Security Copilot Clinic: AI‑Driven Agentic Defense for Healthcare
Healthcare security teams are operating under unprecedented pressure. Ransomware continues to target clinical environments, identity‑based attacks are increasing in sophistication, and the risk of PHI exposure remains a constant concern — all while SOC teams face chronic staffing shortages. Microsoft Security Copilot is now available for organizations using Microsoft 365 E5, bringing generative AI assistance directly into the security tools healthcare teams already rely on. This clinic series is designed to show how Security Copilot changes day‑one operations — turning noisy alerts into clear, actionable investigations and faster containment. Why attend this clinic For healthcare CISOs, SOC leaders, and security architects, Security Copilot represents more than an AI assistant — it’s a shift in how investigations are conducted across endpoint, identity, email, data, and cloud workloads. In this session, you’ll see how Security Copilot helps healthcare security teams: Move faster with confidence by summarizing complex evidence across security signals Reduce investigation fatigue by standardizing analyst workflows Communicate risk clearly by translating technical findings into leadership‑ready insights Protect patient data without adding new tools or headcount All examples and demonstrations are grounded in real healthcare security scenarios. What we’ll explore See the full incident picture in one place Microsoft‑built Security Copilot agents embedded across Defender, Entra, Intune, and Purview automatically correlate signals from endpoint, identity, email, data, and cloud applications into a single investigation view — eliminating manual pivoting between tools. Move from alert to action faster Embedded agents analyze related signals in real time and surface prioritized investigation paths along with recommended containment actions directly in the analyst workflow. Standardize investigations and reduce noise Agent‑driven prompts and investigation structure help standardize analyst response, reduce alert fatigue, and create repeatable workflows that scale in lean SOC environments. Protect PHI and communicate risk with confidence Security Copilot uses embedded data and threat intelligence to produce leadership‑ready summaries that clearly articulate potential PHI exposure, attack progression, and business impact. Session format and audience Format 60‑minute live session End‑to‑end demo Interactive Q&A Who should attend CISOs and Security Leaders SOC Managers and Analysts Security and Cloud Architects Clinical IT and Infrastructure Leaders Upcoming sessions Date Time (ET) Registration March 13, 2026 12:00 – 1:00 PM Session #1 March 20, 2026 12:00 – 1:00 PM Session #2 March 27, 2026 12:00 – 1:00 PM Session #3 Secure healthcare — together Security Copilot enables healthcare organizations to respond faster, investigate smarter, and communicate risk more effectively — all within the Microsoft security ecosystem teams already trust. If you’re evaluating how AI‑driven, agentic defense can support your healthcare SOC, this clinic will give you practical insight you can apply immediately.Dragon Copilot centralizes trusted medical content and relevant contextual information in-workflow
This blog is co-authored by Bert Hoorne, Principal Program Manager & Ksenya Kveler, Principle Medical Science Manager Dragon Copilot delivers medical intelligence from trusted sources directly within clinical workflows for healthcare organizations in one solution. We are pleased to announce that we are expanding those knowledge sources with additional best‑in‑class content providers and enabling broader access to your organization’s internal sources with Microsoft 365 Copilot integration. Access information from new credible medical content providers Dragon Copilot users will gain access to an additional robust collection of trusted clinical content from leading evidence-based resources. We are partnering with renowned publishers to bring you the best, most trusted content, safely and securely, within clinician’s workflows while helping to reduce the use of unauthorized AI tools and applications, commonly referred to, as “shadow AI.” Access content from Wolters Kluwer UpToDate We’ve partnered with Wolters Kluwer UpToDate to bring trusted, evidence-based clinical guidance directly into Dragon Copilot. Customers with an active Wolters Kluwer UpToDate license will be able to access UpToDate content in Dragon Copilot, within the context of their clinical workflows. This integration allows clinicians to ask both general questions and patient specific questions and receive answers grounded in UpToDate evidence, with clear references to supporting sources. Over time, it will also introduce contextual links to UpToDate concepts layered on top of Dragon Copilot–generated notes, further enhancing clinical insight at the point of care. “Clinicians need reliable guidance that supports fast, confident decision-making without disrupting care delivery. We are excited to partner with Microsoft to bring UpToDate’s gold standard evidence and expertise-based clinical insights to Dragon Copilot, helping clinicians quickly access, actionable answers that reduce cognitive burden and support better patient care.” Yaw Fellin, Senior Vice President and General Manager, UpToDate Clinical Decision Support and Provider Solutions Wolters Kluwer Health Here’s an example of UpToDate content embedded in the Dragon Copilot workflow: Obtain trusted clinical evidence with Elsevier ClinicalKey AI Elsevier’s ClinicalKey AI will be available in Dragon Copilot. This integration enables customers with an active Elsevier ClinicalKey AI license to surface trusted medical literature and clinical evidence directly within clinicians’ workflows. “Clinicians are navigating a complex and rapidly changing healthcare landscape and need solutions they can trust. The ClinicalKey AI extension for Dragon Copilot transforms how clinicians interact with trusted medical literature and clinical answers. The conversational interface makes evidence discovery faster and more intuitive.” Jukka Valimaki, SVP Clinical Solutions Elsevier Here’s an example of ClinicalKey AI content embedded in the Dragon Copilot workflow: Support clinical decisions with EBMcalc With the integration of EBMcalc medical calculators, Dragon Copilot enables clinicians to use evidence-based calculators directly within their workflows—applied in context to the patient they’re caring for. “Clinicians need trusted, evidence-based insights exactly at the point of care. By integrating EBMcalc’s rigorously curated clinical calculators and references into Dragon Copilot, we’re helping make high