Swap Management

Swap Management Skill

Expert procedures for managing schedule swaps while maintaining compliance and fairness.

When This Skill Activates

• Processing swap requests
• Finding compatible swap partners
• Resolving scheduling conflicts
• Answering swap policy questions
• Validating proposed swaps
• Handling emergency coverage

Swap Types

1. One-to-One Swap

Definition: Direct exchange between two people

Person A: Tuesday AM → Person B
Person B: Thursday PM → Person A

Requirements:

• Both parties consent
• Both qualified for swapped rotation
• Neither exceeds hour limits after swap
• Supervision ratios maintained

2. Absorb (Give Away)

Definition: One person takes another's shift without exchange

Person A: Gives away Friday PM
Person B: Absorbs Friday PM (no return)

Requirements:

• Receiver doesn't exceed limits
• Receiver qualified for rotation
• Giver has legitimate reason (PTO, conference, etc.)

3. Three-Way Swap

Definition: Circular exchange among three people

A → B's shift
B → C's shift
C → A's shift

Requirements:

• All three consent
• All compliance checks pass
• Tracked as linked transactions

Auto-Matching Algorithm

The system finds compatible swap partners using:

Match Criteria (Ranked)

1. Qualification Match - Same rotation certification
2. Hour Balance - Neither party exceeds limits
3. Preference Alignment - Historical preferences
4. Fairness Score - Workload distribution
5. Proximity - Schedule adjacency for handoffs

MCP Tool

Tool: analyze_swap_compatibility
Input: { requestor_id, target_shift, swap_type }
Output: { matches: [...], compatibility_scores: {...} }

Validation Checklist

Before approving any swap:

Pre-Swap Checks

• Both parties have consented
• Qualification/credentialing verified
• 80-hour limit maintained for both
• 1-in-7 day off preserved
• Supervision ratios still valid
• No double-booking created
• Handoff timing adequate

Post-Swap Verification

• Calendar updated for both parties
• Notifications sent
• Audit trail created
• Metrics recalculated

Swap Request Workflow

Step 1: Request Submission

POST /api/swap/request
{
  "requestor_id": "uuid",
  "target_shift": { "date": "2025-01-15", "session": "PM" },
  "swap_type": "one_to_one",
  "reason": "Conference attendance",
  "preferred_partners": ["uuid1", "uuid2"]  // optional
}

Step 2: Auto-Match (if no partner specified)

System runs matching algorithm and returns:

• Top 5 compatible partners
• Compatibility scores
• Any warnings (approaching limits, etc.)

Step 3: Partner Response

Partner has 48 hours to:

• Accept → Proceeds to validation
• Decline → Returns to matching
• Counter-propose → New swap terms

Step 4: Validation

System performs full compliance check:

Tool: validate_swap
Returns: { valid: boolean, issues: [], warnings: [] }

Step 5: Execution

If valid:

1. Update assignments in database
2. Send calendar invites
3. Notify stakeholders
4. Log audit trail

Step 6: Rollback Window

24-hour window to reverse swap if issues discovered.

Emergency Coverage Protocol

When immediate coverage needed:

Tier 1: Available Pool

Check faculty/residents marked as backup:

SELECT * FROM persons
WHERE backup_available = true
  AND NOT EXISTS (shift on target date)
  AND weekly_hours < 75;

Tier 2: Swap Market

Broadcast need to all qualified personnel:

• Push notification
• Email alert
• Slack message (via n8n)

Tier 3: Absorb Request

Ask nearby shifts to extend coverage:

• Maximum 4-hour extension
• Must maintain 8-hour break

Tier 4: Escalation

If unfilled after 4 hours:

• Alert Program Director
• Consider schedule restructure
• Document coverage gap

Fairness Considerations

Swap Equity Tracking

Monitor swap patterns for fairness:

Protected Categories

Extra scrutiny for swaps affecting:

• Residents on probation
• Faculty approaching burnout indicators
• Personnel with documented accommodations

Examples

Example 1: Conference Swap Request

User Request: "Dr. Smith needs to swap her Thursday March 24 PM shift because she has a required conference. Can you help find a match?"

