The integration helps provide AI-powered clinical insights, streamline administrative processes, and improve clinician productivity through features like note summarization and automated coding suggestions.

Delivering value beyond infrastructure: The Microsoft Cloud for Healthcare promise

Microsoft’s well-rounded partnership with Epic® is one of the many reasons why Azure is the cloud of choice for many of our healthcare customers.

The decision to move mission-critical workloads to the cloud is often not just about infrastructure. Customers like Mercy chose Azure to not only modernize their infrastructure but also extract value from sizeable data archives. Mercy’s digital transformation on Azure enabled it to connect previously siloed data and use several Microsoft services such as Azure Data Lake to result in positive business outcomes. For example, by empowering care teams with smart dashboards and insights into factors that determine patient discharge, Mercy has been able to reduce patient stay durations significantly. Mercy employs Azure AI Document Intelligence to scan and recognize information on patient’s insurance cards which then gets updated on their EHR records automatically.

We recognize our customer’s desire to have a complete digital transformation in the cloud that transcends every layer of the stack, and Microsoft Cloud for Healthcare lets us deliver to that promise. It encapsulates a broad spectrum of innovative data and AI innovations from Microsoft, purpose-built for the healthcare industry, enabling our customers to achieve their cloud-first goals faster and easier. Recently, Microsoft announced several innovations as part of the portfolio, including new healthcare AI models in Azure AI Foundry, capabilities for healthcare data solutions in Microsoft Fabric, the healthcare agent service in Copilot Studio, and an AI-powered nursing workflow solution.

As customers realize the value of consolidating their IT investments around a single vendor, Azure is increasingly being adopted for mission-critical workloads. By seamlessly connecting and delivering value across all layers of the stack, Azure for mission-critical extends a customer’s return on cloud investments. Customers like St. Luke’s University Health System are reaping the benefits of their Epic® on Azure migration by taking advantage of several synergies in the Microsoft portfolio, like the interoperability of Microsoft Teams with Epic®. Security is of paramount importance when dealing with patient records, and customers like Jefferson Health migrate their Epic® environments to Azure with high confidence with Microsoft Defender for end-point detection and response.

Next steps

As we continue to transform mission-critical workloads in the cloud, we are making it easier for our partners and customers to create connected experiences at every point of care, empower their healthcare workforce, and unlock the value from their data, all with uncompromised privacy and security. Microsoft Cloud for Healthcare is supporting healthcare organizations on every step of their journey toward shaping a healthier future.

• Explore Epic® on Azure.
• Learn more about the Mbv3 VM series.
• Learn more about Microsoft Cloud for Healthcare.

*Epic® and Chronicles are trademarks of Epic Systems Corporation.

¹ World Economic Forum, 4 ways data is improving healthcare, December 2019.

The post Azure for mission-critical workloads in healthcare: EHR and beyond appeared first on Microsoft AI Blogs.

Generative and other types of AI are transforming radiology from the reading room to the research lab. Sometimes, generative AI looks like a more efficient meeting for the tumor board. Other times, it’s a draft impression that reminds you to include details you might have missed. In all instances, it looks like empowered radiology teams, working more efficiently to improve patient outcomes.

At RSNA 2024 we showcased the expanding opportunities for radiology teams to access responsible AI, delivered at scale.¹

Giving time back to radiologists

Most radiologists still spend a significant amount of their time on tasks outside of image interpretation and report creation. From supporting a tumor board conference to drafting a grant proposal, it’s important work, but it’s time consuming.

It’s also the kind of work that Microsoft 365 Copilot can help accelerate.

Microsoft 365 Copilot, with over 400 million users, is boosting productivity worldwide. By securely connecting and graphing the data across your applications and web activity, Copilot has the context it needs to provide more personalized and intelligent responses.

When entire radiology teams automate, search, summarize, and create with Copilot to address their everyday tasks, the minutes they save quickly add up.

Applying AI—including generative AI—inside the reading room

In 2023, we shared how Precision Imaging Network, part of Microsoft Cloud for Healthcare, makes it easier for radiology teams to integrate third-party AI into their reporting workflows.

Precision Imaging Network offers a single point of access to a collection of third-party AI models that have gone through security, compliance, and technical reviews. There are triage AI models that work to identify imaging with critical findings and help ensure radiologists read the most urgent cases first. Detection and measurement AI models assist radiologists with image interpretation by highlighting abnormalities.

In his white paper, “A platform for healthcare enterprise AI and digital transformation,” neuroradiologist and medical AI expert Dr. Reza Forghani explains how Precision Imaging Network facilitates scalable, efficient AI model deployment in weeks, not months.

“Precision Imaging Network is designed to help healthcare organizations, regardless of size, participate and lead in an AI revolution. Its framework facilitates effective and seamless integration of imaging AI models that can help them deliver better patient care.”

Dr. Reza Forghani, MD, PhD, Neuroradiologist and medical AI expert

Beyond seamlessly integrating third-party AI into their workflow, radiologists can harness generative AI to help automate report creation.

PowerScribe Smart Impression, which won the AuntMinnie award for Best New Radiology Software 2024, uses generative AI to automatically create a draft impression in the radiologist’s own style of dictation, using order metadata, approved AI inputs, and dictated findings. It is trained on millions of reports and delivers a range of benefits—from greater efficiency to increased job satisfaction.

Dr. Janusz Kikut is Professor of Radiology and Division Chief at the Nuclear Medicine Department of Radiology at University of Vermont. He’s found that three-quarters of the radiologists in his department using PowerScribe Smart Impression believe it has made them more efficient. “It also helps them not to forget things […] and contributes to their satisfaction at work,” says Dr. Kikut.