quality medical evidence more accessible, more actionable, and easier to use within everyday clinical workflows”. Louis Leff, MD, MACP, Founder and CEO EBMcalc Access independent evidence in Dragon Copilot with Wiley and Cochrane Wiley and Microsoft are partnering to bring scientific literature and clinical evidence directly into the healthcare workflow, starting with the Cochrane Library. Through this integration, customers with an active Cochrane Library AI license will be able to access Cochrane’s high-quality, independent evidence, systematic reviews, and clinical answers, to inform more reliable and efficient decision-making. This includes the Cochrane Database of Systematic Reviews (CDSR), the home of gold-standard evidence syntheses, widely used to inform clinical guidelines worldwide. "Working with Microsoft to bring the Cochrane Library into Dragon Copilot reflects a shared commitment to meeting researchers and clinicians where they are. Healthcare Institutions can now access independent, peer-reviewed evidence— right within their clinical workflow” Josh Jarrett, SVP & GM of AI Growth Wiley Access work context with Microsoft 365 Copilot in Dragon Copilot With the Microsoft 365 Copilot integration, Dragon Copilot enables clinicians to seamlessly access information from their emails, chats, OneDrive and SharePoint, within the flow of their clinical work. Clinicians can combine this information with additional questions and actions, all governed by existing organizational and user access controls. Use of this data within Dragon Copilot workflow remains fully at the user’s discretion. Here’s an example of content from an email surfaced by Microsoft 365 Copilot accessible through the Dragon Copilot workflow: Read more for a deeper dive on how Dragon Copilot enables work context access with Microsoft 365 Copilot integration. Safe web search Dragon Copilot safe web search delivers trusted, evidence linked answers when curated sources are unavailable—ensuring clinicians continue to receive timely support without disrupting their workflow. The goal of safe web search is to prevent broken workflows and eliminate unsafe external browsing. Clinicians remain within their clinical context, focused on the patient—without tab hopping or the risk of landing on unreliable or unverified websites. Safe web search eliminates “no response” dead ends by maintaining a seamless conversational experience in Dragon Copilot and reducing unanswered prompts. This capability is enabled by using verified, secure, and responsible mechanisms designed for safe clinical experiences. It enforces multilayer protection through evidence validation, provenance linked responses, content filtering, and regulated search with built in safeguards. Here’s an example of content from a safe web search in the Dragon Copilot workflow: Conclusion These advancements represent an important step forward in how Dragon Copilot delivers trusted medical intelligence - bringing together best‑in‑class clinical evidence, organizational knowledge, and safe web access in one governed, in‑workflow experience. We will continue to expand our partner ecosystem, deepen integrations with leading evidence providers, and evolve Dragon Copilot conversational extensibility to meet clinicians where they work.Bringing Organizational Knowledge into the Clinical Workflow
This blog is co-authored by Hadas Bitran, Partner GM, Health AI, Microsoft Health & Life Sciences Every day, clinicians spend valuable time looking for information that lives in different places. An email thread from a specialist colleague. A Microsoft Teams discussion about a complex case. Updated organizational processes buried in SharePoint or OneDrive. This information provides context that could be critical to their workflows or help inform their decisions. But that context is not part of their clinical workflow. The result? Clinicians are forced to break their clinical workflow, searching manually across organizational resources, and mentally combining scattered data points, all while a patient is waiting. This isn't a knowledge problem. It's a retrieval problem. And it's costing time, focus, cognitive burden and clinical confidence every single day. That's exactly the gap we're closing by bringing clinical intelligence and your organization's knowledge into one seamless, workflow-native experience. Clinical workflow, now with your organizational context Within Dragon Copilot, clinicians will be able to securely surface relevant information across Microsoft 365, without leaving the clinical workflow: Email: retrieve relevant information that was exchanged with patients, colleagues or from specialist correspondence, referral communications, or care coordination threads. find me the email from Dr. Ting that mentioned the latest research about this mutation. In this example, the chat functionality in Dragon Copilot uses the patient and encounter context to resolve the referenced mutation, then leverages Microsoft 365 Copilot behind the scenes to locate the email from Dr. Ting that mentions it. Microsoft Teams: surface information from Microsoft Teams chats that the clinician had with colleagues, discussions or group chat conversations. The patient is traveling to Florida. Identify dialysis centers near the patient’s destination based on information shared by Dr. Salomon in Microsoft Teams and provide practical travel guidelines I can share with the patient. In this example, Dragon Copilot uses trusted sources for travel guidelines and Microsoft 365 Copilot to retrieve relevant Microsoft Teams messages from Dr. Salomon, identifying nearby dialysis centers in Florida. SharePoint and OneDrive: access organizational knowledge on demand: HR policies, facility procedures, compliance guidelines, shift schedules, and more Who is on call for nephrology tonight and who is covering tomorrow morning? In this example, Dragon Copilot leverages Microsoft 365 Copilot behind the scenes to locate the most up‑to‑date Excel file with upcoming shift and coverage information from the hospital’s SharePoint, and surfaces the answer directly in the conversation, without disrupting the clinician’s workflow. With Microsoft 365 Copilot, work context is available directly inside Dragon Copilot, clinicians can choose if, and when to access their work information. Within Dragon Copilot, they can ask questions in natural language and receive the most relevant information, grounded in patient context, from trusted clinical sources and their Microsoft 365 data. One conversational flow. Full clinical and work context. No tab switching, no manual searching, no lost focus. Trusted by design, built for healthcare Security and privacy are built in from the ground up. Information is always accessed on behalf of the individual user, fully respecting existing Microsoft 365 identity and access management, compliance, and privacy controls, meaning clinicians see only what they're authorized to see, and that Dragon Copilot will only use their work context if the clinician consented to it. This also means no new security risks to manage, and no changes to how your organization governs access to information. For healthcare organizations where data sensitivity, regulatory compliance, and patient privacy are non-negotiable, this better-together experience is designed to meet that bar from day one. Join the Private Preview If you're a Dragon Copilot customer, and your organization is using Microsoft 365 Copilot, we invite you to be among the first to experience this new capability. Register now for early access to the private preview and play a role in shaping the future of clinical workflow intelligence. Register for private preview
