Patent-Pending Technology

Model Context Protocol

Unifying institutional knowledge across healthcare systems through an intelligent middleware that delivers real-time operational guidance with full auditability.

What is MCP?

The Model Context Protocol (MCP) is a revolutionary middleware architecture that transforms how institutional knowledge is accessed and applied in healthcare settings. It serves as a central hub for proprietary rule sets, workflows, and domain expertise that are typically siloed across different systems.

MCP extracts structured context from natural language inputs, then routes these contexts to the appropriate institutional knowledge tools. Each tool processes the context independently, and the results are combined into a coherent, actionable recommendation with complete provenance tracking.

Institutional Knowledge

Encapsulates proprietary expertise in billing rules, scheduling constraints, and treatment protocols as first-class data sources.

Deterministic Results

Delivers policy-based outputs with line-by-line auditability and explanation for every recommendation.

Operational Guidance

Provides real-time decision support at the point of patient intake and throughout the care journey.

Natural Language Input Patient notes, clinical requests Context Frame Extraction Structured data parsing Orchestrator Tool selection & routing Billing Tool CPT codes, prior auth Scheduling Tool Resource allocation Protocol Tool Treatment guidelines Aggregated Results With provenance tracking

Basic Usage Example

MCP provides a simple CLI interface for processing patient intake notes, retrieving institutional knowledge, and viewing provenance traces:

bash
$ ./src/index.js intake \
  --note "12-year-old ♂ referred for treatment-resistant depression, Aetna PPO" \
  --preferred-date 2025-06-03

The system extracts a structured ContextFrame containing patient age, sex, diagnosis, insurer, and requested date. It then invokes the appropriate institutional knowledge tools (billing, scheduling, treatment protocol) and returns aggregated guidance:

output
╭─ Aggregated Institutional Guidance ────────────────────────────────────────────╮
│ 💳  BillingAdvisor         CPT 90867 (initial TMS session)                      │
│                          Prior auth: required  ❑ not yet requested             │
│                          Internal cost model: 45-minute use; reimbursement $    │
│                          312.14   (Aetna region-Northeast)                      │
│ 📅  SchedulingEngine      Earliest slot: 2025-06-04 10:00  (TMS Room 2)         │
│                          Technician: Rivera-RN  |  Child-life support needed    │
│ 📜  TxProtocol-Peds-MDD   Stimulation target: L-DLPFC, 10 Hz, 3 000 pulses      │
│                          Concomitant meds check: sertraline ≤100 mg ok          │
╰──────────────────────────────────────────────────────────────────────────────────╯
(Provenance hashes: ba7e10-bill • 83c1d9-sched • c451af-proto)

Each recommendation is backed by a cryptographic provenance hash that can be traced to view the complete audit trail:

bash
$ ./src/index.js trace ba7e10-bill

Patent Strategy for Legal Review

Patentability Framework

The patent draft frames MCP not as an abstract business scheme but as a concrete technical solution to interoperability and auditability in healthcare middleware. This positioning is essential for overcoming §101 Alice challenges.

Independent Claims Structure

The first-filed independent claims recite a hub-and-spoke orchestrator that:

  • Parses a ContextFrame from clinical input
  • Dynamically selects and invokes ToolDescriptors based on capability declarations
  • Appends each result to a cryptographic provenance ledger with content-addressing

Dependent Claims Strategy

Dependent claims carve out specific technical implementations to ensure robust fallback positions:

  • Specific hashing algorithms for the provenance ledger
  • Capability-ranking heuristics with entropy contribution scoring
  • JSON-schema enforcement steps
  • Policy-gate modules for pre-execution governance

§112 Enablement Strategy

The specification supplies detailed pseudocode, JSON schemas, sample data structures, and performance benchmarks to satisfy §112 enablement requirements and avoid indefiniteness rejections.

§101 Alice Defense Strategy

To overcome §101 (Alice) rejections, the patent draft emphasizes that MCP improves the computer itself by:

  • Reducing integration complexity from O(N²) to O(N)
  • Guaranteeing deterministic chaining via content-addressed hashes
  • Enforcing pre-execution governance to prevent invalid data crossing trust boundaries

Expert declarations quantify throughput gains and audit-trail efficacy to support these technical advances.

