> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hana.health/llms.txt
> Use this file to discover all available pages before exploring further.

# HANA Overview

> A concise overview of HANA's agentic system design and infrastructure, providing a high-level understanding of the system architecture and technology underlying HANA's healthcare voice agents.

## Welcome

HANA is a platform that empowers healthcare organizations to deploy voice-based patient engagement agents. These agents automate outbound and inbound patient conversations, streamline clinical data collection, and improve care coordination — all while maintaining the highest standards of clinical safety, security, and reliability.

Built from clinical voice research in behavioral health populations and refined across nearly one million patient interactions, HANA's architecture is designed for the specific challenges of healthcare: safety-critical conversations, longitudinal patient relationships, and integration with clinical workflows.

### Clinical Domains

HANA has been deployed and validated across diverse clinical settings:

* **Behavioral Health**: Bipolar disorder, PTSD, depression, ADHD — including longitudinal monitoring and crisis detection
* **Primary Care**: Chronic care management, medication adherence, preventive screening outreach
* **Surgical / Specialty Care**: Pre-operative preparation, post-operative follow-up, recovery milestone tracking
* **Palliative Care**: End-of-life care coordination, symptom monitoring, caregiver support
* **Multi-Specialty Clinics**: Dermatology, orthopedics, physical rehabilitation, psychotherapy

### ROI-First Approach

1. Provide an alternative to manual patient outreach at a fraction of the staff cost per engagement
2. Success metrics include: patient completion rates, recovered appointments, accurate clinical data extraction, and CMS-billable conversation documentation
3. Validated across pilot deployments; organizations can measure ROI against their own baseline during pilot phase

### Safe, Reliable, and Certain

1. Our team includes experienced healthcare operators who have managed clinical workflows across behavioral health, primary care, and multi-specialty organizations
2. Our technology provides seamless integration with existing EHR systems, enabling smooth adaptation to clinical workflows
3. Unlike text-based engagement tools, HANA offers voice-first patient interaction with high completion rates and minimal staff burden
4. Rule-based safety guardrails with full traceability — not black-box LLM safety — ensure deterministic risk detection and auditable escalation

### Highly Modular & Customizable

1. Tailor solutions to fit the unique needs of your organization with our highly modular agent system
2. Customize protocols, voice personas, and clinical workflows to match existing processes
3. Easily scale and modify agents to adapt to changing requirements and growth

***

## System Card

**Scalable Architecture for Voice-Based Healthcare Agents**

### Design Principles

* **Isolated**: Each agent operates independently with its own protocol configuration and data context
* **Self-contained**: Agents include all necessary components for end-to-end conversation execution
* **Composable**: Agents can be combined for complex workflows (e.g., intake → screening → scheduling)
* **Customizable**: Every aspect of agent behavior is configurable per organization, specialty, and patient

### High-Level Architecture

```mermaid theme={null}
graph TB
    subgraph Shared["Shared Services"]
        AUTH["Authentication (SAML/OIDC)"]
        TEL["Telephony (SIP Trunking)"]
        EHRG["EHR Gateway (FHIR R4)"]
        QUEUE["Queue System"]
        EVALPIPE["Evaluation Pipeline"]
        OBSRV["Observability"]
    end

    subgraph Agent["Agent Package"]
        PROTO["Protocol Configuration"]
        RE["Reasoning Engine\n(GPU · Pre-Call Planning)"]
        CE["Conversation Engine\n(CPU · Real-Time)"]
        EHRC["EHR Connector"]
        QE["Quality Evaluation"]
    end

    subgraph Safety["Safety Layer"]
        OSA["Observational Safety Agents\n(Rule-Based · Deterministic)"]
        VI["Vocal Intelligence\n(Prosodic Analysis)"]
    end

    PROTO --> RE
    RE -->|Conversation Plan| CE
    EHRC --> RE
    EHRC --> CE
    TEL <--> CE
    EHRG <--> EHRC
    EVALPIPE --> QE
    AUTH --> CE
    CE --> QE
    OSA -->|Parallel Monitoring| CE
    VI -->|Audio Features| OSA
    VI -->|Engagement Signals| CE
    QUEUE --> RE
    QUEUE --> CE
```

#### System Architecture Characteristics

1. **Dual-Model Design**: Each conversation is powered by two distinct AI systems — a pre-call Reasoning Engine for planning and a real-time Conversation Engine for execution
2. **Consistent Stack**: Standard technology stack across all agents: Python/Node.js services, PostgreSQL, Redis, cloud object storage, and FHIR-based EHR integration
3. **Multi-Modal Support**: System supports voice (outbound/inbound calls), SMS, and messaging channels
4. **Headless Agents**: Some agents operate without a patient-facing interface — processing EHR data, generating care gap lists, or coordinating between other agents via APIs

### Horizontal Components/Services

1. **Authentication**: Unified authentication mechanism across all agent services using SAML/OIDC with enterprise IdP integration
2. **Telephony**: Centralized SIP trunking and voice synthesis infrastructure shared across all voice agents
3. **EHR Gateway**: Standardized FHIR R4 interface providing consistent EHR read/write across Epic, Cerner, Athena, and other systems
4. **Queue System**: Enables agents to send and receive messages, trigger actions, and share results across the conversation pipeline
5. **Evaluation Pipeline**: Shared quality assessment infrastructure using LLM-as-judge and rule-based validation
6. **Observability**: Centralized logging, metrics, and tracing across all agent services

### Agent Package Components

Each agent package includes:

1. **Protocol Configuration**: Clinical workflow definition including questions, branching logic, escalation triggers, and data extraction rules
2. **Reasoning Engine Integration**: Pre-call planning that generates conversation plans from patient EHR data and protocol rules
3. **Conversation Engine**: Real-time dialogue management with constrained response generation
4. **EHR Connector**: Bi-directional data exchange with the patient's health record
5. **Quality Evaluation**: Automated post-conversation quality scoring and error detection
6. **Analytics**: Conversation outcome tracking, completion metrics, and clinical data extraction reporting

### Sample Agent Design

**Design Exercise — Voice Engagement Agent**

**Agent Package Components:**

* Authentication: Enabled by default for secure EHR access
* Telephony: SIP integration for outbound/inbound voice
* Protocol Engine: Configurable clinical conversation workflows

**Infrastructure:**

* Database: Patient context cache, conversation state, and extracted data
* REST APIs: Conversation initiation, status, outcomes, and transcript retrieval
* WebSocket: Real-time streaming for voice synthesis and recognition

**Implementation:**

1. API Endpoints: Conversation CRUD, protocol management, EHR sync
2. Reasoning Service: Pre-call planning using patient data and protocol rules
3. Conversation Service: Real-time dialogue with constrained generation
4. Containerization: Kubernetes deployment with auto-scaling based on concurrent call volume