At Microsoft, we’re focused on making AI available to more individuals which is why PowerScribe Smart Impression is now included with PowerScribe One.² Best of all, it’s natively embedded in the workflow with no additional integrations required.

Making AI innovation safer and simpler

We’re also committed to helping healthcare customers, developers, and partners build and use AI that’s guided by our security, safety, and privacy principles. Our recently announced healthcare agent service in Microsoft Copilot Studio is a great example. It allows healthcare organizations to create their own generative AI-powered agents with state-of-the-art healthcare-specific chat, clinical, and compliance safeguards. This empowers them to innovate responsibly with AI. To help improve the patient experience and streamline workflows. This empowers them to innovate responsibly with AI to help improve the patient experience and streamline workflows.

Another way we’re helping organizations experiment with AI is through the pretrained, multimodal healthcare AI models in Azure AI Foundry. The teams at University of Wisconsin-Madison and UW Health have been using their database of 2.5 million chest x-rays to fine-tune a model that generates draft reports with visual grounding techniques. The radiologist can click on the report content to see where the referenced areas are located directly on the chest x-ray.

“Grounded report generation from medical images is a new frontier. Collaborating with Microsoft allows us to explore the potential of these models to enhance radiology reporting and improve patient care.”

Dr. Richard Bruce, Vice Chair of Informatics, Radiology at University of Wisconsin-Madison

This story is just one of many. Healthcare visionaries are applying our multimodal foundational models to address challenges from transforming radiology workflows to accelerating advancing clinical research, drug discovery, and patient care.

Supporting our partners to build a brighter future

At RSNA 2024, we shared how our collaboration with Sectra underscores our commitment to advancing healthcare through AI. By collaborating with Sectra, we are combining their leading expertise in medical imaging with our AI capabilities to develop advanced solutions that help radiologists and clinicians make faster, more informed decisions, driving improvements in healthcare outcomes. 

At our booth, we showcased a stellar lineup of partners with Azure-hosted and aligned solutions. These included Flywheel’s medical imaging data management platform, Volpara + Lunit’s AI solution for detecting lesions suspicious for breast cancer on mammograms, and Merge by Merative’s enterprise imaging workflow solution. We also featured Niramai’s AI-based breast cancer screening using AI over thermal scans, NVIDIA’s DGX™ Cloud serverless AI model building, and Paige’s AI software for cancer diagnosis in pathology. Additionally, we shared how CitiusTech’s Azure DICOM Migration Suite is using Azure Health Data Services and Microsoft Fabric to help ensure reliable data migration with high availability and disaster recovery.

A look forward to AI in radiology

AI continues to significantly transform radiology—expanding the realm of possibilities. Importantly, it can streamline workflows, enabling radiologists to focus on what matters most—improving patient care with high-quality reporting. As more radiology teams adopt and advance AI innovation, we eagerly anticipate an inspiring year ahead.

Copilot livestream for radiology

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¹Microsoft 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. You are responsible for ensuring solutions comply with applicable laws and regulations.

²PowerScribe Smart Impression available for PowerScribe One versions 2019.8 and 2023.1 Service Pack 2.

The post RSNA 2024: AI’s impact inside and outside the reading room appeared first on Microsoft AI Blogs.

Existing language models have revolutionized how we interact and use powerful AI models for text-based use cases in healthcare. But the practice of modern medicine is chiefly multimodal. Effectively assessing the complete picture of patient health requires moving beyond medical text comprehension to sophisticated AI models capable of integrating and analyzing diverse data sources across modalities such as medical imaging, genomics, clinical records, and more.

The creation of comprehensive multimodal models has traditionally been hindered by the need for large-scale, integrated datasets and the significant computational power needed to train these models. These barriers have limited the ability of many healthcare organizations to fully leverage AI.

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Microsoft Cloud for Healthcare helps to bridge this gap and accelerate AI development. We’re announcing the launch of healthcare AI models, a collection of cutting-edge multimodal medical imaging foundation models available in the Microsoft Azure AI model catalog. Developed in collaboration with Microsoft Research and strategic partners, these AI models are specifically designed for healthcare organizations to test, fine-tune, and build AI solutions tailored to their specific needs, all while minimizing the extensive compute and data requirements typically associated with building multimodal models from scratch. With healthcare AI models, health professionals have the tools they need to explore the full potential of AI to transform patient care.

Healthcare AI models include:

• MedImageInsight: An embedding model enables sophisticated image analysis, including classification and similarity search in medical imaging. Healthcare organizations and researchers can use the model embeddings and build adapters for their specific tasks, streamlining workflows in radiology, pathology, ophthalmology, dermatology, and other modalities. For example, researchers can explore how the model can be used to build tools to automatically route imaging scans to specialists, or flag potential abnormalities for further review, enabling improved efficiency and patient outcomes.¹
• MedImageParse: Designed for precise image segmentation, this model covers various imaging modalities, including x-rays, CTs, MRIs, ultrasounds, dermatology images, and pathology slides. It can be fine-tuned for specific applications such as tumor segmentation or organ delineation, allowing developers to test and validate the ability to leverage AI for highly targeted cancer and other disease detection, diagnostics, and treatment planning.²
• CXRReportGen: Chest x-rays are the most common radiology procedure globally. They’re crucial because they help doctors diagnose a wide range of conditions—from lung infections to heart problems. These images are often the first step in detecting health issues that affect millions of people. By incorporating current and prior images, along with key patient information, this multimodal AI model generates detailed, structured reports from chest x-rays, highlighting AI-generated findings directly on the images to align with human-in-the-loop workflows. Researchers can test this capability and the potential to accelerate turnaround times while enhancing the diagnostic precision of radiologists. This model has demonstrated exceptional performance on the industry standard MIMIC-CXR benchmark.³

These foundational models can accelerate the arrival of groundbreaking AI models that bring intelligent workflows, efficient report generation, and advanced view identification and segmentation to the radiologist experience. In addition to supporting report accuracy, AI can help advance patient care by unlocking new insights from radiology and pathology and genomics, accelerating the discovery of new treatments for disease, and predicting outcomes and optimal treatment plans.

