Enhancing Healthcare AI with Model Context Protocol and Semantic Kernel
AI in healthcare isn’t just about chatbots or summarizing clinical notes anymore.
AI in healthcare isn’t just about chatbots or summarizing clinical notes anymore. We’re entering an era where AI must act—connecting to enterprise systems, pulling live data, and executing workflows—all while respecting the complex and high-stakes environment of healthcare. That’s where Microsoft’s Model Context Protocol (MCP) and the Semantic Kernel SDK come in.
If you’re a technical leader in a hospital system or academic medical center, MCP represents a critical evolution in how AI integrates into your enterprise stack.
What is the Model Context Protocol?
MCP is a new open standard from Microsoft (and now embraced by others) that defines how AI models communicate with external systems in real time. Think of it as a “universal adapter” that allows models like GPT-4 to securely and predictably interact with APIs, data sources, and business logic—across any domain.
This is a game-changer for healthcare.
Instead of relying on prompt engineering hacks or custom APIs baked into a closed LLM, MCP gives you auditable, secure, enterprise-ready tools that your AI assistant can use dynamically and consistently.
The Semantic Kernel: Bringing MCP to Life
Microsoft’s Semantic Kernel is an SDK that makes it easier to build intelligent apps that mix traditional code, LLM capabilities, and now—with MCP—real-time tool use. The recent update walks you through how to build an MCP-compliant server using ASP.NET Core and the Semantic Kernel.
The gist: you expose your internal plugins or APIs (EHR services, workforce tools, clinical FTE engines, etc.) as MCP “tools.” The LLM then understands how to discover and invoke them using standardized metadata and descriptions.
In other words, you can now build an AI assistant that knows your health system, follows your rules, and takes real action.
Why Healthcare Needs This
Healthcare IT is famously fragmented. We have EMRs, HR systems, revenue cycle tools, scheduling, credentialing, research compliance… the list goes on. And yet, our physicians and executives expect a Copilot experience—something intelligent, voice-enabled, and truly helpful.
But no Copilot is useful if it can’t get access to your data and processes in real time.
MCP solves for this by creating a safe, well-defined bridge between AI models and operational tools. Combined with the Semantic Kernel, we can quickly wrap legacy systems and modern APIs alike into standardized tools an AI can use without hallucination, overreach, or compliance risk.
At RealActivity, we see enormous potential for MCP in three specific areas:
1. Clinical FTE and Workload Optimization
Hospitals struggle to accurately track provider time and workload. By exposing our Physician Experience Copilot’s underlying data models via MCP, health systems can enable real-time queries and actions from executives and practice managers. Imagine asking, “How many wRVUs did cardiology log this week per FTE?”—and getting a precise answer backed by source data.
2. Research Compliance & Effort Reporting
We’re also piloting MCP-based plugins that connect AI agents to effort reporting systems for NIH compliance. With Semantic Kernel and MCP, we can let researchers and administrators interact with these complex workflows using plain language—while preserving audit trails and policy logic.
3. Operational Voice Assistants
Through mobile or desktop experiences, an MCP-powered Copilot could allow a department chair or CFO to ask, “Show me the top 10 productivity outliers in surgery last month,” or “Send the HR team a report on Dr. Chen’s clinical time split.” All of that is possible, securely, with MCP as the bridge.
Implementation Tips
To get started:
Build an MCP server using the Semantic Kernel blog tutorial.
Expose your key APIs (think: time tracking, payroll, RVU engines, compliance databases) as MCP tools.
Define your metadata carefully. The better your tool descriptions and schemas, the more intelligent and safe the model interactions will be.
Test in a sandbox with your preferred LLM (OpenAI GPT-4, Azure OpenAI, etc.) to refine behavior.
Layer in governance early—role-based access, audit logs, and context boundaries.
Final Thoughts
MCP is the missing piece that allows AI assistants to go from passive responders to active, intelligent participants in healthcare operations. With Semantic Kernel, we now have the framework to integrate AI into complex workflows—without sacrificing control, safety, or trust.
For healthcare systems already exploring Copilots, this is your next move. If your teams are building internal apps or buying from vendors that are not MCP-aware, it’s time to ask them why. Please remember us at RealActivity when you’re ready to start your enterprise AI roadmap.