Claude Response:

Step 1: Verify shift eligibility

Checking shift details...
- Person: Dr. Sarah Smith (FAC-CORE-02)
- Date: March 24, 2026 (Thursday PM)
- Current assignment: Outpatient Clinic
- Shift status: Active (not frozen) ✅

Step 2: Run auto-matcher

Calling analyze_swap_compatibility...

Top 5 compatible matches:
1. Dr. Johnson (FAC-CORE-05) - Score: 0.92
   - Available: Tuesday PM (clinic)
   - Current hours: 45/80 (safe buffer)
   - Historical preference: Prefers PM shifts ✅

2. Dr. Williams (FAC-CORE-01) - Score: 0.85
   - Available: Wednesday AM (clinic)
   - Current hours: 52/80
   - Note: Approaching preferred workload limit

3. Dr. Davis (FAC-CORE-07) - Score: 0.78
   - Available: Friday AM (procedures)
   - Current hours: 38/80
   - Note: Different rotation type (may need cross-training)

Step 3: Validate top match

Validating swap: Smith ↔ Johnson...

{
  "valid": true,
  "compliance_check": {
    "smith_hours_post_swap": 47,
    "johnson_hours_post_swap": 47,
    "acgme_violations": 0,
    "supervision_ratios": "maintained"
  },
  "warnings": []
}

Recommendation:

"✅ Dr. Johnson is the best match (92% compatibility). Both will remain well under work hour limits. Swap maintains all compliance requirements. Shall I notify Dr. Johnson and request consent?"

User: "Yes, please proceed"

Step 4: Execute swap

Sending swap request to Dr. Johnson...
- Notification sent via email + app
- Awaiting response (48-hour window)
- Swap ID: SWAP-20260312-001

I'll notify you when Dr. Johnson responds.

Example 2: Emergency Sick Coverage

User Request: "URGENT: Dr. Martinez just called in sick for her Sunday call shift. We need immediate coverage."

Claude Response:

🚨 Emergency coverage protocol activated

Step 1: Check backup pool

Querying available backup faculty...

Backup pool found: 3 available
1. Dr. Chen (FAC-CORE-08) - No assignments Sunday
   - Current hours: 42/80 ✅
   - Qualified for call ✅
   - Marked as backup_available ✅

2. Dr. Patel (FAC-CORE-11) - Has AM shift only
   - Could extend to cover PM call
   - Current hours: 38/80 ✅

3. Dr. Kim (FAC-CORE-03) - Weekend off
   - In backup pool but hours: 68/80 ⚠️

Step 2: Validate emergency absorb

Checking Dr. Chen for emergency coverage...

Compliance check:
- Post-absorb hours: 42 + 12 = 54/80 ✅
- 1-in-7 day off: Preserved ✅
- No double-booking: ✅
- Recent call: Last call 9 days ago ✅

✅ Dr. Chen is safe to absorb the shift

Step 3: Execute without consent (emergency)

Executing emergency absorb...

POST /api/swap/emergency-absorb
{
  "original_person_id": "FAC-MARTINEZ",
  "replacement_person_id": "FAC-CHEN",
  "shift": "2026-03-27 PM (Sunday Call)",
  "reason": "sick_call",
  "emergency": true,
  "skip_consent": true
}

✅ Assignment updated
✅ Dr. Chen notified (SMS + email + app)
✅ Calendar invite sent
✅ Program Director notified of emergency swap

Step 4: Workload balancing note

Emergency swap logged for fairness tracking:
- Dr. Chen: +1 emergency absorb (total this quarter: 2)
- Dr. Martinez: Sick day recorded

Recommendation: Offer Dr. Chen compensatory time off or first pick
on next swap request to maintain equity.

Summary:

"✅ Emergency coverage secured. Dr. Chen will cover Sunday call. All compliance requirements maintained. Dr. Martinez can focus on recovery."