Resources are no longer a barrier to innovation

With so many demands on healthcare and life sciences organizations, it’s challenging to dedicate time, resources, and budget to experiment with AI. Healthcare AI models feature open source, pretrained models that represent some of the highest level of performance currently achievable on public benchmarks.

In aggregate, the healthcare AI models and others in our catalog of multimodal medical foundation models span a wide range of modalities and a growing catalog of competencies, enabling the testing and validation of a wide range of use cases, including:

• Using an image embedding model to search for similar images or facilitate detection of anomalies that could indicate potential data issues or system errors (Fig. 2: Image embedding).
• Building an adapter to the embedding model for a specific task. (Fig. 3: Adapter to specific task)
• Fine-tuning pretrained unimodal models to create a narrow model. (Fig. 4: Fine-tuning for a specific task)
• Integrating language models to enable the extraction of insights across modalities and enhance the interpretability of multimodal data. (Fig. 5: Adapter to general reasoner)
• Connecting different data modalities for a more comprehensive, holistic view of data that derives new insights and enables the discovery of previously hidden correlations and patterns.

With the flexibility and breadth of models, individual unimodal health models can be used independently, connected to different modalities, or further combined with advanced general reasoning models like GPT-4o and Phi to create powerful multimodal models without the need for massive integrated datasets from the outset. Azure AI Studio and healthcare AI models complement the healthcare data solutions available in Microsoft Fabric, creating a unified environment for comprehensive analysis and vital patient insights.

Created by a collaborative network of partners

Our ecosystem of partners dedicated to advancing the industry’s use of AI made healthcare AI models possible. Paige, Providence Healthcare, Nvidia, and M42 contributed foundational models to the catalog, spanning pathology, 3D medical imaging, biomedical research, and medical knowledge sharing. Developed under a core set of shared AI principles, these models provide a powerful starting point for organizations as they launch their own AI projects, while embedding responsible practices across the industry. Microsoft is committed to responsibly scaling AI and to listen, learn, and improve our tools. We work with organizations to help them harness the data to build the predictive and analytical power required for their own competitive advantage.

The open access to AI models on the catalog and modular approach allows healthcare organizations to customize solutions, maintain control over their data, and build trust through shared development and oversight. This approach aligns with our commitment to responsible AI, ensuring our technologies meet ethical standards and earn the trust of the medical community.

The catalog’s ongoing evolution will be a collaborative effort—not just among those providing foundational models, but also with the support of customers and partners that are building on these models to develop their own research or clinical systems.

Microsoft is committed to fostering transparency and community involvement within an ecosystem that empowers partners, developers, and researchers to push the boundaries of what is possible in healthcare and empower healthcare and life sciences organizations to achieve more. It’s not just about building models; it’s about unlocking new insights, accelerating innovation, and ultimately improving patient outcomes on a global scale, from pioneering cutting-edge pharmaceutical research to delivering life-changing medical care.

Innovation in action

Several customers are already taking advantage of the possibilities unlocked by healthcare AI models.

Mass General Brigham, as well as the University of Wisconsin School of Medicine, Public Health and UW Health are targeting advanced report generation from medical imaging analysis. With ever-increasing imaging volumes colliding with the ongoing combination of radiologist burnout and shortages, a state-of-the-art medical imaging model can be used to build an application that can transform a medical image into a draft note. Projects like these can transform the efficiency of core healthcare workflows, supporting better outcomes for patients while helping clinicians focus on the hands-on components of their roles.

“Grounded report generation from medical images is a new frontier. Our shared collaboration brings diverse expertise to developing, testing, and validating new models. We are working to identify and overcome the challenges of how models can be integrated into real clinical systems and workflows so that a pathway exists for these capabilities to have the potential to impact real patient care in the future.”

—Richard Bruce MD, radiology Vice Chair of Informatics, University of Wisconsin-Madison

In life sciences, Paige is working to combine radiology, pathology, and genomic insights for a more comprehensive approach to disease diagnosis, aimed at accelerating the discovery of new treatments. AI has a key role to play throughout the healthcare continuum, and advances made in our understanding of risks, diseases, and treatments will be instrumental for improving downstream patient care.

“The collaboration with Microsoft has enabled Paige to unlock insights from millions of digitized pathology slides, clinical reports, and genomic data, to gain a more holistic understanding of cancer. Together, we are pioneering frontier multi-modal AI models that have the potential to accelerate and redefine cancer detection, diagnosis, and treatment. We are thrilled to continue to lead the charge and shape the future of precision oncology.”

—Razik Yousfi, Chief Executive Officer & Chief Technology Officer of Paige

And it’s not just human health that healthcare AI models are supporting; Mars PETCARE is exploring use cases in veterinary medicine, such as data evaluation for radiology and pathology teams. Treating pets is every bit as complicated as treating humans, so this work just goes to show the platform’s versatility—each of these models can be turned to a novel application with the right approach.