Why nursing needs a different kind of AI—and how Dragon Copilot delivers
The Dragon Copilot experience for nurses was made generally available (GA) in December 2025 with a clear mission: help nursing staff focus on care, not the computer. From the start, the goal was to create a comprehensive AI clinical assistant—one that works alongside nurses throughout their shift, reduces cognitive load, captures the full scope of care delivered, and translates real clinical work into automated next steps, including documentation—fundamentally transforming workflows to keep patient care at the center. Microsoft has continued to execute on that vision. Recent enhancements include extended mobile access with Android support—enabling nurses to record care in Epic Rover on Android devices—as well as significant expansion in ambient documentation coverage. Together, these advances reflect a consistent approach: adoption follows when technology aligns with how nurses work. Expansive nursing documentation coverage Nursing work spans multiple flowsheet templates, assessments, state changes, and, at times, narrative notes. When solutions support only a subset of this work, nurses are left filling gaps after the fact—reintroducing cognitive load and eroding the value of this technology. Microsoft has expanded Dragon Copilot’s ambient documentation capabilities by broadening the range of supported nursing value types—and by extending it to deliver complete coverage across all flowsheet templates in supported departments and settings. The result is comprehensive documentation generated from each recording including: Lines, Drains, Airways, and Wounds (LDAs) documentation, including assessments, additions, and removals Nurse notes, automatically generated from natural nurse-patient conversations and voice memos captured on the go Full flowsheet template coverage—not just a subset—including admission and discharge flowsheets, blood administration, CIWA-Ar, and care plan-related flowsheets Adaptations to each organizations charting philosophy, including macros support, chart-by-exception, pertinent positives, and more This breadth matters because nursing work is rarely captured within only a narrow set of flowsheets—nor does it typically result in just narrative notes. Yet many solutions labeled “for nurses” prioritize what is easiest to automate, rather than what nurses need. The result can be a false sense of completeness, with nurses still managing gaps across their shift. Why nursing ambient documentation is hard—and what makes Dragon Copilot unique Achieving comprehensive, high‑quality nursing documentation has required specialized technology designed to address the structural, workflow, and feedback challenges unique to nursing—challenges that general narrative ambient models and physician‑oriented solutions are not built to solve: Flowsheets are messy, complex, and frequently changing Flowsheets are large, hospital-specific, internally ambiguous, and constantly evolving under governance. Complex logic—such as cascading rows, documentation‑by‑exception patterns, and duplicative or overlapping rows—makes it far from straightforward to accurately map a clinical observation to the correct field and value. Microsoft works directly with real hospital schemas, handling hierarchy, ambiguity, and multiple valid documentation destinations—without requiring flowsheet redesign or sacrificing quality. Nurses don’t speak for documentation Bedside language is optimized for care delivery, not chart completeness. Critical details are often implied or never spoken aloud. Microsoft’s technology translates natural nursing communication into accurate documentation without changing nurse behavior. Built on industry‑leading transcription accuracy and decades of speech recognition expertise, Dragon Copilot is informed by real‑world integration across diverse EHR environments, preserving accurate translation and clinical intent that directly impact downstream documentation accuracy. Nursing audio is diverse Recordings mix shorthand, dialogue, monologue, and unit-specific language. Dragon Copilot accounts for mixed speaking modes instead of flattening audio through a generic pipeline or requiring nurses to speak in constrained ways. Feedback loops are noisy Nurse corrections to AI output often reflect hindsight or personal preferences rather than model error. Microsoft’s approach analyzes correction patterns with clinical context, enabling calibration at the institution, department, and even individual user level. Bedside workflows demand predictability Baseline LLMs are not suited for real-world nursing accuracy, latency, and cost requirements — especially with tens-of-thousands of possible flowsheet values. Dragon Copilot is optimized for consistent performance across real nursing environments, exceeding the reliability and latency characteristics of baseline models. Beyond specialized nursing architecture, Dragon Copilot enforces strict quality and safety gates for new documentation outputs—including oversight by Microsoft’s internal, nurse-led Clinical Integrity team, phased validation, and Responsible AI review—ensuring new documentation covered meets defined nursing standards before being introduced at scale. Dragon Copilot represents a fundamental shift in how nursing work is supported by AI by meeting the full complexity of bedside care head-on. By delivering comprehensive ambient documentation across live inpatient care environments, Dragon Copilot helps ensure that the care nurses provide is accurately captured, trusted, and usable downstream. The result is an AI clinical assistant that keeps nurses focused on what matters most: their patients.Can you use AI to implement an Enterprise Master Patient Index (EMPI)?
