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healthcare-emr-patterns

EMR/EHR development patterns for healthcare applications. Clinical safety, encounter workflows, prescription generation, clinical decision support integration, and accessibility-first UI for medical data entry. Use when building EMR or EHR features such as encounter workflows, prescription generation, or clinical data entry UI.

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healthcare-emr-patterns is a Claude Code agent skill that eMR/EHR development patterns for healthcare applications. Clinical safety, encounter workflows, prescription generation, clinical decision support integration, and accessibility-first UI for medical data entry. Use when building EMR or EHR features such as encounter workflows, prescription generation, or clinical data entry UI.

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說明文件

Healthcare EMR Development Patterns

Patterns for building Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems. Prioritizes patient safety, clinical accuracy, and practitioner efficiency.

When to Use

  • Building patient encounter workflows (complaint, exam, diagnosis, prescription)
  • Implementing clinical note-taking (structured + free text + voice-to-text)
  • Designing prescription/medication modules with drug interaction checking
  • Integrating Clinical Decision Support Systems (CDSS)
  • Building lab result displays with reference range highlighting
  • Implementing audit trails for clinical data
  • Designing healthcare-accessible UIs for clinical data entry

How It Works

Patient Safety First

Every design decision must be evaluated against: "Could this harm a patient?"

  • Drug interactions MUST alert, not silently pass
  • Abnormal lab values MUST be visually flagged
  • Critical vitals MUST trigger escalation workflows
  • No clinical data modification without audit trail

Single-Page Encounter Flow

Clinical encounters should flow vertically on a single page — no tab switching:

Patient Header (sticky — always visible)
├── Demographics, allergies, active medications
│
Encounter Flow (vertical scroll)
├── 1. Chief Complaint (structured templates + free text)
├── 2. History of Present Illness
├── 3. Physical Examination (system-wise)
├── 4. Vitals (auto-trigger clinical scoring)
├── 5. Diagnosis (ICD-10/SNOMED search)
├── 6. Medications (drug DB + interaction check)
├── 7. Investigations (lab/radiology orders)
├── 8. Plan & Follow-up
└── 9. Sign / Lock / Print

Smart Template System

interface ClinicalTemplate {
  id: string;
  name: string;             // e.g., "Chest Pain"
  chips: string[];          // clickable symptom chips
  requiredFields: string[]; // mandatory data points
  redFlags: string[];       // triggers non-dismissable alert
  icdSuggestions: string[]; // pre-mapped diagnosis codes
}

Red flags in any template must trigger a visible, non-dismissable alert — NOT a toast notification.

Medication Safety Pattern

User selects drug
  → Check current medications for interactions
  → Check encounter medications for interactions
  → Check patient allergies
  → Validate dose against weight/age/renal function
  → If CRITICAL interaction: BLOCK prescribing entirely
  → Clinician must document override reason to proceed past a block
  → If MAJOR interaction: display warning, require acknowledgment
  → Log all alerts and override reasons in audit trail

Critical interactions block prescribing by default. The clinician must explicitly override with a documented reason stored in the audit trail. The system never silently allows a critical interaction.

Locked Encounter Pattern

Once a clinical encounter is signed:

  • No edits allowed — only an addendum (a separate linked record)
  • Both original and addendum appear in the patient timeline
  • Audit trail captures who signed, when, and any addendum records

UI Patterns for Clinical Data

Vitals Display: Current values with normal range highlighting (green/yellow/red), trend arrows vs previous, clinical scoring auto-calculated (NEWS2, qSOFA), escalation guidance inline.

Lab Results Display: Normal range highlighting, previous value comparison, critical values with non-dismissable alert, collection/analysis timestamps, pending orders with expected turnaround.

Prescription PDF: One-click generation with patient demographics, allergies, diagnosis, drug details (generic + brand, dose, route, frequency, duration), clinician signature block.

Accessibility for Healthcare

Healthcare UIs have stricter requirements than typical web apps:

  • 4.5:1 minimum contrast (WCAG AA) — clinicians work in varied lighting
  • Large touch targets (44x44px minimum) — for gloved/rushed interaction
  • Keyboard navigation — for power users entering data rapidly
  • No color-only indicators — always pair color with text/icon (colorblind clinicians)
  • Screen reader labels on all form fields
  • No auto-dismissing toasts for clinical alerts — clinician must actively acknowledge

Anti-Patterns

  • Storing clinical data in browser localStorage
  • Silent failures in drug interaction checking
  • Dismissable toasts for critical clinical alerts
  • Tab-based encounter UIs that fragment the clinical workflow
  • Allowing edits to signed/locked encounters
  • Displaying clinical data without audit trail
  • Using any type for clinical data structures

Examples

Example 1: Patient Encounter Flow

Doctor opens encounter for Patient #4521
  → Sticky header shows: "Rajesh M, 58M, Allergies: Penicillin, Active Meds: Metformin 500mg"
  → Chief Complaint: selects "Chest Pain" template
    → Clicks chips: "substernal", "radiating to left arm", "crushing"
    → Red flag "crushing substernal chest pain" triggers non-dismissable alert
  → Examination: CVS system — "S1 S2 normal, no murmur"
  → Vitals: HR 110, BP 90/60, SpO2 94%
    → NEWS2 auto-calculates: score 8, risk HIGH, escalation alert shown
  → Diagnosis: searches "ACS" → selects ICD-10 I21.9
  → Medications: selects Aspirin 300mg
    → CDSS checks against Metformin: no interaction
  → Signs encounter → locked, addendum-only from this point

Example 2: Medication Safety Workflow

Doctor prescribes Warfarin for Patient #4521
  → CDSS detects: Warfarin + Aspirin = CRITICAL interaction
  → UI: red non-dismissable modal blocks prescribing
  → Doctor clicks "Override with reason"
  → Types: "Benefits outweigh risks — monitored INR protocol"
  → Override reason + alert stored in audit trail
  → Prescription proceeds with documented override

Example 3: Locked Encounter + Addendum

Encounter #E-2024-0891 signed by Dr. Shah at 14:30
  → All fields locked — no edit buttons visible
  → "Add Addendum" button available
  → Dr. Shah clicks addendum, adds: "Lab results received — Troponin elevated"
  → New record E-2024-0891-A1 linked to original
  → Timeline shows both: original encounter + addendum with timestamps

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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