“Our strategic partnership with Microsoft represents a significant leap forward in veterinary diagnostics. As early adopters of AI in digital pathology and radiology, we’ve seen firsthand how this technology can transform animal care. By combining our veterinary expertise with Microsoft’s frontier AI models, we’re not just advancing diagnostics, we’re creating a better world for pets. This collaboration will accelerate our AI R&D [research and development] efforts, empowering veterinarians with more accurate and efficient tools. Together, we’re setting new standards in veterinary medicine and reinforcing our commitment to innovation in animal health.”

—Jerry Martin, Vice President, Research & Development, Mars Science & Diagnostics

“Sectra is exploring how image and text embeddings from foundational models can be leveraged to transform workflow tasks in radiology. Traditionally managed through static configurations, these tasks are now being revamped to adapt to the diverse nature of healthcare data using generative AI.”

—Fredrik Häll, Head of Product, SECTRA

“Topcon Healthcare is building a multimodal and three-dimensional ophthalmic imaging Foundation Model (FM) to phenotype healthy populations by leveraging data collected from large population-based screening environments. This FM facilitates exploration of biomarkers in the eye that are early indicators of eye and systemic diseases.”

—Mary Durbin, Vice President of Clinical Science, Topcon Healthcare

“We are excited to offer Med42, our leading clinical LLM, through Azure AI Studio. With Med42, we are harnessing the power of AI to impactfully disrupt traditional healthcare systems and deliver value for clinicians, scientists, and patients. With advancements like our Med42 suite of healthcare foundation models to MEDIC, our comprehensive clinical evaluation framework for LLMs, M42 is advancing global innovation in healthcare.”

—Dr. Ronnie Rajan, Associate Director, AI & Applied Science, M42

“The development of foundational AI models in pathology and medical imaging is expected to drive significant advancements in cancer research and diagnostics. These models can complement human expertise by providing insights beyond traditional visual interpretation, and as we move toward a more integrated, multimodal approach, will reshape the future of medicine.”

—Carlo Bifulco, MD, Chief Medical Officer, Providence Genomics and a co-author of the Prov-GigaPath study

Microsoft Cloud for Healthcare is helping your organization shape a healthier future with data and AI

We’re excited to strengthen our data and AI investments through the Microsoft Cloud for Healthcare. Our healthcare solutions are built on a foundation of trust and Microsoft’s Responsible AI principles. Through these innovations, we’re making it easier for our partners and customers to create connected experiences at every point of care, empower their healthcare workforce, and unlock the value from their data using data standards that are important to the healthcare industry.

Learn more about AI with healthcare

• Join us at HLTH2024, Booth #4004 to see healthcare AI models in action.
• Starting on October 20, 2024, visit Microsoft Azure AI Studio to learn more about healthcare AI models and explore how its foundational models could help you transform the way your organization uses AI.
• Connect and learn about Microsoft Research’s advancements in AI and language models
• Learn more about healthcare data solutions in Microsoft Fabric.
• Watch the AI for Business Transformation: Multimodal Models interview.

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.

Generative AI does not always provide accurate or complete information. AI outputs do not reflect the opinions of Microsoft. Customers/partners will need to thoroughly test and evaluate whether an AI tool is fit for the intended use and identify and mitigate any risks to end users associated with its use. Customers/partners should thoroughly review the product documentation for each tool.

¹MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging, 2024.

²BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once, 2024.

³MAIRA-2: Grounded Radiology Report Generation, 2024.

The post Unlocking next-generation AI capabilities with healthcare AI models appeared first on Microsoft AI Blogs.

Healthcare systems face numerous challenges, including workforce shortages, rising costs, and increasing patient care demands. Clinical staff in health organizations are overburdened with workloads resulting in high levels of stress and long hours, burnout among healthcare professionals, and higher rates of attrition and staff shortage. Generative AI offers a potential solution to these challenges by automating administrative tasks, analyzing vast amounts of data for actionable insights, and assisting healthcare professionals in decision making. 

Healthcare agent service in Microsoft Copilot Studio

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To address this, Microsoft is announcing the public preview of healthcare agent service for Microsoft Copilot Studio. Copilot Studio serves as the single platform to build powerful and connected agents, offering a comprehensive suite of tools and features. With the addition of the healthcare agent service, users can now create safer healthcare agents by using generative AI and a healthcare-specialized stack. This service allows customers to build their own agents with reusable healthcare-specific features, pre-built healthcare intelligence from credible sources, templates, and pre-built healthcare use cases. It ensures that these agents meet industry standards and address safety in healthcare by offering state-of-the-art healthcare specific chat, clinical, and compliance safeguards.

The healthcare agent service enables healthcare organizations to develop their own generative AI-powered agents for patients or clinicians, supporting diverse use cases across appointment scheduling, clinical trial matching, patient triaging, and more. The service also supports extending an organization’s agents with additional plugins, no matter where the plugins were built.

Reinforcing our commitment to responsible AI safeguards

Healthcare is a truly unique industry. It is a sensitive domain that impacts people’s health and well-being. When it comes to generative AI, issues like fabrications, omissions and inaccurate answers become more important to address and need to be handled in a way that is specific to the needs of the healthcare industry.

To address these concerns, we are making our clinical safeguards APIs available as private preview to select customers, to be used for evaluation and additional verification of generative AI outputs. The following safeguards will be available as part of this private preview:

• Clinical fabrications and omissions detection, helping detect them in generative answers compared to grounding data.
• Clinical anchoring, providing clinical context and concept identification to clinical elements within prompts, making them more prominent to the AI system.
• Clinical provenance, helping identify the source of claims against the grounding data
• Clinical coding verification, helping verify that clinical codes provided by generative AI actually exist and are relevant to context.
• Clinical semantic validation, helping verify that responses conform to known valid clinical semantic structures.