The Short Answer: Yes. And It's Better Than You Think. If you've worked in healthcare IT for any length of time, you've dealt with this problem. Patient A shows up at Hospital 1 as "Jonathan Smith, DOB 03/15/1985." Patient B shows up at Hospital 2 as "Jon Smith, DOB 03/15/1985." Patient C shows up at a clinic as "John Smythe, DOB 03/15/1985." Same person? Probably. But how do you prove it at scale — across millions of records, dozens of source systems, and data quality that ranges from pristine to "someone fat-fingered a birth year"? That's the problem an Enterprise Master Patient Index (EMPI) solves. And traditionally, it's been solved with expensive commercial products, rigid rule engines, and a lot of manual review. We built one with AI. On Azure. With open-source tooling. And the results are genuinely impressive. This post walks through how it works, what the architecture looks like, and why the combination of deterministic matching, probabilistic algorithms, and AI-enhanced scoring produces better results than any single approach alone. 1. Why EMPI Still Matters (More Than Ever) Healthcare organizations don't have a "patient data problem." They have a patient identity problem. Every EHR, lab system, pharmacy platform, and claims processor creates its own patient record. When those systems exchange data via FHIR, HL7, or flat files, there's no universal patient identifier in the U.S. — Congress has blocked funding for one since 1998. The result: Duplicate records inflate costs and fragment care history Missed matches mean clinicians don't see a patient's full medical picture False positives can merge two different patients into one record — a patient safety risk Traditional EMPI solutions use deterministic matching (exact field comparisons) and sometimes probabilistic scoring (fuzzy string matching). They work. But they leave a significant gray zone of records that require human review — and that queue grows faster than teams can process it. What if AI could shrink that gray zone? 2. The Architecture: Three Layers of Matching Here's the core insight: no single matching technique is sufficient. Exact matches miss typos. Fuzzy matches produce false positives. AI alone hallucinates. But layer them together with calibrated weights, and you get something remarkably accurate. Let's break each layer down. 3. Layer 1: Deterministic Matching — The Foundation Deterministic matching is the bedrock. If two records share an Enterprise ID, they're the same person. Full stop. The system assigns trust levels to each identifier type: Identifier Weight Why Enterprise ID 1.0 Explicitly assigned by an authority SSN 0.9 Highly reliable when present and accurate MRN 0.8 System-dependent — only valid within the same healthcare system Date of Birth 0.35 Common but not unique — 0.3% of the population shares any given birthday Phone 0.3 Useful signal but changes frequently Email 0.3 Same — supportive evidence, not proof The key implementation detail here is MRN system validation. An MRN of "12345" at Hospital A is completely unrelated to MRN "12345" at Hospital B. The system checks the identifier's source system URI before considering it a match. Without this, you'd get a flood of false positives from coincidental MRN collisions. If an Enterprise ID match is found, the system short-circuits — no need for probabilistic or AI scoring. It's a guaranteed match. 4. Layer 2: Probabilistic Matching — Where It Gets Interesting This is where the system earns its keep. Probabilistic matching handles the messy reality of healthcare data: typos, nicknames, transposed digits, abbreviations, and inconsistent formatting. Name Similarity The system uses a multi-algorithm ensemble for name matching: Jaro-Winkler (60% weight): Optimized for short strings like names. Gives extra credit when strings share a common prefix — so "Jonathan" vs "Jon" scores higher than you'd expect. Soundex / Metaphone (phonetic boost): Catches "Smith" vs "Smythe," "Jon" vs "John," and other sound-alike variations that string distance alone would miss. Levenshtein distance (typo detection): Handles single-character errors — "Johanson" vs "Johansn." These scores are blended, and first name and last name are scored independently before combining. This prevents a matching last name from compensating for a wildly different first name. Date of Birth — Smarter Than You'd Think DOB matching goes beyond exact comparison. The system detects month/day transposition — one of the most common data entry errors in healthcare: Scenario Score Exact match 1.0 Month and day swapped (e.g., 03/15 vs 15/03) 0.8 Off by 1 day 0.9 Off by 2–30 days 0.5–0.8 (scaled) Different year 0.0 This alone catches a category of mismatches that pure deterministic systems miss entirely. Address Similarity Address matching uses a hybrid approach: Jaro-Winkler on the normalized full address (70% weight) Token-based Jaccard similarity (30% weight) to handle word reordering Bonus scoring for matching postal codes, city, and state Abbreviation expansion — "St" becomes "Street," "Ave" becomes "Avenue" 5. Layer 3: AI-Enhanced Matching — The Game Changer This is where the architecture diverges from traditional EMPI solutions. OpenAI Embeddings (Semantic Similarity) The system generates a text embedding for each patient's complete demographic profile using OpenAI's text-embedding-3-small model. Then it computes cosine similarity between patient pairs. Why does this work? Because embeddings capture semantic relationships that string-matching can't. "123 Main Street, Apt 4B, Springfield, IL" and "123 Main St #4B, Springfield, Illinois" are semantically identical even though they differ character-by-character. The embedding score carries only 10% of the total weight — it's a signal, not a verdict. But in ambiguous cases, it's the signal that tips the scale. GPT-5.2 LLM Analysis (Intelligent Reasoning) For matches that land in the human review zone (0.65–0.85), the system optionally invokes GPT-5.2 to analyze the patient pair and provide structured reasoning: { "match_score": 0.92, "confidence": "high", "reasoning": "Multiple strong signals: identical last name, DOB matches exactly, same city. First name 'Jon' is a common nickname for 'Jonathan'.", "name_analysis": "First name variation is a known nickname pattern.", "potential_issues": [], "recommendation": "merge" } The LLM doesn't just produce a number — it explains why it thinks two records match. This is enormously valuable for the human reviewers who make final decisions on ambiguous cases. Instead of staring at two records and guessing, they get AI-generated reasoning they can evaluate. When LLM analysis is enabled, the final score blends traditional and LLM scores: Final Score = (Traditional Score × 0.8) + (LLM Score × 0.2) The LLM temperature is set to 0.1 for consistency — you want deterministic outputs from your matching engine, not creative ones. 6. The Graph Database: Modeling Patient Relationships Records and scores are only half the story. The real power comes from how the system stores and traverses relationships. We use Azure Cosmos DB with the Gremlin API — a graph database that models patients, identifiers, addresses, and clinical data as vertices connected by typed edges. (:Patient)──[:HAS_IDENTIFIER]──▶(:Identifier) │ ├──[:HAS_ADDRESS]──▶(:Address) │ ├──[:HAS_CONTACT]──▶(:ContactPoint) │ ├──[:LINKED_TO]──▶(:EmpiRecord) ← Golden Record │ ├──[:POTENTIAL_MATCH {score, confidence}]──▶(:Patient) │ └──[:HAS_ENCOUNTER]──▶(:Encounter) └──[:HAS_OBSERVATION]──▶(:Observation) Why a Graph? Three reasons: Candidate retrieval is a graph traversal problem. "Find all patients who share an identifier with Patient X" is a natural graph query — traverse from the patient to their identifiers, then back to other patients who share those same identifiers. In Gremlin, this is a few lines. In SQL, it's a multi-table join with performance that degrades as data grows. Relationships are first-class citizens. A POTENTIAL_MATCH edge stores the match score, confidence level, and detailed breakdown directly on the relationship. You can query "show me all high-confidence matches" without any joins. EMPI records are naturally hierarchical. A golden record (EmpiRecord) links to multiple source patients via LINKED_TO edges. When you merge two patients, you're adding an edge — not rewriting rows in a relational table. Performance at Scale Cosmos DB's partition strategy uses source_system as the partition key, providing logical isolation between healthcare systems. The system handles Azure's 429 rate-limiting with automatic retry and exponential backoff, and uses batch operations for bulk loads to avoid RU exhaustion. 7. FHIR-Native Data Ingestion The system ingests HL7 FHIR R4 Bundles — the emerging interoperability standard for healthcare data exchange. Each FHIR Bundle is a JSON file containing a complete patient record: demographics, encounters, observations, conditions, procedures, immunizations, medication requests, and diagnostic reports. The FHIR loader: Maps FHIR identifier systems to internal types (SSN, MRN, Enterprise ID) Handles all three FHIR date formats (YYYY, YYYY-MM, YYYY-MM-DD) Extracts clinical data for comprehensive patient profiles Uses an iterator pattern for memory-efficient processing of thousands of patients Tracks source system provenance for audit compliance This means the service can ingest data directly from any FHIR-compliant EHR — Epic, Cerner, MEDITECH, or Synthea-generated test data — without custom integration work. 8. The Conversational Agent: Matching via Natural Language Here's where it gets fun. The system includes a conversational AI agent built on the Azure AI Foundry Agent Service. It's deployed as a GPT-5.2-powered agent with OpenAPI tools that call the matching service's REST API. Instead of navigating a complex UI to find matches, a data steward can simply ask: "Search patients named Aaron" "Compare patient abc-123 with patient xyz-456" "What matches are pending review?" "Approve the match between patient A and patient B" The agent is integrated directly into the Streamlit dashboard's Agent Chat tab, so users never leave their workflow. Under the hood, when the agent decides to call a tool (like "search patients"), Azure AI Foundry makes an HTTP request directly to the Container App API — no local function execution required. Available Agent Tools Tool What It Does searchPatients Search patients by name, DOB, or identifier getPatientDetails Get detailed patient demographics and history findPatientMatches Find potential duplicates for a patient compareTwoPatients Side-by-side comparison with detailed scoring getPendingReviews List matches awaiting human decision submitReviewDecision Approve or reject a match getServiceStatistics MPI dashboard metrics This same tool set is also exposed via a Model Context Protocol (MCP) server, making the matching engine accessible from AI-powered IDEs and coding assistants. 