Skill: aipoch-ai/digital-twin-discharge-drafter (version 0.1.0)

--- name: digital-twin-discharge-drafter description: Use when drafting patient discharge summaries, creating personalized discharge instructions, simulating post-discharge outcomes, reducing hospital readmissions, or optimizing care transitions. Generates AI-enhanced discharge documentation with digital twin predictions for improved patient safety. allowed-tools: "Read Write Bash Edit" license: MIT metadata: skill-author: AIPOCH version: "1.0" --- # Digital Twin Discharge Drafter Generate AI-enhanced discharge summaries and personalized care plans using digital twin patient models to predict outcomes and optimize post-discharge care transitions. ## Quick Start ```python from scripts.discharge_drafter import DischargeDrafter drafter = DischargeDrafter() # Generate comprehensive discharge summary summary = drafter.generate( patient_id="PT12345", admission_data=admission_info, hospital_course=treatment_history, digital_twin_model=patient_model, output_format="structured" ) # Export patient-friendly version patient_version = drafter.generate_patient_friendly(summary) print(summary.readmission_risk_score) # 0.23 print(summary.key_interventions) # ['home_health', 'med_reconciliation'] ``` ## Core Capabilities ### 1. Digital Twin-Powered Summary Generation ```python summary = drafter.create_summary( patient_data=patient_record, digital_twin_model=twin_model, include_predictions=True, risk_stratification="high", readmission_risk_threshold=0.15 ) ``` **Summary Components:** - **Hospital Course**: AI-summarized treatment narrative - **Digital Twin Predictions**: 7-day, 30-day outcome probabilities - **Risk Stratification**: Readmission risk score with factors - **Medication Reconciliation**: AI-validated med list - **Follow-up Schedule**: Optimized based on patient model ### 2. Post-Discharge Outcome Simulation ```python scenarios = drafter.simulate_outcomes( patient_model=digital_twin, scenarios=[ "medication_adherent", "medication_non_adherent", "follow_up_missed", "social_support_optimal" ], timeframe="30_days", metrics=["readmission_risk", "recovery_trajectory", "cost_projection"] ) ``` **Simulation Outputs:** | Scenario | Readmission Risk | Recovery Time | Cost Impact | |----------|-----------------|---------------|-------------| | Optimal adherence | 5% | 14 days | Baseline | | Med non-adherent | 25% | 28 days | +$8,500 | | Missed follow-up | 18% | 21 days | +$4,200 | ### 3. Personalized Patient Instructions ```python instructions = drafter.create_personalized_instructions( patient_profile=profile, health_literacy_level="assessed", # or "8th_grade", "college" language_preference="English", cultural_considerations=True, access_barriers=["transportation", "cost"] ) # Returns structured instructions print(instructions.medication_list) # Formatted medication table print(instructions.followup_appointments) # Scheduled visits print(instructions.red_flags) # When to call doctor print(instructions.lifestyle_changes) # Diet, activity restrictions ``` **Personalization Factors:** - **Health Literacy**: Adjust complexity (Flesch-Kincaid 6th-12th grade) - **Language**: Multi-language support with medical accuracy - **Cultural**: Dietary restrictions, family dynamics, beliefs - **Barriers**: Transportation, cost, caregiver availability ### 4. Risk-Based Care Planning ```python care_plan = drafter.create_risk_based_plan( patient_risk_score=0.72, risk_factors=["CHF", "diabetes", "living_alone"], interventions=[ "telehealth_monitoring", "home_health_visit", "pharmacy_consult" ] ) ``` **Risk Stratification:** | Risk Level | Score | Interventions | |------------|-------|---------------| | Low | <0.10 | Standard discharge + phone follow-up | | Moderate | 0.10-0.25 | + Telehealth monitoring | | High | 0.25-0.50 | + Home health visit within 48h | | Very High | >0.50 | + Care coordination + daily check-ins | ### 5. Quality Assurance ```python qa_report = drafter.validate_summary( discharge_summary, checks=[ "completeness_jcaho", "medication_accuracy", "readability_score", "prediction_confidence" ] ) ``` ## CLI Usage ```bash # Generate complete discharge package python scripts/discharge_drafter.py \ --patient PT12345 \ --digital-twin-model models/patient_v2.pkl \ --include-predictions \ --output-format both \ --output-dir discharge_summaries/ # Batch process high-risk patients python scripts/discharge_drafter.py \ --batch high_risk_patients.csv \ --priority ICU,CCU \ --auto-escalate-risk 0.30 # Generate patient-friendly only python scripts/discharge_drafter.py \ --patient PT12345 \ --mode patient-friendly \ --reading-level 6th_grade \ --language Spanish \ --output patient_handout.pdf ``` ## Common Patterns ### Pattern 1: CHF Patient Discharge **Digital Twin Insights:** - Baseline readmission risk: 22% - With medication adherence: 8% - Without follow-up: 35% **Generated Interventions:** - Daily weight telemonitoring - Cardiology appointment within 7 days - Medication reconciliation with pharmacist - Home health evaluation ### Pattern 2: Post-Surgical Patient **Digital Twin Insights:** - Infection risk peaks day 3-5 - Mobility compliance critical for recovery **Generated Plan:** - Wound care video instructions - Physical therapy schedule - Red flag symptom checklist - Pain management protocol ## Quality Checklist **Pre-Discharge:** - [ ] Digital twin model updated with hospital course - [ ] Readmission risk calculated and documented - [ ] Medication reconciliation completed - [ ] Follow-up appointments scheduled - [ ] Patient/caregiver education requirements assessed **Discharge Summary:** - [ ] Includes digital twin predictions with confidence intervals - [ ] Risk factors clearly listed with mitigation strategies - [ ] Patient-friendly instructions at appropriate literacy level - [ ] Emergency contact numbers provided - [ ] 24/7 nurse line access included **Post-Discharge (24-48 hours):** - [ ] Automated follow-up call triggered - [ ] Pharmacy notified of new prescriptions - [ ] Primary care provider receives summary - [ ] Home health services activated (if indicated) ## Best Practices **Digital Twin Model Maintenance:** - Update models weekly with new patient data - Validate predictions against actual outcomes - Retrain models quarterly for accuracy improvement **Patient Communication:** - Always provide both clinical and patient-friendly versions - Use teach-back method to confirm understanding - Document health literacy level in patient record ## Common Pitfalls ❌ **Over-reliance on AI**: Digital twin predictions supplement, not replace, clinical judgment ✅ **Clinical Oversight**: Physician reviews and approves all AI-generated content ❌ **Generic Instructions**: One-size-fits-all discharge plans ✅ **Personalized Plans**: Tailored to individual patient models and barriers ❌ **Ignoring Low-Risk Patients**: Focusing only on high-risk cases ✅ **Universal Application**: All patients benefit from digital twin insights --- **Skill ID**: 214 | **Version**: 1.0 | **License**: MIT


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