Revolutionizing healthcare with generative AI

Recently, two prominent healthcare institutions, Cleveland Clinic and Galilee Medical Center, have been at the forefront of integrating advanced AI technologies into their services. Their collaboration with Microsoft has led to significant advancements in patient care and information accessibility.

Cleveland Clinic took part in the private preview of healthcare agent service in Microsoft Copilot Studio.

“We are constantly seeking innovative ways to improve the patient experience and provide seamless access to relevant information. Utilizing AI technologies opens up new possibilities for how patients can interact with us, and we appreciate the opportunity to collaborate with Microsoft on this project. Our goal is to help develop AI-powered tools that will make it easier for our patients to find the information they need, ask health-related questions, and navigate our services”

Cleveland Clinic

Galilee Medical Center has also embraced advanced AI solutions to simplify complex medical data for patients, it has been using Clinical safeguards API as an early adopter.

“In collaboration with Microsoft, we developed a patient-friendly radiology report, powered by [Microsoft] Azure OpenAI [Service], making complex radiology data accessible in simple, layman’s terms. Patients not only receive this simplified report but can also ask follow-up questions. They can query specific findings and track how each element relates to the original radiology report. Leveraging the Clinical Provenance Safeguard, which is part of the Clinical safeguards API, we ensured traceability, providing clear evidence of where each piece of simplified information originates from within the original medical report.”

Dr. Dan Paz, Head of Radiology Department, Galilee Medical Center

Do more with your data with Microsoft Cloud for Healthcare

With healthcare agent service in Microsoft Copilot Studio infused with generative AI and healthcare safeguards, health organizations can transform their medical professionals and patient experiences, integrate those with health data services and with AI models that discover new insights using the power of machine learning and AI in a safer way, and manage protected health information (PHI) data with confidence. Enable your future of healthcare innovation with Microsoft Cloud for Healthcare. We look forward to being your partner as you build the future of health.

• Discover how we are shaping the future of health with cutting-edge solutions and collaborative efforts.
• Learn more in the healthcare agent service in Copilot Studio documentation.
• Apply for the private preview of clinical safeguards in healthcare agent service in Copilot Studio.
• Explore the possibilities with Microsoft Copilot Studio.
• Expand your knowledge about Microsoft Cloud for Healthcare.

Sources:

1. Burned Out on Burnout — The Urgency of Equity-Minded Structural Approaches to Support Nurses

The post Introducing healthcare agent service in Microsoft Copilot Studio appeared first on Microsoft AI Blogs.

Learn more about how Microsoft is enhancing healthcare with data and responsible AI. Read the latest Microsoft Cloud for Healthcare announcements.

We are thrilled to announce the general availability of healthcare data solutions in Microsoft Fabric, a comprehensive solution that enables organizations to ingest, store, and analyze healthcare-related data from various sources and modalities into one unified data store for analytics and AI. Also, the healthcare application templates in Microsoft Purview, an innovative suite of features designed to help you govern your healthcare data with confidence, is now available in public preview. With these advancements, Microsoft Cloud for Healthcare now offers an expanded bundle of general availability services to further empower healthcare organizations in their digital transformation journey.

EPAM, renowned for supporting Microsoft Cloud for Healthcare clients with advanced technical solutions, has experienced substantial benefits from leveraging healthcare data solutions in Microsoft Fabric. These advantages manifest as heightened customer satisfaction, notable cost savings, and accelerated project timelines. The solution’s impact is particularly noteworthy as it provides healthcare clients with reliable, AI-ready data in an efficient manner.  

“For a recent AI and Advanced Analytics Solutions, our projections estimated a significant investment to develop the necessary data products for the analytics needs, with a substantial portion dedicated to establishing the data foundation. However, with the launch of this solution and a successful pilot, we were able to revise the project scope significantly. This resulted in more than 40% reduction in implementation time and costs, highlighting the efficiency and cost-effectiveness of the solution.” 
—Brian Blanchard, Cloud Chief Technology Officer, EPAM

Unify your multi-modal healthcare data for analytics and AI with healthcare data solutions in Microsoft Fabric 

The general availability of healthcare data solutions in Microsoft Fabric represents a significant leap in the journey towards data-centric healthcare. This all-encompassing platform streamlines the integration, preservation, and examination of diverse biomedical data, bridging the gaps between isolated data repositories and fostering powerful analytical insights. 

Healthcare data solutions in Microsoft Fabric enables healthcare organizations to ingest, store, and analyze data from various sources and modalities. These data solutions provide a set of capabilities that enable a multi-modal biomedical lakehouse, which can handle clinical, imaging, claims, conversational, and social determinants of health (SDOH) data. It adheres to Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) standards, introduces data transformations, and ensures a secure, regulatory-compliant framework for managing data. 

By using healthcare data solutions, you can benefit from the following features:  

• A unified data model that supports industry standards such as FHIR and DICOM. 

• A rich set of data transformation and enrichment tools that prepare your data for analysis, as well as add clinical and demographic annotations.  

• A suite of data visualization and cohorting experiences, that can help you discover patterns, trends, and outliers in your data, as well as create dashboards and reports.  

• A secure and compliant environment that aims to meet the Health Insurance Portability and Accountability Act (HIPAA), Health Information Trust Alliance (HITRUST), and General Data Protection Regulation (GDPR) requirements, with role-based access control, data governance, and data lineage.  