Claim Type Strategy

By pairing system-, method-, and computer-readable-medium claims, and maintaining strict claim differentiation, the strategy navigates 101, 112, and 103 hurdles while maximizing breadth and enforceability.


1. Hub-and-Spoke Middleware

Traditional healthcare integration requires creating a direct connection between each pair of systems, resulting in M×N integration complexity . MCP solves this by implementing a hub-and-spoke architecture where tools declare their capabilities and the middleware routes contexts to the appropriate tools.

This reduces integration complexity from O(N²) to O(N) , dramatically simplifying deployment and maintenance while enabling dynamic tool discovery.

Reduced Integration Dynamic Discovery Semantic Mapping
javascript
// Tool Registry with Capability Declaration
const billingAdvisorCapabilities = {
  name: "BillingAdvisor",
  version: "2.5.0",
  capabilities: [
    {
      domain: "billing",
      actions: ["getCPTCode", "checkPriorAuth"],
      supportedEntities: ["aetna", "bluecross"]
    }
  ],
  contextRequirements: {
    required: ["insurer"],
    optional: ["patient_age", "diagnosis"]
  }
};

// Register with the capability registry
registry.registerTool(billingAdvisorCapabilities, getBillingInfo);
javascript
// Orchestrator dynamically selects tools based on context
function selectTools(contextFrame) {
  // Get tools that can handle the context
  const candidateTools = registry.findToolsForContext(contextFrame);
  
  // Filter by matching capabilities to context hints
  return candidateTools.filter(tool => {
    const toolDomains = tool.capabilities.map(cap => cap.domain);
    const hasMatchingDomain = contextFrame.hints.some(hint => 
      toolDomains.includes(hint)
    );
    return hasMatchingDomain;
  });
}
Tool 1 Tool 2 Tool 3 Tool 4 Tool 5 Tool 6 Traditional: O(N²) 15 Connections MCP Tool 1 Tool 2 Tool 3 Tool 4 Tool 5 Tool 6 MCP: O(N) 6 Connections Point-to-Point Architecture Hub-and-Spoke Architecture

2. Algorithmic Provenance Ledger

Every knowledge transaction in MCP is recorded in a tamper-evident ledger that cryptographically seals the exact context, tool version, and data sources. Unlike traditional audit logs, our provenance system is content-addressed , creating a verifiable chain where each record references its predecessor.

This provides complete auditability while being storage-efficient, as each hash encodes sufficient context to reproduce the transaction.

Cryptographic Sealing Tamper-Evident Reproducible
javascript
// Content-addressed provenance record
function recordProvenance(toolName, contextFrame, output, sources) {
  // Create provenance record with inputs and outputs
  const provenance = {
    tool: {
      name: toolName,
      version: getToolVersion(toolName)
    },
    timestamp: new Date().toISOString(),
    context_hash: contextFrame.hash,
    sources: sources.map(source => ({
      id: source.id,
      type: source.type,
      matches: source.matches || []
    })),
    output_summary: summarizeOutput(output)
  };
  
  // Link to previous record (creating a chain)
  const previousHash = store.getLatestHash();
  if (previousHash) {
    provenance.previous_hash = previousHash;
  }
  
  // Generate hash and sign the record
  const hash = generateProvenanceHash(provenance);
  provenance.hash = hash;
  provenance.signature = generateSignature(provenance);
  
  // Store immutably
  store.store(hash, provenance);
  
  return hash;
}
bash
$ ./src/index.js trace bill123abc

┬─ BillingAdvisor v2.5.0  @2025-05-20T15:22:44Z
│ context_hash: 2d19…
│ previous_hash: sche42a9b1
│ signature: f78e91ab567d4c2e
├─ RULE MATCH  billing.rules/pa_required.json   → true
│            insurance="Aetna PPO"  age=12
├─ RULE MATCH  billing.rules/cpt_mapping.yaml   → 90867
└─ OUTPUT  CPT=90867  prior_auth=true  reimbursement=312.14

3. Capability-Ranking Algorithm

MCP's orchestrator includes a sophisticated capability-ranking algorithm that combines rule-based selection with runtime performance metrics and entropy contribution scoring to select the optimal tools for each context.