Example 3: Standing Preference Swap

User Request: "Two residents want to permanently swap their clinic days - PGY-2 resident wants Mondays instead of Tuesdays. Is this possible?"

Claude Response:

Step 1: Identify swap scope

Request: Standing swap arrangement
- Resident A (PGY-2): Currently Tuesday PM clinic
- Resident B (PGY-2): Currently Monday PM clinic
- Duration: Remainder of academic year (6 months)

Step 2: Check long-term compliance

Validating standing swap arrangement...

Checking 24-week projection:
✅ Week 1-4: Both residents maintain <80hr limit
✅ Week 5-8: Both residents maintain <80hr limit
...
✅ Week 21-24: Both residents maintain <80hr limit

⚠️  Warning: Resident A has scheduled conference March 30 (Monday)
    - Will need one-time exception that week

Long-term ACGME compliance: ✅ PASS

Step 3: Create recurring swap template

Creating standing swap template...

{
  "template_id": "SWAP-TEMPLATE-001",
  "type": "recurring_weekly",
  "participant_a": "RES-PGY2-03",
  "participant_b": "RES-PGY2-07",
  "swap_pattern": {
    "a_gives": "Tuesday PM Clinic",
    "a_receives": "Monday PM Clinic",
    "b_gives": "Monday PM Clinic",
    "b_receives": "Tuesday PM Clinic"
  },
  "duration": "2026-03-12 to 2026-09-30",
  "exceptions": ["2026-03-30 (Resident A conference)"],
  "review_frequency": "quarterly"
}

✅ Template created
✅ Next 24 swaps auto-scheduled

Step 4: Fairness review schedule

Standing swap approved with quarterly review:
- First review: June 15, 2026
- Review criteria:
  * Both residents satisfied with arrangement
  * No negative impact on team dynamics
  * Work hour compliance maintained
  * Fair distribution of weekend coverage

I'll remind you 1 week before each quarterly review.

Recommendation:

"✅ Standing swap approved for 6 months. Both residents will swap clinic days weekly (with 1 exception for conference). Quarterly fairness reviews scheduled. Both residents notified of approval."

Common CLI Scenarios

Scenario: Conference Conflict

Request: Need Thursday off for required conference Process:

1. Check if shift is swappable (not frozen)
2. Find partner with complementary availability
3. Validate both parties' compliance
4. Execute swap with "conference" reason code

Scenario: Sick Coverage

Request: Called in sick, need immediate coverage Process:

1. Skip consent (emergency protocol)
2. Query backup pool first
3. Broadcast if no backups available
4. Log as emergency absorb
5. Balance workload later

Scenario: Preference Swap

Request: Want to swap AM for PM regularly Process:

1. Check if standing swap arrangement possible
2. Validate long-term compliance
3. If approved, create recurring swap template
4. Review quarterly for fairness

Escalation Triggers

Escalate to Coordinator when:

1. No compatible matches found
2. Swap would create compliance violation
3. Same person requesting >3 swaps/month
4. Pattern suggests scheduling problem
5. Conflict between swap participants

Reporting Format

## Swap Request Summary

**Request ID:** [ID]
**Type:** [One-to-One / Absorb / Three-Way]
**Status:** [Pending / Matched / Validated / Executed / Rejected]

### Parties
- Requestor: [Name] - [Current Hours: X/80]
- Partner: [Name] - [Current Hours: Y/80]

### Shifts Involved
- [Date] [Session] [Rotation] → From [A] to [B]
- [Date] [Session] [Rotation] → From [B] to [A]

### Validation Result
- Compliance: [PASS/FAIL]
- Warnings: [List any]
- Approval: [Auto/Manual Required]

### Next Steps
1. [Action item]

MCP Tools Reference

Concrete Usage Example

End-to-End: Processing a Conference Swap Request

Scenario: Dr. Smith needs Thursday PM off for a required conference. Find a compatible swap partner and execute the swap safely.