Additionally, we are pleased to announce the public preview of additional functionality that enhances the existing capabilities within our healthcare data solutions offering. These include: 

• Conversational Data Integration: Enable customers to send their conversational data, such as patient conversations from DAX Copilot to Fabric. By sending DAX audio files, transcripts, and draft clinical notes to Fabric, customers and partners can leverage various native tools in Azure and Fabric to analyze this data or combine it with other data to generate comprehensive insights. 

• Social determinants of health public datasets transformation: Ingest, persist, harmonize, and consume SDOH public datasets such as Location Affordability Index, Food Environment Atlas and Rural Atlas from the United States Department of Agriculture (USDA), Environmental Justice Index, ACS Education Attainment, SDOH Dataset from the Agency for Healthcare Research and Quality (AHRQ), Australian Socio-Economic Indexes for Areas (SEIFA), and United Kingdom Indices of Deprivation to enable healthcare organizations to identify risks and health-related social needs to create equitable health care for all patients and communities. 

• Centers for Medicare and Medicaid Services Claim and Claim Line Feed Files (CMS CCLF) claims data ingestion: Streamline the ingestion of CMS CCLF claims data and harmonize with clinical, imaging, and SDOH data to unlock actionable insights on patients and populations. Use this data to understand and manage healthcare costs, identify care gaps, and help health outcomes. 

• Care management analytics: Leverage unified healthcare data and care management analytical templates to enhance patient care by identifying high-risk individuals, optimizing treatment plans, and improving care coordination and empower your organization to deliver personalized, efficient, and proactive care. 

• Data discovery and cohorting: An integrated workflow that allows you to create, manage, analyze, and share patient cohorts. Create cohorts using natural language and export cohorts into AI pipelines, notebooks, or downstream applications.

With healthcare data solutions, you can unlock the value of your data and enable data-driven healthcare. You can use your data to help improve patient outcomes, enhance population health, optimize operational efficiency, and accelerate research and innovation. You can also leverage the power of AI and machine learning to build predictive models, identify risk factors, and personalize interventions.  

Unlock the full potential of your healthcare data with the new features and capabilities now available in Microsoft Fabric’s healthcare data solutions. Don’t miss out on the opportunity to revolutionize your healthcare services; start your free trial now.

Healthcare data solutions in Microsoft Fabric

Unlock the full potential of your healthcare data with the new features and capabilities now available

Learn how

Protect your data with the healthcare application templates in Microsoft Purview 

Healthcare organizations need to govern their data with assured confidence—guaranteeing robust security and strict compliance. Microsoft Purview is a unified data governance service that helps you discover, catalog, and classify data across your data estate. The healthcare application templates for Microsoft Purview accelerates our customers journey by providing healthcare-specific classifications, glossaries for healthcare standards, and guidelines on how to use them together with healthcare data solutions in Microsoft Fabric. 

“Healthcare application templates in Microsoft Purview strengthens Microsoft’s commitment to support its clients in the healthcare industry. Here at Sentara we have been leveraging its glossaries and classifications to identify and tag our critical assets. We look forward to partnering with them on their journey continuous advancement in data governance.”
—Abdul Ghani Mohammed, IT Manager (Data Governance & Data Quality), Sentara Health 

Identify sensitive healthcare data with healthcare classifications 

One of the key challenges of governing healthcare data is identifying and protecting sensitive information, such as protected health information (PHI) or personal identifiable information (PII). Microsoft Purview addresses this challenge by providing a set of healthcare-specific classifications based on HIPAA privacy rules. These classifications cover important healthcare classifications such as date of admission, date of discharge, and more. You can also create custom classifications using regular expressions or keywords, allowing you to automatically discover and classify sensitive data across different data sources like Azure Data Lake Storage, Azure SQL Database, Azure Synapse Analytics, and Power BI. 

Understand your healthcare data with glossaries based on healthcare standards 

Healthcare data is often complex and diverse, coming from various sources and formats. To help you understand and manage your data, Microsoft Purview provides glossaries for healthcare standards such as Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM). These glossaries contain definitions and metadata for common healthcare terms and concepts, improving data quality, consistency, and interoperability across your data estate. 

Test your governance solution with healthcare sample data 

Sample data is essential for evaluating, testing, and validating the custom healthcare classifications and glossaries. By providing a realistic and relevant healthcare dataset, customers can validate if the Purview healthcare application templates meet their organization’s needs without bringing in their own production or sample data. 

Protect your data in healthcare data solutions in Microsoft Fabric 

The healthcare application templates provided by Microsoft Purview facilitate the discovery, cataloging, and classification of data, thereby enhancing data quality, consistency, and interoperability across the healthcare data estate.  

Using these templates, you can enhance your healthcare data solutions in Microsoft Fabric with data governance capabilities that help protect your data. You can use Microsoft Purview to scan your healthcare data solutions in Microsoft Fabric data lake and catalog your data assets with classifications and glossaries. You can also use Microsoft Purview to monitor your data lineage, data quality, and data access across your healthcare data solutions in Microsoft Fabric pipelines and applications. By integrating Microsoft Purview with healthcare data solutions in Microsoft Fabric, you can gain a holistic and trusted view of your healthcare data and leverage it for better insights and outcomes. This unified approach not only strengthens security but also ensures strict compliance, enabling healthcare organizations to govern their data with confidence.  

Learn more about how to get access to the public preview, and learn how you can use healthcare application templates to protect your data in the healthcare data solutions in Microsoft Fabric. 