9. The Dashboard: Putting It All Together The Patient Matching Service includes a full-featured Streamlit dashboard for operational management. Page What You See Dashboard Key metrics, score distribution charts, recent match activity Match Results Filterable list with score breakdowns — deterministic, probabilistic, AI, and LLM tabs Patients Browse and search all loaded patients with clinical data Patient Graph Interactive graph visualization of patient relationships using streamlit-agraph Review Queue Pending matches with approve/reject actions Agent Chat Conversational AI for natural language queries Settings Configure match weights, thresholds, and display preferences The match detail view provides six tabs that walk reviewers through every scoring component: Summary, Deterministic, Probabilistic, AI/Embeddings, LLM Analysis, and Raw Data. Reviewers don't just see a number — they see exactly why the system scored a match the way it did. 10. Azure Architecture The full solution runs on Azure: Service Role Azure Cosmos DB (Gremlin + NoSQL) Patient graph storage and match result persistence Azure OpenAI (GPT-5.2 + text-embedding-3-small) LLM analysis and semantic embeddings Azure Container Apps Hosts the FastAPI REST API Azure AI Foundry Agent Service Conversational agent with OpenAPI tools Azure Log Analytics Centralized logging and monitoring The separation between Cosmos DB's Gremlin API (graph traversal) and NoSQL API (match result documents) is intentional. Graph queries excel at relationship traversal — "find all patients connected to this identifier." Document queries excel at filtering and aggregation — "show me all auto-merge matches from the last 24 hours." 11. What We Learned AI doesn't replace deterministic matching. It augments it. The three-layer approach works because each layer compensates for the others' weaknesses: Deterministic handles the easy cases quickly and with certainty Probabilistic catches the typos, nicknames, and formatting differences that exact matching misses AI provides semantic understanding and human-readable reasoning for the ambiguous middle ground The LLM is most valuable as a reviewer's assistant, not a decision-maker. We deliberately keep the LLM weight at 20% of the final score. Its real value is the structured reasoning it produces — the "why" behind a match score. Human reviewers process cases faster when they have AI-generated analysis explaining the matching signals. Graph databases are naturally suited for patient identity. Patient matching is fundamentally a relationship problem. "Who shares identifiers with whom?" "Which patients are linked to this golden record?" "Show me the cluster of records that might all be the same person." These are graph traversal queries. Trying to model this in relational tables works, but you're fighting the data model instead of leveraging it. FHIR interoperability reduces integration friction to near zero. By accepting FHIR R4 Bundles as the input format, the service can ingest data from any modern EHR without custom connectors. This is a massive practical advantage — the hardest part of any EMPI project is usually getting the data in, not matching it. 12. Try It Yourself The Patient Matching Service is built entirely on Azure services and open-source tooling https://github.com/dondinulos/patient-matching-service : Python with FastAPI, Streamlit, and the Azure AI SDKs Azure Cosmos DB (Gremlin API) for graph storage Azure OpenAI for embeddings and LLM analysis Azure AI Foundry for the conversational agent Azure Container Apps for deployment Synthea for FHIR test data generation The matching algorithms (Jaro-Winkler, Soundex, Metaphone, Levenshtein) use pure Python implementations — no proprietary matching engines required. Whether you're building a new EMPI from scratch or augmenting an existing one with AI capabilities, the three-layer approach gives you the best of all worlds: the certainty of deterministic matching, the flexibility of probabilistic scoring, and the intelligence of AI-enhanced analysis. Final Thoughts Can you use AI to implement an EMPI? Yes. And the answer isn't "replace everything with an LLM." It's "use AI where it adds the most value — semantic understanding, natural language reasoning, and augmenting human reviewers — while keeping deterministic and probabilistic matching as the foundation." The combination is more accurate than any single approach. The graph database makes relationships queryable. The conversational agent makes the system accessible. And the whole thing runs on Azure with FHIR-native data ingestion. Patient matching isn't a solved problem. But with AI in the stack, it's a much more manageable one. Tags: Healthcare, Azure, AI, EMPI, FHIR, Patient Matching, Azure Cosmos DB, Azure OpenAI, Graph Database, InteroperabilityManaging data sharing and access in healthcare systems
I am looking for general guidance on how healthcare teams manage data sharing and user access across different systems. I am interested in understanding common approaches for keeping data secure while still allowing the right staff to access what they need. This is more about best practices and real-world experience rather than a specific product issue. Any insights from similar healthcare environments would be helpful.Healthcare agent service in Microsoft Copilot Studio is now Generally Available
Healthcare organizations continue to face immense challenges: workforce shortages, rising costs, and growing demands for patient care. The clinical staff is overburdened, leading to stress, burnout, and staff shortages. Generative AI presents a powerful opportunity when it can automate administrative workflows, surface relevant insights, and assist the clinical staff with contextual, credible and up-to-date information. With that opportunity, we are excited to announce General Availability (GA) of healthcare agent service in Microsoft Copilot Studio. Building responsible, AI-powered healthcare agents With healthcare agent service, organizations can create healthcare-specialized AI applications that use generative AI within a framework that promotes trust, compliance, and real-world clinical scenarios. Agents combine built-in credible medical sources, such as FDA, CDC, MedlinePlus, MSD Manuals, DailyMed and more, with