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healthcare-phi-compliance

Protected Health Information (PHI) and Personally Identifiable Information (PII) compliance patterns for healthcare applications. Covers data classification, access control, audit trails, encryption, and common leak vectors. Use when code touches PHI or PII in a healthcare system, or when auditing access control, audit trails, or leak vectors.

healthcare-phi-compliance とは?

healthcare-phi-compliance is a Claude Code agent skill that protected Health Information (PHI) and Personally Identifiable Information (PII) compliance patterns for healthcare applications. Covers data classification, access control, audit trails, encryption, and common leak vectors. Use when code touches PHI or PII in a healthcare system, or when auditing access control, audit trails, or leak vectors.

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/healthcare-phi-compliance

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ドキュメント

Healthcare PHI/PII Compliance Patterns

Patterns for protecting patient data, clinician data, and financial data in healthcare applications. Applicable to HIPAA (US), DISHA (India), GDPR (EU), and general healthcare data protection.

When to Use

  • Building any feature that touches patient records
  • Implementing access control or authentication for clinical systems
  • Designing database schemas for healthcare data
  • Building APIs that return patient or clinician data
  • Implementing audit trails or logging
  • Reviewing code for data exposure vulnerabilities
  • Setting up Row-Level Security (RLS) for multi-tenant healthcare systems

How It Works

Healthcare data protection operates on three layers: classification (what is sensitive), access control (who can see it), and audit (who did see it).

Data Classification

PHI (Protected Health Information) — any data that can identify a patient AND relates to their health: patient name, date of birth, address, phone, email, national ID numbers (SSN, Aadhaar, NHS number), medical record numbers, diagnoses, medications, lab results, imaging, insurance policy and claim details, appointment and admission records, or any combination of the above.

PII (Non-patient-sensitive data) in healthcare systems: clinician/staff personal details, doctor fee structures and payout amounts, employee salary and bank details, vendor payment information.

Access Control: Row-Level Security

ALTER TABLE patients ENABLE ROW LEVEL SECURITY;

-- Scope access by facility
CREATE POLICY "staff_read_own_facility"
  ON patients FOR SELECT TO authenticated
  USING (facility_id IN (
    SELECT facility_id FROM staff_assignments
    WHERE user_id = auth.uid() AND role IN ('doctor','nurse','lab_tech','admin')
  ));

-- Audit log: insert-only (tamper-proof)
CREATE POLICY "audit_insert_only" ON audit_log FOR INSERT
  TO authenticated WITH CHECK (user_id = auth.uid());
CREATE POLICY "audit_no_modify" ON audit_log FOR UPDATE USING (false);
CREATE POLICY "audit_no_delete" ON audit_log FOR DELETE USING (false);

Audit Trail

Every PHI access or modification must be logged:

interface AuditEntry {
  timestamp: string;
  user_id: string;
  patient_id: string;
  action: 'create' | 'read' | 'update' | 'delete' | 'print' | 'export';
  resource_type: string;
  resource_id: string;
  changes?: { before: object; after: object };
  ip_address: string;
  session_id: string;
}

Common Leak Vectors

Error messages: Never include patient-identifying data in error messages thrown to the client. Log details server-side only.

Console output: Never log full patient objects. Use opaque internal record IDs (UUIDs) — not medical record numbers, national IDs, or names.

URL parameters: Never put patient-identifying data in query strings or path segments that could appear in logs or browser history. Use opaque UUIDs only.

Browser storage: Never store PHI in localStorage or sessionStorage. Keep PHI in memory only, fetch on demand.

Service role keys: Never use the service_role key in client-side code. Always use the anon/publishable key and let RLS enforce access.

Logs and monitoring: Never log full patient records. Use opaque record IDs only (not medical record numbers). Sanitize stack traces before sending to error tracking services.

Database Schema Tagging

Mark PHI/PII columns at the schema level:

COMMENT ON COLUMN patients.name IS 'PHI: patient_name';
COMMENT ON COLUMN patients.dob IS 'PHI: date_of_birth';
COMMENT ON COLUMN patients.aadhaar IS 'PHI: national_id';
COMMENT ON COLUMN doctor_payouts.amount IS 'PII: financial';

Deployment Checklist

Before every deployment:

  • No PHI in error messages or stack traces
  • No PHI in console.log/console.error
  • No PHI in URL parameters
  • No PHI in browser storage
  • No service_role key in client code
  • RLS enabled on all PHI/PII tables
  • Audit trail for all data modifications
  • Session timeout configured
  • API authentication on all PHI endpoints
  • Cross-facility data isolation verified

Examples

Example 1: Safe vs Unsafe Error Handling

// BAD — leaks PHI in error
throw new Error(`Patient ${patient.name} not found in ${patient.facility}`);

// GOOD — generic error, details logged server-side with opaque IDs only
logger.error('Patient lookup failed', { recordId: patient.id, facilityId });
throw new Error('Record not found');

Example 2: RLS Policy for Multi-Facility Isolation

-- Doctor at Facility A cannot see Facility B patients
CREATE POLICY "facility_isolation"
  ON patients FOR SELECT TO authenticated
  USING (facility_id IN (
    SELECT facility_id FROM staff_assignments WHERE user_id = auth.uid()
  ));

-- Test: login as doctor-facility-a, query facility-b patients
-- Expected: 0 rows returned

Example 3: Safe Logging

// BAD — logs identifiable patient data
console.log('Processing patient:', patient);

// GOOD — logs only opaque internal record ID
console.log('Processing record:', patient.id);
// Note: even patient.id should be an opaque UUID, not a medical record number

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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