Step 1: Receive Swap Request

# Via API or admin interface
from app.models import SwapRequest
from datetime import date

swap_request = SwapRequest(
    requestor_id="person-001",  # Dr. Smith
    target_date=date(2025, 2, 13),  # Thursday
    target_session="PM",
    swap_type="one_to_one",
    reason="ACFP Conference (required)",
    preferred_partners=[]  # Will auto-match
)

Step 2: Find Compatible Partners

from app.services.swap_matcher import SwapMatcher

matcher = SwapMatcher(db)
matches = await matcher.find_compatible_partners(
    requestor_id="person-001",
    target_shift={
        "date": date(2025, 2, 13),
        "session": "PM",
        "rotation": "FM Clinic"
    },
    max_matches=5
)

print(f"Found {len(matches)} compatible partners:")
for match in matches:
    print(f"  {match.person_id}: {match.compatibility_score:.2f}")
    print(f"    - Current hours: {match.current_weekly_hours}/80")
    print(f"    - Qualified: {match.is_qualified}")
    print(f"    - Suggested return shift: {match.suggested_return_shift}")

Expected Output:

Found 3 compatible partners:
  person-002: 0.92
    - Current hours: 68/80
    - Qualified: True
    - Suggested return shift: 2025-02-18 AM (Tuesday)

  person-005: 0.87
    - Current hours: 65/80
    - Qualified: True
    - Suggested return shift: 2025-02-19 PM (Wednesday)

  person-003: 0.75
    - Current hours: 72/80
    - Qualified: True
    - Suggested return shift: 2025-02-20 AM (Thursday)

Step 3: Present Options to Requestor

# Dr. Smith reviews matches and selects person-002
swap_request.partner_id = "person-002"
swap_request.return_shift = {
    "date": date(2025, 2, 18),
    "session": "AM"
}

Step 4: Validate Swap

from app.services.swap_validator import SwapValidator

validator = SwapValidator(db)
validation_result = await validator.validate_swap(swap_request)

if validation_result.is_valid:
    print("✅ Swap validation: PASS")
else:
    print("❌ Swap validation: FAIL")
    for issue in validation_result.issues:
        print(f"   - {issue.severity}: {issue.description}")

# Check warnings even if valid
if validation_result.warnings:
    print("⚠️ Warnings:")
    for warning in validation_result.warnings:
        print(f"   - {warning}")

Expected Output:

✅ Swap validation: PASS

⚠️ Warnings:
   - person-002 will have 3 swaps this month (approaching limit of 4)

Step 5: Get Partner Consent

# Send notification to partner
from app.notifications import send_swap_request_notification

await send_swap_request_notification(
    to_person_id="person-002",
    swap_request=swap_request,
    expiration_hours=48
)

print("Notification sent. Waiting for response...")

# Partner responds (via API or UI)
# Assume they accept
swap_request.partner_status = "accepted"
swap_request.partner_accepted_at = datetime.now()

Step 6: Execute Swap

from app.services.swap_executor import SwapExecutor

executor = SwapExecutor(db)
result = await executor.execute_swap(swap_request)

if result.success:
    print(f"✅ Swap executed successfully!")
    print(f"   Swap ID: {result.swap_id}")
    print(f"   Audit trail: {result.audit_trail_id}")

    # What changed:
    for change in result.changes:
        print(f"   - {change.person_id}: {change.from_assignment} → {change.to_assignment}")
else:
    print(f"❌ Swap failed: {result.error}")

Expected Output:

✅ Swap executed successfully!
   Swap ID: swap-12345
   Audit trail: audit-67890

   Changes:
   - person-001: 2025-02-13 PM FM Clinic → [unassigned]
   - person-002: 2025-02-13 PM [unassigned] → 2025-02-13 PM FM Clinic
   - person-001: 2025-02-18 AM [unassigned] → 2025-02-18 AM FM Clinic
   - person-002: 2025-02-18 AM FM Clinic → [unassigned]