Microsoft Cloud for Healthcare is helping your organization shape a healthier future with data and AI 

We are excited to strengthen our data and AI investments through the Microsoft Cloud for Healthcare. Our healthcare solutions are built on a foundation of trust and Microsoft’s responsible AI principles. Through these innovations, we are making it easier for our partners and customers to create connected experiences at every point of care, empower their healthcare workforce, and unlock the value from their data using data standards that are important to the healthcare industry. 

Learn more about all of our solutions in healthcare: Microsoft Cloud Solution Center.

The post Power healthcare AI with unified and protected multi-modal healthcare data appeared first on Microsoft AI Blogs.

Powering healthcare AI with unified and protected data

We are excited to announce the general availability of healthcare data solutions in Microsoft Fabric, along with the public preview of healthcare application templates for Microsoft Purview. These advancements represent a leap forward for data-centric healthcare, enabling organizations to streamline, secure, and analyze diverse biomedical data—the foundation to build powerful and responsible healthcare AI.

In the latest public preview release, Microsoft Fabric’s healthcare data solutions now integrate DAX Copilot data, offering a comprehensive platform for ingesting, storing, and analyzing a wide range of healthcare data. This includes clinical, imaging, claims, conversational, and social determinants of health (SDOH) data, all handled within a multi-modal biomedical lakehouse. The platform adheres to healthcare standards, such as Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM), and provides tools for data transformation, enrichment, discovery, and visualization.

With the new addition of DAX, the Dragon Ambient Experience, a draft medical note is generated at the point of care, combining healthcare and conversational data to generate unique insights. The platform has controls in place to support an organization’s compliance with the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), offering role-based access control and robust data governance. An AI-powered data discovery and grouping experience enables customers to discover their data and supports healthcare providers build patient cohorts for AI development, enhancing the capabilities of data discovery and building cohorts in this public preview.

In tandem, the healthcare application templates for Microsoft Purview are designed to help healthcare organizations govern their data with confidence. These templates allow healthcare organizations to discover and govern sensitive data in compliance with industry standards.

AI advances in healthcare

Microsoft Cloud for Healthcare offers both healthcare data solutions and healthcare AI models well suited for managing multimodal healthcare data.

We are excited to unveil the public preview of Microsoft healthcare AI models in Azure AI Studio and GitHub, a collection of cutting-edge multimodal generative AI models for healthcare and life sciences available in the Azure AI model catalog. This platform empowers developers and researchers to create sophisticated multimodal health and life sciences solutions more efficiently, using fewer data and compute resources, thus reducing development time and costs.

We have collaborated with our partner ecosystem to bring forth a suite of advanced AI models that can work with different types of medical data. These models are engineered to operate both in conjunction with advanced general reasoning models like GPT-4o and Microsoft’s Phi-3 family, paving the way for potent multimodal solutions that meet the needs of the healthcare and life sciences community.

Microsoft Fabric’s healthcare data solutions are the multimodal data estate that powers our AI, integrating diverse data types into a unified environment. This integration facilitates enhanced decision-making and propels patient outcomes by providing comprehensive insights, particularly by combining specialized multimodal AI models with Fabric’s robust data integration tools.

We are also pleased to announce the public preview of healthcare agent service integrated in Microsoft Copilot Studio. This innovative platform is tailored for the rapid, secure, and compliant development of healthcare copilots, incorporating generative AI based on a healthcare-specialized stack.

With healthcare agent service, our customers can create their own copilots, featuring pre-built healthcare specific safeguards and intelligence. Healthcare agent service is specifically designed for the healthcare sector, incorporating a range of responsible AI safeguards, including chat, clinical, and compliance measures ensuring that the development of healthcare copilot agents adheres to the highest standards of safety and regulatory compliance.

This announcement underscores our commitment to empower health and life science organizations with advanced tools that are not only innovative but also responsible and compliant with industry standards.

Furthermore, the Azure Health Data Services de-identification service is set to reach general availability in November. The cloud API service uses natural language processing techniques to find, redact, or surrogate protected health information (PHI) in unstructured text, including clinical notes, doctor/patient text messages, clinical trial data, discharge summaries, and more. By leveraging the Azure Health Data Services de-identification service, healthcare organizations can confidently utilize or share their data for research, analytics, and AI-powered insights without compromising data privacy.

The future of AI in healthcare

As we stand on the brink of a new era in healthcare, the integration of AI and data solutions is not just a possibility but a necessity. The advancements we’ve discussed, from Microsoft’s responsible AI initiatives to the groundbreaking capabilities of Microsoft Fabric and healthcare AI models, are transforming the way we approach patient care, data management, and overall healthcare delivery. These innovations are not only enhancing patient outcomes but also bringing joy back to the practice of medicine by alleviating the burdens on healthcare providers. The time to lead the charge in revolutionizing healthcare is now.

We invite you to join us on this journey. Explore the possibilities with Microsoft’s AI and data solutions and see how they can empower your organization to achieve new heights in patient care and operational efficiency.

Microsoft Cloud for Healthcare is helping your organization shape a healthier future with data and AI

We are excited to strengthen our data and AI investments through the Microsoft Cloud for Healthcare. Our healthcare solutions are built on a foundation of trust and Microsoft’s responsible AI principles. Through these innovations, we are making it easier for our partners and customers to create connected experiences at every point of care, empower their healthcare workforce, and unlock the value from their data using data standards that are important to the healthcare industry.

• Explore more about Azure Health Data Services.
• Learn more about healthcare data solutions in Microsoft Fabric.
• Later this month, visit the Healthcare and Life Sciences Blog to explore the innovative de-identification service that’s transforming how we protect patient privacy.