the organization’s own knowledge sources and plugins, while leveraging healthcare-specific actions. Customers can define the intended healthcare roles, such as healthcare professionals or patients, so the behavior is relevant and appropriate for the audience and use case. Pre-built use cases include clinical documentation assistance, patient self-service, helping healthcare professionals triage by organizing information, finding medication information, accessing recent clinical guidelines information, and more. Because responsible AI in healthcare is a top priority, healthcare agent service is infused with safeguards that are reinforced by a healthcare-adapted orchestrator optimized for safety. Clinical, chat, and compliance safeguards help keep interactions evidence-based and trustworthy, increasing the reliability and accuracy of generated responses and adherence to the highest standards of safety, privacy, and regulatory compliance. Healthcare agent service underscores our ongoing commitment to responsible AI in healthcare, by offering customers a reliable, production-ready foundation for healthcare solutions that can be used to help support patients and medical professionals. Extending Dragon Copilot with conversational solutions Healthcare agent service provides a framework for building conversational AI applications that can be integrated directly into Dragon Copilot, giving partners and healthcare organizations the ability to extend its functionality in a scalable, compliant way. Today, Information Assist in Dragon Copilot, built on healthcare agent service, delivers safeguarded generative AI answers grounded in trusted sources and enriched with patient history and context, ensuring clinicians receive accurate, timely, and context-aware insights. Clinicians can effortlessly access a broad range of clinical topics directly within their workflow using natural language, surfacing insights from leading, trusted healthcare content partners that promote more informed clinical decisions with less administrative work. Partners and healthcare organizations can use healthcare agent service to create tailored solutions with built-in safeguards that help ensure output meets healthcare standards and supports safe decision-making at the point of care. These solutions can be integrated directly into Dragon Copilot to enhance both clinical and financial performance. Real-world impact with customers Healthcare organizations are already adopting healthcare agent service to bring generative AI into real-world care settings. Early adopters are seeing meaningful impact in reducing administrative burden, improving patient experience, and empowering clinicians with trusted information. Bayer Pharmaceuticals has recently worked with Microsoft to enable new agentic AI workflows for drug submission using healthcare agent service in Copilot Studio: “We have collaborated with Microsoft to build an AI-powered multi-agent decision board using the healthcare agent service in Copilot Studio. This multi-agent decision board revolutionizes how we strategize drug submissions, pricing, and patient targeting for global market access. By simulating expert board discussions and synthesizing diverse data—from regulatory approvals to health economics and real-world evidence—the system streamlines the complex process of securing drug reimbursement. Healthcare agent service helped us get results quicker, empowering teams to make smarter, data-driven decisions without replacing human expertise, which would enable better access to life-changing therapies for patients worldwide. Importantly, this tool is not limited to pharmaceutical companies. It also supports decision-making for health authorities, NGOs, and other stakeholders across the healthcare ecosystem—enabling more informed, collaborative, and impactful choices that benefit public health at large.” — Shay Zohar, local Market Access Director and member of Bayer Pharmaceutical’s global Early Access team Allgemeines Krankenhaus (AKH) Wien, the largest hospital in Vienna, Austria and the Medical University of Vienna collaborated with Microsoft to extend Dragon Copilot with healthcare agent service, to automate pre-anesthesia intake. “Transforming pre-anesthesia assessments with AI agents for greater efficiency has a great potential to decrease the administrative burden on anesthesiologists. In this project we used healthcare agent service to extend Dragon Copilot with AI-powered agents that automate pre-anesthesia intake to enhance clinical documentation, significantly reducing the administrative workload for anesthesiologists. By orchestrating conversational and workflow agents, the solution interacts with patients, completes assessments, checks for data conflicts, and generates clinical notes, all consolidated for physician review in Dragon Copilot.” — Dr. Oliver Kimberger, Professor for Perioperative Information Management at the Department of General Anesthesia and Intensive Care Medicine, AKH Wien. Empowering healthcare innovation Healthcare agent service offers a low-code interface for building and deploying custom AI solutions with chat, compliance and clinical safeguards that support safety and accuracy in generative AI. With seamless integration and the ability to extend the capabilities of Dragon Copilot, you gain the flexibility to tailor solutions to your organization’s evolving needs. Learn more in healthcare agent service in Copilot Studio documentation Explore the possibilities with Microsoft Copilot Studio Expand your knowledge about Microsoft for Healthcare Discover how we are shaping the future of health with cutting-edge solutions and collaborative efforts here 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 Disclaimer: 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 mitigateHow Microsoft Dragon Copilot Uses The Azure Health Data Services De-Identification Service
