Metadata-Version: 2.3
Name: hospital-sim
Version: 0.0.1
Summary: HospitalSim - Swarms
License: MIT
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering
Author: Kye Gomez
Author-email: kye@swarms.world
Requires-Python: >=3.10,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: chromadb
Requires-Dist: loguru
Requires-Dist: swarms
Project-URL: Documentation, https://github.com/The-Swarm-Corporation/HospitalSim
Project-URL: Homepage, https://github.com/The-Swarm-Corporation/HospitalSim
Project-URL: Repository, https://github.com/The-Swarm-Corporation/HospitalSim
Description-Content-Type: text/markdown


# HospitalSim

![BACK GROUND IMAGE](bg.jpg)

**Enterprise-Grade Hospital Management & Simulation System**

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## Overview

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.

## System Architecture Flow

```mermaid
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

```

## Key Features

### Multi-Agent Architecture
- **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

### Advanced Patient 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

### Electronic Health Records (EHR)
- **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

### Operational Intelligence
- **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

## Technical Architecture

### Core Components

#### Patient Class
```python
@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
```

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

#### Hospital Staff Framework
```python
class HospitalStaff:
    - Role-based agent specialization
    - Performance metrics tracking
    - Dynamic patient assignment
    - Collaborative decision-making capabilities
```

### Simulation Engine

#### Real-time Operations
- Continuous patient flow processing
- Dynamic resource allocation
- Staff availability management
- Performance optimization algorithms

#### Strategic Management
- Executive team meetings for hospital strategy
- Financial performance analysis
- Quality improvement initiatives
- Resource planning and allocation

## Installation

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

### Setup

1. **Clone the repository**
   ```bash
   git clone https://github.com/yourusername/HospitalSim.git
   cd HospitalSim
   ```

2. **Install dependencies**
   ```bash
   pip install -r requirements.txt
   ```

3. **Configure environment**
   ```bash
   export OPENAI_API_KEY="your-api-key-here"
   ```

### Dependencies
- **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

## Usage

### Basic Hospital Simulation

```python
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
)
```

### Single Patient Processing

```python
# 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")
```

### EHR System Integration

```python
# 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"
)
```

## Business Benefits

### Operational Efficiency
- **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

### Financial Performance
- **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

### Quality of Care
- **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

### Strategic Management
- **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

## Configuration

### Staff Configuration
The system supports customizable staff roles and specializations:

```python
# 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
```

### Performance Metrics
Monitor key performance indicators:

```python
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
}
```

## Advanced Features

### AI-Powered Decision Making
- Natural language processing for patient-staff interactions
- Machine learning algorithms for predictive analytics
- Automated triage and priority assignment
- Intelligent resource allocation and scheduling

### Enterprise Integration
- 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

### Compliance and Security
- 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

## Support and Documentation

### Getting Help
- **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

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

## License

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

## About

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.