Microsoft Cloud for Healthcare

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The post Enhancing healthcare with data and responsible AI: New innovations from Microsoft Cloud for Healthcare appeared first on Microsoft AI Blogs.

Growing demand, excessive documentation requirements, and inefficient workflows are all contributing to nurses’ exhaustion, feelings of burnout, and high turnover rates.

Based on recent conversations I’ve had with nursing executives, one thing is crystal clear: to make a real difference for patients and clinicians, innovation needs to flow throughout the care continuum—and the nursing workflow is no exception. It is the most ubiquitous given nurses comprise the largest workforce in healthcare.

With 32% of nurses planning to exit the US workforce this year¹ and the World Health Organization (WHO) predicting a shortage of 4.5 million nurses by 2030,² the urgency to deliver technology to support the nursing profession is felt more than ever.

Nursing info hub

Empower nurses to efficiently document care

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Listening to nurses’ voices

The people who truly understand the intricacies and challenges of nursing workflows are those who live and breathe it every day. To build an innovative solution that’s impactful, nurses and their leadership need to be the guiding light of the process, from design to launch and all the way through to successful adoption by the workforce.

As Lea Ann Arnold, Director of Nursing Informatics at Northwestern Medicine, explains:

“Nurses need to be at the table. They need to be a voice; they need to be able to help technology teams figure out you can’t just put a solution out there. You must get into the actual workflows and understand how nurses work.”

Lea Ann Arnold, Director of Nursing Informatics at Northwestern Medicine

Terry McDonnell, Senior Vice President and Chief Nursing Executive at Duke Health, agrees: “The number one key in ensuring that any technology is going to be successful or useful for nurses is to really involve nurses in the design.”

“Setting nurses up for success and making sure that they are part of the solution is crucial. We cannot expect them to accept a solution just because we think it is the way to do it. We need to bring nurses along with the decision and make sure they feel that they understand how to use the technology,” said Tammy Daniel, Senior Vice President and Chief Nursing Officer at Baptist Health of Northeast Florida. 

AI in the nursing workflow

AI is rapidly evolving technology, and it’s crucial that its powers are extended to nursing workflows. Nurses need to manage their workloads in a way that doesn’t lead to increased stress and burnout, allowing nurses to focus their energy on patient care.

“It’s so critical for our nurses to understand AI,” says Arnold. “Then they start to realize that that adding AI is not meant to replace the human factor, but it’s really meant to augment and take off that work that could be done by a machine.”

Microsoft is committed to empowering customers with cutting-edge tools designed to harness the power of generative AI. Our dedication to innovation continues in our latest investment in industry-specific solutions that enable businesses to adopt and integrate AI technologies swiftly and efficiently. We are seizing the remarkable opportunity that AI presents in healthcare, propelling the industry towards a transformative future.

Creating a purpose-built solution with nurses at the table

Though physicians and nurses work closely together to treat and care for patients, the working day of a nurse looks very different to that of a physician, and nursing documentation workflow is likewise separate and distinct. Nurses are mobile during their shift, moving between rooms to see their patients. They have bedside conversations and capture patient information in highly structured formats such as flowsheets.

Microsoft has been on a multi-year journey to address the challenge of nursing documentation and complex workflows with sophisticated AI to build a solution we’ve deployed at multiple customers.

We gathered feedback from hundreds of frontline nurses, nurse managers, and executives. Our team has spent hours shadowing nurses during their shifts to see how they carry out their tasks and to discover where the greatest points of friction exist throughout their day.

We established the ambient category five years ago for physician documentation and now hundreds of organizations use our AI capabilities for clinical workflow. We are building on that success and defining the standard for AI in nursing. 

“Patients are complex, and we need to think about how we’re going to document differently—because ultimately the value of nursing is back at the bedside, it is not the taxing work of documenting in a flowsheet. With the work that Microsoft has done with providers, I was very excited to be chosen as one of the pilot sites to truly inform that roadmap.”

Gretchen Brown, Chief Nursing Information Officer at Stanford Health Care

We are actively collaborating with several leading healthcare organizations—including Advocate Health, Baptist Health of Northeast Florida, Duke Health, Intermountain Health Saint Joseph Hospital, Mercy, Northwestern Medicine, NYU Langone Health, Stanford Health Care, and Tampa General Hospital—and building an AI solution that addresses nursing documentation by completing flowsheets supported by ambient technology, allowing nurses to focus less on paperwork and more on their patients.​ Many of these organizations have already deployed a preview version as we further optimize the capabilities before making them generally available.

Tapping into our proven track record with Dragon Medical and electronic health record (EHR) embedded workflows, our initial work in this space builds on a longstanding strategic relationship and joint development collaboration with Epic.

Explore the potential of AI for nursing

Nursing is on the brink of significant innovation. We see this with the momentum of virtual nursing, where health systems are investing in technologies focused on supporting nursing care teams. We aim to meet the immediate pain point of documentation and, in time, fundamentally reshape the experience of nurses, helping to evolve that experience into something intuitive and efficient, where the non-patient care work can be facilitated by cutting edge technologies working in the backdrop, returning the human connection of patient and nurse to the forefront.

As I engage in the dialogue between nursing leaders and industry innovators, I have no doubt the future will be transformed. Collectively, we have the ambition and the conviction to point these breakthrough technologies at our most critical problems, in support of some of the most essential members of the healthcare industry: our nurses!

¹Surveyed nurses consider leaving direct patient care at elevated rates, McKinsey, 2022.

²Nursing and midwifery, World Health Organization, 2024.

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