Empowering physician productivity through secure AI Microsoft developed Dragon Copilot to revolutionize real-time clinical documentation. Using clinically adapted generative AI, it listens to patient-clinician conversations and automatically generates draft clinical notes, freeing physicians to focus on what matters most: their patients. Dragon Copilot also allows clinicians to get the information they need when they need it and automates many other tasks such as initiating orders or writing draft patient after-visit summaries. The tool eliminates the burden of manual note-taking and multiple other clicks in the EMR, boosting efficiency, and reducing burnout, all of which are critical challenges in healthcare. With strong market traction across hospitals and physician practices across the USA, Dragon Copilot, previously known as Dragon Ambient eXperience (DAX) Copilot, has become a trusted productivity engine for healthcare organizations. In a field where protecting patient data is critical , privacy is paramount. Dragon Copilot’s deep commitment to data privacy, however, requires a strategic partner like the de-identification service to support safe and responsible AI development at scale. How the Azure Health Data Services de-identification service empowers Dragon Copilot Dragon Copilot operates at the intersection of audio capture, natural language generation (NLG), and clinical workflows. Its data pipelines include highly sensitive patient health information. As a result, Microsoft has invested in the Azure Health Data Services de-identification service to de-identify millions of patient transcripts and notes to uphold strict privacy standards and deliver secure, scalable clinical documentation. De-identifying unstructured text like clinical notes is particularly challenging due to the complexity and variability of how Protected Health Information (PHI) appears in real-world clinical documentation. References to dates like “Christmas” or “New Year’s Eve,” names, locations, and other identifiers are often embedded in free text in unpredictable ways. The Azure Health Data Services de-identification service is purpose-built to handle these nuances. It accurately identifies and replaces patient names while distinguishing them from doctors’ names, and it can also detect and tag the names of family members or close contacts mentioned in the clinical narrative. The service also retains the format of the dates presents in clinical notes, shifting them by a random number within a 45-day window and surrogates holidays with replacements close in seasonality. A key strength of the de-identification service is its use of surrogation, where sensitive terms are replaced with realistic, context-appropriate substitutes. This approach, used in services like Dragon Copilot, helps ensure clinical notes remain readable and useful while concealing real PHI in plain sight, strengthening privacy without sacrificing usability. Connecting to Microsoft Fabric for scalable analytics Once Dragon Copilot generates draft clinical notes, the data can be securely ingested into Microsoft Fabric, a unified data platform built for analytics and governance. Within Fabric, healthcare organizations can centralize and manage de-identified data using OneLake, making it accessible for advanced analytics, operational reporting, and research. Azure Health Data Services play a critical role in this ecosystem by ensuring that sensitive PHI is de-identified before analysis, allowing healthcare agents to extract meaningful insights, identify trends, and optimize care delivery without compromising patient privacy. Use Cases unlocked through partnering with the Azure Health Data Services de-identification service Azure Health Data Services de-identification has become a critical component of the Dragon Copilot data ingestion pipeline. Our service supports several teams within Dragon Copilot: Research Enablement: De-identified data fuels AI model building, success tracking, and product improvement—without exposing sensitive patient data. AI Model Quality & Evaluation: De-identified data supports safe iteration and experimentation while preserving important context (i.e. gender, timeline, and more). What makes Azure Health Data Services de-identification service stand out Dragon Copilot builds on the consistency, robustness, and seamless integration offered by Azure Health Data Services' de-identification capabilities. This service is purpose-built for healthcare and plays a critical role in enabling Dragon Copilot to uphold the highest privacy standards while continuing to innovate. Key strengths of the service include: Context Preservation: Maintains formatting and context alignment, which are essential for clinical accuracy. Surrogation Support: Replaces PHI with realistic pseudonyms to ensure de-identified data remains useful for model training. Beyond HIPAA Compliance: De-identifies 27 categories of PHI, surpassing HIPAA’s 18 identifiers, to support more comprehensive privacy protection. This foundation allows Dragon Copilot to scale responsibly, ensuring both compliance and usability in real-world clinical settings. Looking Ahead: Where Dragon Copilot is going with de-identification As Dragon Copilot expands and continues to add new capabilities, Azure Health Data Services de-identification service will continue to be a foundational piece of their AI development lifecycle. For Dragon Copilot, de-identification isn’t just a checkbox, it’s a catalyst for innovation. Learn more about the Azure Health Data Services De-identification service
