| Name | hacs-models JSON |
| Version |
0.4.3
JSON |
| download |
| home_page | None |
| Summary | HACS Healthcare Data Models - Pure Pydantic models for FHIR-compliant healthcare data |
| upload_time | 2025-08-12 14:27:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT |
| keywords |
agents
ai
fhir
healthcare
models
pydantic
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
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No coveralls.
|
# HACS Models
**Pure Healthcare Data Models for AI Agent Systems**
[](https://www.python.org/downloads/)
[](http://mypy-lang.org/)
[](https://github.com/astral-sh/ruff)
[](https://hl7.org/fhir/)
## Overview
`hacs-models` provides pure, type-safe Pydantic data models for healthcare applications. These models are designed for AI agent communication and are fully compliant with FHIR R4/R5 standards.
## Design Principles
- **Pure Data Models**: No business logic, just data structures
- **Type Safety**: Full type annotations with mypy strict mode
- **FHIR Compliance**: Adherent to healthcare data standards
- **Zero Dependencies**: Minimal dependency footprint (only Pydantic)
- **Immutable Design**: Designed for functional programming patterns
- **AI-Optimized**: Structured for AI agent communication
## Features
### Core Healthcare Models
- `Patient` - Patient demographics and identifiers
- `Observation` - Clinical observations and measurements
- `Encounter` - Healthcare encounters and visits
- `Condition` - Medical conditions and diagnoses
- `Medication` - Medication information
- `MedicationRequest` - Medication prescriptions
- `Procedure` - Medical procedures
- `Goal` - Care goals and objectives
### Specialized Models
- `MemoryBlock` - AI agent memory structures
- `AgentMessage` - Inter-agent communication
- `ResourceBundle` - FHIR resource collections
- `WorkflowDefinition` - Clinical workflow definitions
### Base Classes
- `BaseResource` - Foundation for all healthcare resources
- `DomainResource` - Base for domain-specific resources
- `BackboneElement` - Reusable data structures
## Installation
```bash
# Install from PyPI (when published)
pip install hacs-models
# Install in development mode
uv add -e packages/hacs-models
```
## Quick Start
```python
from hacs_models import Patient, Observation
from datetime import date
# Create a patient
patient = Patient(
id="patient-001",
full_name="Jane Doe",
birth_date=date(1990, 1, 15),
gender="female"
)
# Create an observation
observation = Observation(
id="obs-001",
subject_reference=f"Patient/{patient.id}",
code="85354-9", # Blood pressure
value_quantity={"value": 120, "unit": "mmHg"}
)
# Models are immutable and type-safe
print(f"Patient: {patient.full_name}")
print(f"Blood Pressure: {observation.value_quantity}")
```
## Architecture
```
hacs-models/
├── base_resource.py # BaseResource, DomainResource
├── patient.py # Patient model
├── observation.py # Observation model
├── encounter.py # Encounter model
├── condition.py # Condition model
├── medication.py # Medication models
├── procedure.py # Procedure model
├── goal.py # Goal model
├── memory.py # AI memory models
├── workflow.py # Workflow models
└── types.py # Common types and enums
```
## Development
```bash
# Run tests
uv run pytest
# Type checking
uv run mypy src/hacs_models
# Code formatting
uv run ruff format src/hacs_models
# Linting
uv run ruff check src/hacs_models
```
## Contributing
1. Fork the repository
2. Create a feature branch
3. Add tests for new models
4. Ensure 100% type coverage
5. Submit a pull request
## License
MIT License - see LICENSE file for details.
Raw data
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"description": "# HACS Models\n\n**Pure Healthcare Data Models for AI Agent Systems**\n\n[](https://www.python.org/downloads/)\n[](http://mypy-lang.org/)\n[](https://github.com/astral-sh/ruff)\n[](https://hl7.org/fhir/)\n\n## Overview\n\n`hacs-models` provides pure, type-safe Pydantic data models for healthcare applications. These models are designed for AI agent communication and are fully compliant with FHIR R4/R5 standards.\n\n## Design Principles\n\n- **Pure Data Models**: No business logic, just data structures\n- **Type Safety**: Full type annotations with mypy strict mode\n- **FHIR Compliance**: Adherent to healthcare data standards\n- **Zero Dependencies**: Minimal dependency footprint (only Pydantic)\n- **Immutable Design**: Designed for functional programming patterns\n- **AI-Optimized**: Structured for AI agent communication\n\n## Features\n\n### Core Healthcare Models\n- `Patient` - Patient demographics and identifiers\n- `Observation` - Clinical observations and measurements \n- `Encounter` - Healthcare encounters and visits\n- `Condition` - Medical conditions and diagnoses\n- `Medication` - Medication information\n- `MedicationRequest` - Medication prescriptions\n- `Procedure` - Medical procedures\n- `Goal` - Care goals and objectives\n\n### Specialized Models\n- `MemoryBlock` - AI agent memory structures\n- `AgentMessage` - Inter-agent communication\n- `ResourceBundle` - FHIR resource collections\n- `WorkflowDefinition` - Clinical workflow definitions\n\n### Base Classes\n- `BaseResource` - Foundation for all healthcare resources\n- `DomainResource` - Base for domain-specific resources\n- `BackboneElement` - Reusable data structures\n\n## Installation\n\n```bash\n# Install from PyPI (when published)\npip install hacs-models\n\n# Install in development mode\nuv add -e packages/hacs-models\n```\n\n## Quick Start\n\n```python\nfrom hacs_models import Patient, Observation\nfrom datetime import date\n\n# Create a patient\npatient = Patient(\n id=\"patient-001\",\n full_name=\"Jane Doe\",\n birth_date=date(1990, 1, 15),\n gender=\"female\"\n)\n\n# Create an observation\nobservation = Observation(\n id=\"obs-001\",\n subject_reference=f\"Patient/{patient.id}\",\n code=\"85354-9\", # Blood pressure\n value_quantity={\"value\": 120, \"unit\": \"mmHg\"}\n)\n\n# Models are immutable and type-safe\nprint(f\"Patient: {patient.full_name}\")\nprint(f\"Blood Pressure: {observation.value_quantity}\")\n```\n\n## Architecture\n\n```\nhacs-models/\n\u251c\u2500\u2500 base_resource.py # BaseResource, DomainResource\n\u251c\u2500\u2500 patient.py # Patient model\n\u251c\u2500\u2500 observation.py # Observation model \n\u251c\u2500\u2500 encounter.py # Encounter model\n\u251c\u2500\u2500 condition.py # Condition model\n\u251c\u2500\u2500 medication.py # Medication models\n\u251c\u2500\u2500 procedure.py # Procedure model\n\u251c\u2500\u2500 goal.py # Goal model\n\u251c\u2500\u2500 memory.py # AI memory models\n\u251c\u2500\u2500 workflow.py # Workflow models\n\u2514\u2500\u2500 types.py # Common types and enums\n```\n\n## Development\n\n```bash\n# Run tests\nuv run pytest\n\n# Type checking \nuv run mypy src/hacs_models\n\n# Code formatting\nuv run ruff format src/hacs_models\n\n# Linting\nuv run ruff check src/hacs_models\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Add tests for new models\n4. Ensure 100% type coverage\n5. Submit a pull request\n\n## License\n\nMIT License - see LICENSE file for details.",
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