One of the biggest challenges for LLMs in healthcare isn’t the models themselves but connecting them to real-world data. The Model Context Protocol (MCP) solves this by acting as a standardized, universal connector, often described as a USB-C port for LLM applications. This allows LLMs to go beyond their internal knowledge and interact directly with external systems like Electronic Health Records (EHRs) and other clinical data sources.
Key Components of the MCP
MCP works by defining a clear way for an LLM to access three critical components:
- Tools: These are the “action” functions that an LLM can call to perform specific tasks. In a clinical setting, this means functions that can make calls to a FHIR server to retrieve a patient’s lab results or generate a database query.
- Resources: This is the “data” that provides context to the LLM. This includes structured data like FHIR resources (e.g., Patient, Observation, MedicationRequest) and responses from API calls.
- Prompts: These are the “structure” templates that guide the LLM’s interactions and help it format its responses in a desired way.
Real-World Applications in Clinical Workflow
By creating a FHIR-based MCP server, we can build a powerful intermediary that translates natural language requests from an LLM into precise FHIR API calls. This allows for truly transformative applications:
- Natural Language Interaction with EHRs: A provider could simply ask a question like, “What were the last three blood pressure readings for Patient A?” The LLM, using MCP, would call a tool to query the FHIR server and present the information clearly, limiting the need to navigate complex menus.
- LLM Agents for Quality Improvement: An LLM agent connected via MCP could actively monitor streams of FHIR data to identify anomalies, like a patient’s blood sugar levels consistently being out of range, and then trigger an alert or suggest a process change.
In short, the Model Context Protocol moves LLM from being a passive knowledge base to an active, integrated tool that can streamline workflows, improve data access, and enhance the quality of care.