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 - 42+ Hacs 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.