Enterprise-Grade Hospital Management & Simulation System

HospitalSim is a sophisticated multi-agent hospital management and simulation system designed to optimize healthcare operations through intelligent automation. This enterprise-grade solution provides comprehensive patient care simulation, electronic health records management, and operational analytics to improve hospital efficiency and patient outcomes.

flowchart TD
    A[Patient Arrives] --> B[Reception Check-in]
    B --> C[Patient Queue]
    C --> D{Triage Assessment}
    D --> E[Priority Scoring]
    E --> F{Emergency?}
    F -->|Yes| G[Emergency Doctor]
    F -->|No| H[General Doctor]
    G --> I[Consultation]
    H --> I[Consultation]
    I --> J[Diagnosis & Treatment Plan]
    J --> K[EHR Documentation]
    K --> L[Patient Discharge]
    
    M[Executive Team] --> N[Strategic Meetings]
    N --> O[Hospital Operations]
    O --> P[Performance Analytics]
    P --> Q[Resource Optimization]
    
    R[ChromaDB EHR] --> S[Patient History]
    S --> I
    R --> T[Similar Cases]
    T --> I
    
    U[AI Agents] --> B
    U --> D
    U --> G
    U --> H
    U --> M

• Executive Team: CEO, CFO, and CMO agents for strategic decision-making
• Medical Staff: Specialized doctors (Emergency Medicine, General Practice) with domain expertise
• Nursing Team: Triage and floor nurses for comprehensive patient care
• Administrative Staff: Receptionists for patient check-in and queue management

• Intelligent Priority Scoring: Dynamic patient triage based on symptoms and vital signs
• Queue Optimization: Priority-based patient queue with estimated wait times
• Real-time Status Tracking: Complete patient journey monitoring
• Multi-step Care Pipeline: Reception → Triage → Consultation → Treatment → Documentation

• ChromaDB Integration: Advanced RAG (Retrieval-Augmented Generation) system for medical records
• Historical Data Access: Comprehensive patient history retrieval and analysis
• Similar Case Matching: AI-powered similarity search for diagnostic support
• Persistent Storage: Reliable data persistence with fallback mechanisms

• Performance Analytics: Real-time metrics on patient throughput, wait times, and satisfaction
• Financial Modeling: Revenue and cost analysis with profit optimization
• Executive Decision Making: Automated strategic planning through executive team collaboration
• Quality Assurance: Continuous monitoring of care quality and staff performance

@dataclass
class Patient:
    - Comprehensive medical information storage
    - AI agent integration for realistic patient interactions
    - Dynamic priority calculation based on clinical indicators
    - Full medical history and vital signs tracking

class EHRSystem:
    - ChromaDB vector database integration
    - Semantic search capabilities for medical records
    - Automated documentation and record keeping
    - Historical data analysis and retrieval

class HospitalStaff:
    - Role-based agent specialization
    - Performance metrics tracking
    - Dynamic patient assignment
    - Collaborative decision-making capabilities

• Continuous patient flow processing
• Dynamic resource allocation
• Staff availability management
• Performance optimization algorithms

• Executive team meetings for hospital strategy
• Financial performance analysis
• Quality improvement initiatives
• Resource planning and allocation

• Python 3.8+
• OpenAI API key (for GPT-4 integration)

1. Clone the repository

   git clone https://github.com/yourusername/HospitalSim.git
   cd HospitalSim

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment

   export OPENAI_API_KEY="your-api-key-here"

