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.