MCPDischarge — Cross-Department MCP Interoperability

MCPDischarge — Cross-Department MCP Interoperability

EHR × Pharmacy × Billing | RBAC | PHI Boundary | FastMCP

CitiusTech Gen AI & Agentic AI Training — Project 5

The Problem Traditional APIs Cannot Solve

A patient is ready for discharge. Data must flow across three departments that have never shared a common protocol:

Traditional workflow (45 minutes, 15 manual handoffs):
  Ward nurse    → prints discharge note
  Ward nurse    → phones pharmacy to check drug availability
  Pharmacy      → calls back 2 hours later (drug out of stock)
  Nurse         → calls doctor to re-prescribe
  Doctor        → updates chart
  Nurse         → re-contacts pharmacy
  Pharmacy      → dispenses (brand name ≠ generic name — wrong drug dispensed?)
  Nurse         → separately calls billing department
  Billing clerk → manually re-enters ICD-10 codes from printed note
  Billing clerk → can see full medication list including controlled substances (HIPAA risk)
  Patient       → waits, often 4–6 hours post-clinical-readiness

MCP (Model Context Protocol) solves this with a standardised, typed, RBAC-enforced tool call layer:

MCP workflow (< 1 second, automated):
  DischargeAgent.EHR.get_discharge_medications()           ← structured, not free text
  DischargeAgent.Pharmacy.check_stock()                    ← semantic name matching
  DischargeAgent.Pharmacy.get_alternative()                ← out-of-stock resolution
  DischargeAgent.EHR.get_billing_safe_summary()            ← PHI stripped at source
  DischargeAgent.Billing.generate_invoice()                ← billing never sees clinical notes

Architecture

┌────────────────────────────────────────────────────────────────┐
│                 Discharge Coordination Agent                    │
│                   (MCP Client — role: discharge_coordinator)   │
└────────┬───────────────────┬───────────────────┬──────────────┘
         │ MCP calls         │ MCP calls          │ MCP calls
         ▼                   ▼                    ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│  EHR MCP Server │ │ Pharmacy Server  │ │ Billing Server   │
│  (port 8001)    │ │ (port 8002)      │ │ (port 8003)      │
│                 │ │                  │ │                  │
│ Tools:          │ │ Tools:           │ │ Tools:           │
│ • discharge_meds│ │ • check_stock    │ │ • get_charges    │
│ • diagnosis_cod │ │ • get_alternative│ │ • get_insurance  │
│ • billing_safe  │ │ • get_price      │ │ • gen_invoice    │
│   _summary      │ │ • dispense_req   │ │                  │
│ [RBAC enforced] │ │ [RBAC enforced]  │ │ [RBAC enforced]  │
└─────────────────┘ └─────────────────┘ └─────────────────┘

PHI Boundary:
  EHR → Billing path uses get_billing_safe_summary()
  PHI fields blocked: name, DOB, MRN, discharge_note, attending_physician
  Billing receives: ICD-10 codes, LOS, ward — non-PHI operational data only

RBAC Policy Matrix

Every tool call validates the caller's role before returning data. Unauthorised calls raise RBACError and are logged to the telemetry feed.

Quick Start

Step 1: Install Dependencies

pip install -r requirements.txt

Step 2: Generate Data

cd data/
python generate_dataset.py

Step 3: Run the Servers

FastMCP HTTP servers (production-style, required for the async MCP agent):

# Terminal 1:
python src/servers/mcp_servers.py --server ehr

# Terminal 2:
python src/servers/mcp_servers.py --server pharmacy

# Terminal 3:
python src/servers/mcp_servers.py --server billing

Or run all three in one process (starts 3 background threads):

python src/servers/mcp_servers.py --all

Direct Python (no HTTP, for training only):

from src.servers.ehr_server import EHRServer

ehr = EHRServer()
meds = ehr.get_discharge_medications("PAT-001", role="discharge_coordinator")

Step 4: Run Discharge Agent

python src/agents/discharge_agent.py PAT-001
python src/agents/discharge_agent.py PAT-003

Step 5: Full Demo

python demo/demo.py               # Runs 4 scenarios
python demo/demo.py --scenario 3  # RBAC violation only

Chat UI (React)

This repo includes a simple React chat frontend that calls a lightweight FastAPIgateway, which in turn calls the MCP servers.

1) Start MCP servers (SSE)

python src/servers/mcp_servers.py --all

2) Start chat gateway API (port 8000)

copy .env.example .env   # then fill in Azure OpenAI settings (optional)
python -m uvicorn src.gateway.chat_gateway:app --reload --port 8000

3) Start React dev server (port 5173)

cd frontend
npm install
npm run dev

Step 6: Evaluation

cd evaluation/
python eval_dashboard.py

Note: evaluation requires the MCP servers running (Step 3), because it calls the async MCP agent over SSE.

