Digital Twin Discharge Drafter

Source: https://github.com/aipoch/medical-research-skills

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.

When to Use

• Use this skill when the task needs 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.
• Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: 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.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• dataclasses: unspecified. Declared in requirements.txt.
• dateutil: unspecified. Declared in requirements.txt.

Example Usage

cd "20260318/scientific-skills/Academic Writing/digital-twin-discharge-drafter"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Quick Start

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

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

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:

3. Personalized Patient Instructions

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

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:

5. Quality Assurance

qa_report = drafter.validate_summary(
    discharge_summary,
    checks=[
        "completeness_jcaho",
        "medication_accuracy",
        "readability_score",
        "prediction_confidence"
    ]
)

CLI Usage

# 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