This hybrid approach prioritizes tools based on past performance, capability match, and expected information value, creating a self-optimizing selection strategy .

Adaptive Selection Entropy Contribution Performance Monitoring
javascript
// Multi-factor tool scoring algorithm
function scoreTools(tools, context, history) {
  return tools.map(tool => {
    // Start with rule-based matching score
    let score = calculateRuleMatchScore(tool, context);
    
    // Add performance factor (prioritize faster, reliable tools)
    const performance = getHistoricalPerformance(tool.name, history);
    score += (1 / (performance.avgLatency + 1)) * LATENCY_WEIGHT;
    score -= (performance.failureRate) * FAILURE_WEIGHT;
    
    // Add entropy contribution factor (information gain)
    const entropyContribution = calculateEntropyContribution(
      tool, context, history
    );
    score += entropyContribution * ENTROPY_WEIGHT;
    
    return { tool, score };
  }).sort((a, b) => b.score - a.score);
}
javascript
// Records performance metrics for tool invocations
async function invokeTool(tool, contextFrame) {
  const startTime = performance.now();
  let success = false;
  
  try {
    const result = await tool.handler(contextFrame);
    success = true;
    return {
      tool: tool.name,
      data: result.data,
      provenanceHash: result.provenanceHash
    };
  } catch (error) {
    return { tool: tool.name, error: error.message };
  } finally {
    const endTime = performance.now();
    const latency = endTime - startTime;
    
    // Update tool performance statistics
    registry.recordInvocation(tool.name, success, latency);
  }
}

4. Governable Reasoning Layer

MCP implements a policy-gate infrastructure that enforces organizational rules before any data crosses trust boundaries. All data structures are validated against JSON schemas, and execution policies are enforced based on tool capabilities and context requirements.

This architecture cleanly separates governance from implementation, creating a governable reasoning layer that enables compliance with regulatory frameworks like the EU AI Act and HIPAA.

Policy Enforcement Schema Validation Trust Boundaries
javascript
// Example policy declaration
policyEngine.registerPolicy({
  name: "HIPAA Compliant Tools Only",
  description: "Only allows tools that declare HIPAA compliance",
  scope: "global",
  rules: [
    {
      condition: "tool.capabilities.compliance.hipaa === true",
      action: "ALLOW",
      message: "Tool declares HIPAA compliance"
    },
    {
      condition: "true",
      action: "DENY",
      message: "Tool does not declare HIPAA compliance"
    }
  ]
});
javascript
// Pre-execution policy gate enforcement
async function invokeTool(tool, contextFrame) {
  try {
    // Validate invocation request against schema
    const requestValidation = validateInvocationRequest({
      contextFrame,
      tool: tool.name,
      timestamp: new Date().toISOString()
    });
    
    if (!requestValidation.isValid) {
      throw new Error(`Invalid request: ${requestValidation.errors.join(', ')}`);
    }
    
    // Apply policy gates before execution
    const policyResult = applyPolicyGates(tool, contextFrame);
    if (!policyResult.allowed) {
      throw new Error(`Policy gate denied execution: ${policyResult.messages.join(', ')}`);
    }
    
    // Only if validation passes do we execute
    const result = await tool.handler(contextFrame);
    return result;
  } catch (error) {
    throw error;
  }
}
📊

Integration Statistics

With 10 tools, traditional integration requires 90 point-to-point connections. MCP reduces this to just 10 connections to the hub.

🔍

Complete Auditability

Every decision records which rules matched, which data sources were used, and creates a cryptographically verifiable audit trail.

🚫

Trust Boundary Protection

Policy gates and schema validation ensure that no data crosses trust boundaries without pre-execution validation.