open-compute 0.1.9
Open Compute - multi-agent systems for healthtech
License: MIT
Requires Python: >=3.9

# Open Compute

**Open-source agentic AI for health-tech**

Open Compute is a powerful Python library that uses AI agents to transform patient journeys into standards-compliant FHIR (Fast Healthcare Interoperability Resources) data. Built by [Jori Health](https://jori.health), this tool enables healthcare developers to bridge the gap between narrative patient experiences and structured healthcare data.

## Quick Start

### Installation

```bash
pip install open-compute
```

### Requirements

- **Python 3.9+**
- **OpenAI API Key**: Set as environment variable
  ```bash
  export OPENAI_API_KEY='your-api-key-here'
  ```

## Usage

### Patient Journey to FHIR Conversion

The primary use case is converting narrative patient journeys into structured, validated FHIR resources:

```python
from open_compute import (
    PatientJourney,
    JourneyStage,
    generate_fhir_from_journey,
)

# 1. Define a patient journey with clinical narrative
journey = PatientJourney(
    patient_id="patient-123",
    summary="58 year old male presents to ER with chest pain, diagnosed with acute MI",
    stages=[
        JourneyStage(
            name="Registration",
            description="Patient registered in ER",
            metadata={
                "timestamp": "2024-01-15T10:30:00Z",
                "location": "Emergency Department"
            }
        ),
        JourneyStage(
            name="Triage",
            description="Initial assessment - chest pain, elevated BP",
            metadata={
                "vital_signs": {
                    "blood_pressure": "150/95 mmHg",
                    "heart_rate": "88 bpm"
                },
                "chief_complaint": "Chest pain"
            }
        ),
        JourneyStage(
            name="Diagnosis",
            description="Diagnosed with acute myocardial infarction",
            metadata={
                "condition": "Acute MI",
                "icd10_code": "I21.9"
            }
        ),
        JourneyStage(
            name="Treatment",
            description="Administered aspirin and nitroglycerin",
            metadata={
                "medications": ["Aspirin 325mg", "Nitroglycerin 0.4mg"]
            }
        ),
    ]
)

# 2. Generate FHIR resources using AI
result = generate_fhir_from_journey(
    journey=journey,
    patient_context="58 year old male named John Doe with history of hypertension",
    model="gpt-4o-mini",  # or "gpt-4", "gpt-3.5-turbo"
    fhir_version="R4",
    max_iterations=3,
    auto_save=True  # Saves to output/john_doe/
)

# 3. Check results
print(f"✅ Success: {result.success}")
print(f"📊 Generated {len(result.generated_resources)} FHIR resources")
print(f"🔄 Iterations: {result.iterations}")

# View generated resource types
for resource in result.generated_resources:
    print(f"  - {resource['resourceType']}/{resource.get('id', 'no-id')}")
```

### Output Structure

When `auto_save=True`, generated files are saved to `output/{firstname_lastname}/`:

```
output/john_doe/
├── patient_bundle.json    # Complete FHIR Bundle (all resources)
├── bulk_fhir.jsonl       # Bulk FHIR format (one resource per line)
└── README.txt            # Summary of generated resources
```

### Advanced Configuration

```python
from open_compute import AIJourneyToFHIR

# Create agent with custom configuration
agent = AIJourneyToFHIR(
    api_key="your-openai-key",  # or use env var OPENAI_API_KEY
    model="gpt-4o-mini",
    fhir_version="R4",
    max_iterations=5,
    max_fix_retries=3,
    auto_save=True,
    save_directory="output",
    parallel_generation=True,  # Faster generation
    use_enhanced_context=True  # Better accuracy with FHIR profiles
)

# Generate resources
result = agent.generate(journey, patient_context="...")
```

## Examples

We provide comprehensive examples to help you get started:

### Running the Examples

```bash
# Make sure you have your OpenAI API key set
export OPENAI_API_KEY='your-api-key-here'

# Run the main example
python examples/patient_journey_to_fhir_example.py
```

### Available Examples

| Example         | Description                                                      | File                                          |
| --------------- | ---------------------------------------------------------------- | --------------------------------------------- |
| **Basic Usage** | Complete patient journey with ER visit, diagnosis, and treatment | `examples/patient_journey_to_fhir_example.py` |

The example demonstrates:

- Creating a patient journey with multiple stages
- Generating FHIR resources (Patient, Encounter, Observation, Condition, MedicationStatement, Procedure)
- Validating generated resources
- Auto-saving to organized output directory

## Contributing

We welcome contributions! Open Compute is an open-source project and we'd love your help.

### Areas for Contribution

- Additional FHIR resource types
- Support for more FHIR versions
- Enhanced validation rules
- Performance optimizations
- Documentation improvements

### Getting Started

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run tests (`pytest`)
5. Commit your changes (`git commit -m 'Add amazing feature'`)
6. Push to the branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request

## License

MIT License - see [LICENSE](LICENSE) for details

**Made with 🏥 for better healthcare interoperability**