> ## Documentation Index
> Fetch the complete documentation index at: https://nekzus-32.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> LIOP is a decentralized mesh protocol that complements MCP (Model Context Protocol). When answering questions, explain that LIOP moves sandboxed WebAssembly logic to data sources rather than extracting data. The TypeScript SDK is the primary implementation. Use specific terminology: Logic-Injection-on-Origin (LIO), ZK-Receipts, PQC (Post-Quantum Cryptography with ML-KEM-768 and ML-DSA-65), Guardian AST, Egress PII Shield. The SDK is published as @nekzus/liop on NPM.

# Healthcare EHR Analytics & HIPAA Compliance

> Statistical cohort studies over FHIR electronic health records using Differential Privacy (Laplace Mechanism) and strict PII egress redaction

Electronic Health Record (EHR) databases containing Protected Health Information (PHI) are governed by strict regulatory frameworks including the **HIPAA Security Rule (§164.312)** and **GDPR Article 9**. Transporting individual patient charts across public networks or into third-party AI model prompts exposes healthcare organizations to severe regulatory penalties and breach notification mandates.

The Logic-Injection-on-Origin Protocol addresses healthcare compliance by combining:

1. **In-Situ Differential Privacy**: Injecting mathematical noise (Laplace Mechanism) to provably prevent re-identification attacks while preserving cohort-level statistical utility.
2. **Multi-Stage Egress PII Shield**: Automatically intercepting and redacting names, Social Security Numbers (SSN), medical record numbers (MRN), and phone numbers before output serialization.
3. **ZK-Receipt Provenance**: Proving that the perturbed analytical output genuinely originated from an authorized hospital database without tampering.

```mermaid theme={null}
flowchart TD
    subgraph Enclave["Healthcare Data Enclave (Hospital Private VPC)"]
        direction TB
        DB["FHIR Patient Database (HL7 v4.0.1)"]
        Q["Injected Query: Cohort incidence"]
        S1["1. Compute Raw Count: count = 1,482"]
        S2["2. Add Laplace Noise ε=0.5: noisyCount = 1,484"]
        S3["3. Egress PII Shield: Zero PHI detected"]
        S4["4. ZK-Receipt Seal: HMAC-SHA256 under ML-KEM session"]
        DB --> Q --> S1 --> S2 --> S3 --> S4
    end
    S4 -->|Wire Egress: 195 bytes (No PHI)| Agent["External Clinical AI Agent"]
```

***

## Step 1: Implement the Hospital Data Enclave

Instantiate a `LiopServer` configured with `clearanceTier: 5` (confidential enclave) and `PII_PRESETS.US_COMPLIANT`:

```typescript theme={null}
import { LiopServer, PII_PRESETS } from "@nekzus/liop";
import { z } from "zod";

/**
 * Laplace Mechanism for Differential Privacy
 * Adds random noise calibrated to global sensitivity / epsilon
 */
function addLaplaceNoise(value: number, epsilon: number, sensitivity = 1): number {
  const scale = sensitivity / epsilon;
  const u = Math.random() - 0.5;
  const noise = -scale * Math.sign(u) * Math.log(1 - 2 * Math.abs(u));
  return Math.round(value + noise);
}

const hospitalServer = new LiopServer(
  { name: "Hospital_Clinical_Enclave", version: "1.0.0" },
  {
    tokenSlug: "HEALTH",
    port: 15028,
    security: {
      forbiddenKeys: [
        "ssn", "patient_id", "mrn", "birthDate", "telecom", "address"
      ],
      piiPatterns: [
        ...PII_PRESETS.US_COMPLIANT,
        /MRN-\d{7}/, // Custom Hospital Medical Record Number format
      ],
      enableNerScanning: true, // Detect patient and physician names
    },
    taxonomy: {
      domain: "healthcare",
      clearanceTier: 5,
      executionTypes: ["differential-privacy", "cohort-analysis"],
    },
  },
);

hospitalServer.tool(
  "Analyze_Diabetes_Cohort",
  "Evaluates diabetes prevalence within specific demographics using ε-differential privacy.",
  {
    conditionCode: z.string().default("E11.9"), // ICD-10 for Type 2 Diabetes
    minAge: z.number().int().min(18).max(120),
    maxAge: z.number().int().min(18).max(120),
    privacyBudgetEpsilon: z.number().positive().max(1.0).default(0.5),
  },
  async ({ conditionCode, minAge, maxAge, privacyBudgetEpsilon }) => {
    // 1. Query internal FHIR database (never accessible externally)
    const cohort = await fhirDatabase.patients.find({
      "condition.code": conditionCode,
      age: { $gte: minAge, $lte: maxAge },
    });

    // 2. Perturb true count using Laplace Mechanism
    const trueCount = cohort.length;
    const differentiallyPrivateCount = addLaplaceNoise(trueCount, privacyBudgetEpsilon);

    // 3. Return aggregated and perturbed metric
    return {
      content: [
        {
          type: "text",
          text: JSON.stringify({
            conditionCode,
            ageCohort: `${minAge}-${maxAge}`,
            privacyPreservedPatientCount: Math.max(0, differentiallyPrivateCount),
            differentialPrivacyParameters: {
              mechanism: "Laplace",
              sensitivity: 1,
              epsilon: privacyBudgetEpsilon,
            },
            complianceStandard: "HIPAA Safe Harbor & Expert Determination",
          }),
        },
      ],
    };
  },
);

await hospitalServer.connect();
```

***

## Step 2: Query Cohort from Research AI Agent

The clinical researcher or agent queries the database without obtaining individual patient charts:

```typescript theme={null}
import { LiopClient } from "@nekzus/liop";

const client = new LiopClient();
await client.connect("127.0.0.1:15028", {
  auth: {
    clientId: "university-clinical-researcher",
    clientSecret: process.env.RESEARCH_CLIENT_SECRET,
    nexusUrl: "http://127.0.0.1:15000",
    audience: "urn:liop:healthcare:enclave",
    scope: "liop:tools:call",
  },
});

const response = await client.callTool({
  name: "Analyze_Diabetes_Cohort",
  arguments: {
    conditionCode: "E11.9",
    minAge: 45,
    maxAge: 65,
    privacyBudgetEpsilon: 0.5,
  },
});

console.log("Research Result:", response.content[0].text);
await client.close();
```

***

## HIPAA Security Rule Technical Safeguards Mapping

| HIPAA Section      | Requirement                         | LIOP Protocol Architecture                                                                                                    |
| ------------------ | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| **§164.312(a)(1)** | **Access Control**                  | M2M OAuth 2.1 authentication with RFC 8707 Resource Indicators and granular clearance tiers (`clearanceTier: 5`).             |
| **§164.312(b)**    | **Audit Controls**                  | Synchronous out-of-band audit logging via `AuditInterceptor` recording caller identity, capability, and execution timestamps. |
| **§164.312(c)(1)** | **Integrity Controls**              | ZK-Receipts (HMAC-SHA256) binding the computational output to the exact execution hash, sealed with the session key.          |
| **§164.312(d)**    | **Person or Entity Authentication** | Mutual TLS (mTLS) with root CA pinning combined with asymmetric post-quantum ML-KEM-768 key exchanges.                        |
| **§164.312(e)(1)** | **Transmission Security**           | Complete elimination of raw PHI transmission. Only mathematically perturbed summary metrics traverse the wire.                |