• swarms: Multi-agent framework for intelligent collaboration
• chromadb: Vector database for advanced medical record storage
• pydantic: Data validation and serialization
• loguru: Advanced logging and monitoring

from hospital_sim.main import HospitalSimulation

# Initialize hospital
hospital = HospitalSimulation(hospital_name="General Hospital")

# Generate sample patients
hospital.generate_patients(num_patients=5)

# Run simulation
hospital.run_simulation(
    duration_minutes=60,
    patient_arrival_rate=0.1
)

# Process individual patient
patient_record = {
    "name": "John Doe",
    "age": 45,
    "gender": "Male",
    "chief_complaint": "Chest pain",
    "symptoms": ["chest pain", "shortness of breath"],
    "medical_history": ["hypertension"],
    "current_medications": ["lisinopril"],
    "allergies": ["penicillin"]
}

result = hospital.run(patient_record)
print(f"Treatment completed: {result['status']}")
print(f"Diagnosis: {result['diagnosis']}")
print(f"Treatment time: {result['total_time_minutes']:.1f} minutes")

# Query patient history
history = hospital.ehr_system.query_patient_history(
    patient_id="patient_123",
    query="chest pain symptoms"
)

# Search similar cases
similar_cases = hospital.ehr_system.search_similar_cases(
    symptoms=["chest pain", "shortness of breath"],
    diagnosis="myocardial infarction"
)

• Reduced Wait Times: Intelligent patient prioritization and resource allocation
• Optimized Staff Utilization: Dynamic assignment based on availability and expertise
• Streamlined Workflows: Automated patient flow from admission to discharge
• Data-Driven Decisions: Real-time analytics for operational improvements

• Revenue Optimization: Efficient patient throughput and billing optimization
• Cost Management: Automated cost tracking and resource optimization
• Profit Analysis: Comprehensive financial modeling and forecasting
• ROI Tracking: Performance metrics for investment decision-making

• Clinical Decision Support: AI-powered diagnostic assistance and treatment recommendations
• Patient Safety: Comprehensive monitoring and risk assessment
• Care Coordination: Seamless communication between medical staff
• Outcome Tracking: Long-term patient health monitoring and follow-up

• Executive Oversight: Automated strategic planning and decision-making
• Performance Monitoring: Real-time dashboards for key performance indicators
• Growth Planning: Data-driven expansion and capacity planning
• Quality Assurance: Continuous improvement through AI-powered analysis

The system supports customizable staff roles and specializations:

# Executive Team Roles
- CEO: Strategic planning and hospital growth
- CFO: Financial management and cost optimization  
- CMO: Medical quality assurance and clinical protocols

# Medical Staff Roles
- Emergency Physician: Rapid assessment and emergency care
- General Practitioner: Comprehensive patient evaluation and care
- Triage Nurse: Initial assessment and priority assignment
- Floor Nurse: Patient care and treatment implementation

Monitor key performance indicators:

simulation_stats = {
    "total_patients": 0,
    "patients_treated": 0,
    "average_wait_time": 0.0,
    "patient_satisfaction": 0.0,
    "revenue": 0.0,
    "costs": 0.0,
    "net_profit": 0.0
}

• Natural language processing for patient-staff interactions
• Machine learning algorithms for predictive analytics
• Automated triage and priority assignment
• Intelligent resource allocation and scheduling

• RESTful APIs for third-party system integration
• Database connectivity for existing hospital information systems
• Real-time data synchronization and backup
• Scalable architecture for multi-location deployments

• HIPAA-compliant data handling and storage
• Audit trails for all patient interactions and decisions
• Role-based access control and authentication
• Encrypted data transmission and storage

• Technical Support: Create an issue in the GitHub repository
• Documentation: Comprehensive API documentation available
• Community: Join our Discord server for community support
• Enterprise Support: Contact us for enterprise licensing and support

We welcome contributions from the healthcare technology community. Please see our contributing guidelines for more information.

This project is licensed under the MIT License. See the LICENSE file for details.

HospitalSim represents the future of healthcare management systems, combining artificial intelligence with proven hospital operations methodologies to create a comprehensive solution for modern healthcare facilities. Our mission is to improve patient outcomes while optimizing operational efficiency through intelligent automation and data-driven decision making.

For more information about enterprise deployment, training, or custom development, please contact our team.