Core models and base classes for Healthcare Agent Communication Standard
Project description
HACS Core
Foundation models for Healthcare Agent Communication Standard
Core Pydantic models and base classes that define the healthcare AI communication protocol.
🏥 Healthcare Models
Essential healthcare data structures optimized for AI agent communication:
- Patient - Demographics, contact info, clinical context
- Observation - Clinical measurements, lab results, vital signs
- Encounter - Healthcare visits, episodes of care
- Actor - Healthcare providers with role-based permissions
- MemoryBlock - Structured memory for AI clinical reasoning
- Evidence - Clinical guidelines, research, decision support
🎯 Key Features
- FHIR Compatible - Full alignment with healthcare standards
- AI Optimized - Structured for LLM processing and tool calling
- Validation Built-in - Healthcare-specific validation rules
- Actor Security - Role-based access control for clinical data
- Memory System - Episodic, procedural, and executive memory types
📦 Installation
pip install hacs-core
🚀 Quick Start
from hacs_core import Patient, Observation, Actor, MemoryBlock
# Healthcare provider
physician = Actor(
name="Dr. Sarah Chen",
role="PHYSICIAN",
organization="Mount Sinai Health System"
)
# Patient record
patient = Patient(
full_name="Maria Rodriguez",
birth_date="1985-03-15",
gender="female",
active=True
)
# Clinical observation
bp_reading = Observation(
code_text="Blood Pressure",
value="145/90",
unit="mmHg",
status="final",
patient_id=patient.id
)
# Clinical memory
memory = MemoryBlock(
content="Patient presents with elevated BP, discussed lifestyle modifications",
memory_type="episodic",
importance_score=0.8
)
🔗 Integration
HACS Core models work seamlessly with:
- MCP Tools - 25+ healthcare tools via Model Context Protocol
- LangGraph - AI agent workflows with clinical memory
- PostgreSQL - Persistent storage with pgvector
- FHIR Systems - Healthcare standards compliance
📄 License
Apache-2.0 License - see LICENSE for details.
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