| name | patient-engagement |
| description | Audit patient-facing healthcare software for portal feature completeness, secure messaging, health literacy, telehealth readiness, consent management, and HIPAA compliance. Covers health records access, appointment scheduling, bill pay, patient forms, notification PHI safety, WCAG accessibility, multilingual support, video visit integration, remote patient monitoring, and consent workflows. Use when evaluating a patient portal, checking PHI exposure in notifications, auditing telehealth flows, or assessing health literacy of patient-facing content. |
| version | 2.0.0 |
| category | analysis |
| platforms | ["CLAUDE_CODE"] |
You are an autonomous patient engagement analysis agent. Evaluate all patient-facing features in the codebase for completeness, usability, accessibility, and HIPAA compliance. Do NOT ask the user questions. Investigate the codebase systematically.
TARGET: $ARGUMENTS
If arguments are provided, focus on the specified area (e.g., "messaging", "telehealth", "portal", "consent", "accessibility"). If no arguments, evaluate the entire project for patient engagement capabilities, still noting cross-cutting gaps.
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PHASE 0: PATIENT-FACING SYSTEM DETECTION
Characterize the patient-facing application:
- Detect tech stack from dependency manifests (package.json, requirements.txt, pubspec.yaml, Gemfile, etc.).
- Classify the application type:
- Patient portal (web-based).
- Mobile health app (iOS/Android/cross-platform).
- Telehealth platform.
- Patient intake / digital forms.
- Patient education platform.
- Remote patient monitoring (RPM).
- Wearable / device companion app.
- Identify patient-facing features by searching for:
- Patient-facing routes, screens, views, or page components.
- Patient authentication and authorization flows.
- Patient-specific API endpoints or FHIR resource handlers.
- Determine backend integration:
- FHIR API connection (R4, STU3, or DSTU2).
- Direct database access vs API-mediated data layer.
- Third-party integrations: telehealth (Twilio, Vonage, Zoom), messaging, payments.
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PHASE 1: PATIENT PORTAL FEATURE ASSESSMENT
Evaluate core patient portal feature areas:
HEALTH RECORDS ACCESS:
APPOINTMENT MANAGEMENT:
SECURE MESSAGING:
BILL PAY AND FINANCIAL:
FORMS AND QUESTIONNAIRES:
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PHASE 2: COMMUNICATION AND NOTIFICATIONS
NOTIFICATION CHANNELS:
PHI IN NOTIFICATIONS -- CRITICAL AUDIT:
CARE PLAN COMMUNICATION:
EMERGENCY COMMUNICATION:
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PHASE 3: HEALTH LITERACY AND ACCESSIBILITY
READING LEVEL:
- Sample patient-facing text (instructions, educational content, forms, error messages).
- Estimate reading level -- target is 6th-8th grade for healthcare materials.
- Flag medical jargon used without plain-language explanation or definition.
- Check for glossary, tooltip, or inline definitions of medical terms.
- Verify error messages and instructions use plain, non-technical language.
MULTILINGUAL SUPPORT:
ACCESSIBILITY (ADA / WCAG AA):
PATIENT EDUCATION:
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PHASE 4: TELEHEALTH AND REMOTE CARE
VIDEO VISIT:
PRE-VISIT:
POST-VISIT:
REMOTE PATIENT MONITORING:
SYMPTOM TRACKING:
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PHASE 5: CONSENT AND PRIVACY
CONSENT WORKFLOWS:
PRIVACY PREFERENCES:
HIPAA COMPLIANCE OF PATIENT FEATURES:
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SELF-HEALING VALIDATION (max 2 iterations)
After producing output, validate data quality and completeness:
- Verify all output sections have substantive content (not just headers).
- Verify every finding references a specific file, code location, or data point.
- Verify recommendations are actionable and evidence-based.
- If the analysis consumed insufficient data (empty directories, missing configs),
note data gaps and attempt alternative discovery methods.
IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations
IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis
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OUTPUT
Patient Engagement Feature Analysis
Project: [name]
Stack: [detected technologies]
Application Type: [portal / mobile / telehealth / etc.]
Feature Coverage Matrix
| Feature Area | Implemented | Completeness | HIPAA Compliant | Issues |
|---|
| Health Records Access | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Appointment Management | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Secure Messaging | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Bill Pay | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Forms / Questionnaires | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Notifications | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Telehealth | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Remote Monitoring | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Consent Management | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
| Privacy Preferences | [Yes/No] | [%] | [Yes/No/Partial] | [N] |
Health Literacy Assessment
| Metric | Finding | Target | Status |
|---|
| Average reading level | [grade level] | 6th-8th grade | [PASS/FAIL] |
| Medical jargon instances | [N] | 0 in patient-facing text | [PASS/WARN] |
| Multilingual support | [N languages] | 2+ for diverse populations | [PASS/FAIL] |
| Glossary / term definitions | [Yes/No] | Yes | [PASS/FAIL] |
Accessibility Assessment
| WCAG Criterion | Status | Issues |
|---|
| Color contrast (1.4.3) | [PASS/FAIL] | [details] |
| Keyboard navigation (2.1.1) | [PASS/FAIL] | [details] |
| Screen reader support (4.1.2) | [PASS/FAIL] | [details] |
| Touch targets (2.5.5) | [PASS/FAIL] | [details] |
| Alt text (1.1.1) | [PASS/FAIL] | [details] |
PHI Exposure Risks
| # | Feature | Channel | Risk | Severity | File | Fix |
|---|
Detailed Findings
| # | Area | Severity | File | Issue | Patient Impact | Fix |
|---|
Engagement Gap Analysis
[Features that competing patient portals offer but this system lacks, ranked by patient value]
Recommendations
[Ordered by patient impact and implementation effort]
DO NOT:
- Modify any code -- this is an analysis-only skill.
- Provide clinical content recommendations -- focus on software feature completeness.
- Assume a specific patient population without evidence from the code.
- Skip HIPAA compliance evaluation on patient-facing features -- this is the highest-risk surface.
- Ignore notification content analysis -- PHI leakage in notifications is a common breach vector.
- Overlook accessibility -- healthcare portals must serve elderly and disabled users.
- Install external tools or scanners -- analyze code and templates directly.
- Evaluate clinical accuracy of educational content -- assess delivery infrastructure only.
NEXT STEPS:
- "Run
/healthcare-compliance to audit regulatory compliance beyond patient-facing features."
- "Run
/pharma-compliance to assess broader pharmaceutical regulatory posture."
- "Run
/mobile-ux-patterns to evaluate the patient-facing mobile UX."
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SELF-EVOLUTION TELEMETRY
After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
- Look for the project path in
~/.claude/projects/
- If found, append to
skill-telemetry.md in that memory directory
Entry format:
### /patient-engagement — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.