Step 7: Post-Execution Verification

# Re-validate ACGME compliance for both parties
from app.scheduling.acgme_validator import ACGMEValidator

acgme_validator = ACGMEValidator()

for person_id in ["person-001", "person-002"]:
    assignments = await db.get_assignments_for_person(
        person_id,
        start_date=date(2025, 2, 1),
        end_date=date(2025, 2, 28)
    )

    compliance_result = acgme_validator.validate_person_schedule(
        person_id=person_id,
        assignments=assignments
    )

    if compliance_result.is_compliant:
        print(f"✅ {person_id}: ACGME compliant")
    else:
        print(f"❌ {person_id}: VIOLATIONS DETECTED - ROLLBACK NEEDED!")
        await executor.rollback_swap(result.swap_id)
        break

Expected Output:

✅ person-001: ACGME compliant
✅ person-002: ACGME compliant

Step 8: Send Confirmations

# Notify both parties
await send_swap_confirmation_notification(
    swap_id=result.swap_id,
    participants=["person-001", "person-002"],
    calendar_updates=True  # Send updated calendar invites
)

print("Swap complete! Confirmations sent.")

Total Time: ~5-10 minutes (most is waiting for partner response)

Workflow Diagram

┌─────────────────────────────────────────────────────────────┐
│ SWAP REQUEST WORKFLOW                                       │
└─────────────────────────────────────────────────────────────┘

1. REQUEST SUBMISSION
   ├─ Requestor specifies shift to give away
   ├─ Optional: Suggest preferred partners
   └─ Provide reason/justification
              ↓
2. AUTO-MATCHING (if no partner specified)
   ├─ Find qualified personnel
   ├─ Check hour limits
   ├─ Score compatibility
   └─ Return top 5 matches
              ↓
3. PARTNER SELECTION
   ├─ Requestor reviews matches
   ├─ Selects preferred partner
   └─ Proposes return shift
              ↓
4. VALIDATION
   ├─ Check qualifications
   ├─ Verify ACGME compliance impact
   ├─ Confirm no double-booking
   └─ Generate warnings if any
              ↓
5. PARTNER CONSENT
   ├─ Send notification to partner
   ├─ 48-hour response window
   ├─ Partner accepts/declines/counters
   └─ If declined, return to step 2
              ↓
6. EXECUTION
   ├─ Begin database transaction
   ├─ Update assignments for both parties
   ├─ Create audit trail
   └─ Commit or rollback on error
              ↓
7. POST-EXECUTION VERIFICATION
   ├─ Re-validate ACGME compliance
   ├─ Check coverage maintained
   ├─ Update metrics
   └─ If issues, rollback immediately
              ↓
8. NOTIFICATIONS
   ├─ Send confirmations to both parties
   ├─ Update calendar invites
   ├─ Log in swap history
   └─ 24-hour rollback window begins

Common Failure Modes

Failure Mode 1: No Compatible Matches Found

Symptom: Auto-matcher returns empty list or very low compatibility scores

Cause: Shift is highly specialized, everyone at hour limits, or unusual constraints

Example:

matches = await matcher.find_compatible_partners(...)
# Returns: []

Resolution:

1. Relax constraints temporarily:

   matches = await matcher.find_compatible_partners(
       ...,
       allow_near_limit=True,  # Include people at 75+ hours
       extend_search_window=True  # Look 2 weeks out instead of 1
   )
   

2. Emergency protocol:

   # Broadcast to all qualified faculty
   await broadcast_emergency_coverage_request(
       shift=target_shift,
       reason="No matches found via auto-matcher"
   )
   

3. Escalate to coordinator:

   await create_coordinator_intervention_ticket(
       swap_request_id=swap_request.id,
       reason="No compatible partners available"
   )
   

Failure Mode 2: Partner Declines After Validation

Symptom: Swap validated successfully but partner says no

Cause: Personal reasons, schedule preference, or found better alternative

Example:

swap_request.partner_status = "declined"
swap_request.partner_decline_reason = "Prefer not to work that day"

Resolution:

1. Return to matching:

   # Exclude declined partner
   matches = await matcher.find_compatible_partners(
       ...,
       exclude_person_ids=["person-002"]  # They already declined
   )
   