Project Structure

mcpdischarge/
├── data/
│   ├── generate_dataset.py          ← Run this first
│   ├── ehr_patients.json            ← 6 patient records with discharge medications
│   ├── pharmacy_inventory.json      ← 17 drugs (4 out of stock, aliases table)
│   ├── billing_rate_cards.json      ← 15 charge codes
│   ├── insurance_contracts.json     ← 2 insurer contracts
│   ├── patient_insurance_map.json   ← Patient → insurer mappings
│   ├── icd10_billing_codes.json     ← ICD-10 → DRG billing mappings
│   └── rbac_policies.json           ← RBAC matrix (role → server → tools)
│
├── src/
│   ├── servers/
│   │   └── mcp_servers.py           ← EHRServer, PharmacyServer, BillingServer + FastMCP wrappers
│   └── agents/
│       └── discharge_agent.py       ← DischargeCoordinationAgent + WorkflowMetrics
│
├── evaluation/
│   ├── eval_dashboard.py
│   ├── 01_manual_vs_mcp.png
│   ├── 02_rbac_telemetry.png
│   └── 03_data_integrity.png
│
├── demo/
│   └── demo.py                      ← 4 scenarios + 2 limitations
│
├── configs/
│   ├── fastmcp_deployment.md        ← FastMCP HTTP server setup
│   ├── azure_foundry_mcp.md         ← Azure AI Foundry MCP integration
│   └── rbac_design.md               ← RBAC policy design guide
│
└── README.md

Injected Challenge Patterns

The Three MCP Servers (Detailed)

EHR Server

PHI-sensitive tools (clinical roles only):

get_patient_discharge_summary(patient_id, caller_role)  # full clinical note
get_discharge_medications(patient_id, caller_role)       # medication list

PHI-safe tools (all roles including billing):

get_diagnosis_codes(patient_id, caller_role)             # ICD-10 only
get_admission_info(patient_id, caller_role)              # LOS, ward, dates
get_billing_safe_summary(patient_id, caller_role)        # strips PHI fields

PHI stripping (what gets blocked for billing):

PHI_FIELDS = {"name", "dob", "mrn", "discharge_note", "attending_physician"}
# Billing receives: patient_id, ward, admission_date, discharge_date, los_days, diagnosis_icd10

Pharmacy Server

Semantic name resolution:

# EHR says "Dapagliflozin" → Pharmacy stores as "Farxiga"
# MCP alias table: {"farxiga": "PH-001", "dapa": "PH-001", "sglt2 inhibitor": "PH-001"}
drug = _find_drug_by_name("Dapagliflozin")  # → PH-001 (Dapagliflozin)
drug = _find_drug_by_name("Humira")          # → PH-008 (Adalimumab, branded)

Dose conflict detection:

# EHR prescribes Semaglutide 0.5mg, formulary standard is 0.25mg starter
if queried_dose not in formulary_dose:
    dose_conflict = True  # triggers clinical review alert

Semantic match score:

# score = word overlap / max(len(ehr_words), len(pharm_words))
# score < 0.85 → NAME_MISMATCH alert even if drug found
semantic_drug_match_score("Humira", "Adalimumab")  # → 0.0 (no word overlap)
semantic_drug_match_score("Furosemide", "Furosemide")  # → 1.0 (exact)

Billing Server

Invoice generation (PHI guard):

def generate_invoice(patient_id, billing_safe_ehr, drug_costs, ...):
    # Verify PHI is stripped
    for phi_field in PHI_FIELDS:
        if phi_field in billing_safe_ehr:
            raise PermissionError(f"PHI field '{phi_field}' in billing payload")
    # Process invoice using only: ICD-10 + LOS + ward + drug prices

MCP vs Traditional API Comparison

Evaluation Results (6 Patient Discharges)

Total: 72 MCP tool calls | 100% success | 15 manual handoffs replaced per discharge | ~45 minutes saved per case

FastMCP HTTP Deployment

See configs/fastmcp_deployment.md. Key pattern:

from fastmcp import FastMCP

ehr_mcp = FastMCP("EHR-Server")

@ehr_mcp.tool()
def get_discharge_medications(patient_id: str, caller_role: str) -> dict:
    """Get discharge medication list from EHR."""
    return EHRServer().get_discharge_medications(patient_id, caller_role)

# Run as HTTP SSE server
ehr_mcp.run(transport="sse", host="0.0.0.0", port=8001)

Agent connects as MCP client:

from mcp import ClientSession, StdioServerParameters
from mcp.client.sse import sse_client

async with sse_client("http://localhost:8001/sse") as (read, write):
    async with ClientSession(read, write) as session:
        result = await session.call_tool(
            "get_discharge_medications",
            {"patient_id": "PAT-001", "caller_role": "discharge_coordinator"}
        )

Azure AI Foundry Integration

See configs/azure_foundry_mcp.md. MCP servers register as Foundry tools:

from azure.ai.projects.models import McpToolDefinition

mcp_tools = [
    McpToolDefinition(server_url="http://ehr-server:8001/sse", name="ehr-server"),
    McpToolDefinition(server_url="http://pharmacy-server:8002/sse", name="pharmacy-server"),
    McpToolDefinition(server_url="http://billing-server:8003/sse", name="billing-server"),
]

agent = client.agents.create_agent(
    model="gpt-4o",
    name="DischargeCoordinationAgent",
    instructions=DISCHARGE_AGENT_SYSTEM_PROMPT,
    tools=[t.as_tool_definition() for t in mcp_tools],
)

CitiusTech Gen AI & Agentic AI Training Program — Project 5 of 5