2. Counter-proposal:

   # Partner might propose different return shift
   if swap_request.partner_counter_proposal:
       print(f"Partner proposes: {swap_request.partner_counter_proposal}")
       # Requestor can accept or decline
   

Failure Mode 3: ACGME Violation After Execution

Symptom: Swap executes but post-validation finds compliance issue

Cause: Validation logic bug, edge case, or cascading effect on other assignments

Example:

# Swap executes
result = await executor.execute_swap(swap_request)

# But post-validation fails
compliance_result = acgme_validator.validate_person_schedule(person_id)
# Returns: violations found - 81 hours in week of 2025-02-17

Resolution:

# Immediate rollback
await executor.rollback_swap(result.swap_id, reason="ACGME violation detected")

# Investigate why validation passed initially
await log_validation_discrepancy(
    swap_id=result.swap_id,
    pre_validation=validation_result,
    post_validation=compliance_result
)

# Fix validation logic before allowing future swaps

Failure Mode 4: Double-Booking Created

Symptom: After swap, person assigned to two shifts at same time

Cause: Race condition, concurrent swap execution, or validation bug

Example:

# Person-002 now has:
# 2025-02-13 PM: FM Clinic (from swap)
# 2025-02-13 PM: Procedures (existing assignment not caught)

Detection:

# Check for overlaps
overlaps = await db.check_person_overlaps(person_id="person-002")
if overlaps:
    print(f"❌ CRITICAL: Double-booking detected!")
    for overlap in overlaps:
        print(f"   {overlap.date} {overlap.session}: {overlap.assignments}")

Resolution:

# Rollback immediately
await executor.rollback_swap(result.swap_id, reason="Double-booking detected")

# Implement locking to prevent concurrent swaps
# Add database-level unique constraint on (person_id, date, session)

Failure Mode 5: Swap Imbalance Tracking

Symptom: Person requests many swaps, always giving away shifts, never taking them

Cause: Gaming the system, burnout avoidance, or workload inequity

Example:

swap_history = await db.get_swap_history(person_id="person-001")

# Output:
# Swaps requested: 12
# Swaps given: 12 (always giving away shifts)
# Swaps taken: 0 (never absorbing others' shifts)

Detection:

# Calculate swap balance
balance = swap_history.swaps_taken - swap_history.swaps_given
if abs(balance) > 4:
    print(f"⚠️ Swap imbalance: {balance}")

Resolution:

# Enforce balance requirement
if balance < -3:
    raise ValidationError(
        "You must accept incoming swaps before requesting more. "
        "Current balance: -3 (you've given 3 more than taken)"
    )

# Or implement points system
# Each swap given = -1 point
# Each swap taken = +1 point
# Require points >= -2 to request new swap

Integration with Other Skills

With schedule-optimization

When: Swaps have degraded schedule fairness Workflow:

1. Execute swaps via swap-management
2. Check fairness metrics (Gini coefficient)
3. If fairness degraded significantly, invoke schedule-optimization
4. Generate minimal-change rebalancing adjustments
5. Execute as coordinated swaps

With acgme-compliance

When: Every swap execution Workflow:

1. Before swap: Invoke acgme-compliance to validate
2. After swap: Invoke acgme-compliance to verify
3. If violations detected, rollback immediately
4. Document in audit trail

With resilience-dashboard

When: Checking if swaps maintain backup capacity Workflow:

1. Before executing swap, check current resilience status
2. Model impact of swap on N-1 contingency
3. If swap would degrade resilience below threshold, warn or block
4. Document resilience impact in swap audit trail

With swap-analyzer

When: Analyzing patterns for fairness or abuse detection Workflow:

1. Monthly: Invoke swap-analyzer to review all swaps
2. Identify imbalances, clustering, or suspicious patterns
3. Generate reports for program coordinator
4. Recommend policy adjustments if needed

With security-audit

When: Suspicious swap patterns detected Workflow:

1. swap-management detects unusual pattern (e.g., circular swaps)
2. Invoke security-audit to investigate
3. Check for collusion, manipulation, or system abuse
4. Escalate to human if fraud suspected