🩺 Docvoxia

Real-Time Multilingual Clinical Reasoning Agent powered by Microsoft Foundry IQ

Transforming doctor-patient conversations into safe, explainable, and interoperable healthcare records through multi-agent clinical reasoning.

🚀 Live Demo

🌐 Try Docvoxia Now

https://try-docvoxia.justprompt.in/

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Experience multilingual clinical conversations, AI-powered diagnostic reasoning, medication safety validation, evidence-backed recommendations, and FHIR-compliant healthcare documentation directly in your browser.

🎯 Problem Statement

Healthcare professionals spend a significant portion of their time documenting consultations and generating prescriptions.

Challenges include:

Manual documentation workflows
Medication errors
Multilingual communication barriers
Inconsistent clinical records
Limited interoperability across healthcare systems
Lack of explainable AI decision support

Medication errors remain one of the most common causes of preventable patient safety incidents.

Traditional speech-to-text systems only transcribe conversations.

They do not understand clinical context, validate decisions, or generate interoperable healthcare records.

💡 Our Solution

Docvoxia is a Real-Time Multilingual Clinical Reasoning Agent that goes beyond transcription.

Using a multi-agent architecture powered by Microsoft Foundry, Foundry IQ, and advanced clinical reasoning workflows, Docvoxia:

✅ Understands multilingual doctor-patient conversations

✅ Retrieves relevant medical knowledge using Foundry IQ

✅ Performs multi-step clinical reasoning

✅ Detects medication risks and interactions

✅ Generates explainable recommendations

✅ Produces FHIR-compliant healthcare records

✅ Maintains doctor oversight through Human-in-the-Loop approval

🧠 Why Docvoxia?

Most healthcare AI systems focus on:

Conversation
↓
Transcription
↓
Output

Docvoxia performs:

Conversation
↓
Knowledge Retrieval
↓
Clinical Reasoning
↓
Medication Safety Validation
↓
Evidence Validation
↓
FHIR Generation
↓
Doctor Approval

This transforms AI from a transcription assistant into a Clinical Decision Intelligence System.

🏗️ System Architecture

The complete architecture is shown below:

The architecture consists of four major layers:

1. Conversation Intelligence Layer

Processes multilingual consultations using:

Speech Recognition
Speaker Diarization
Language Detection
Medical Entity Extraction
Clinical Context Understanding

2. Foundry IQ Knowledge Layer

Provides grounded retrieval using:

Patient History
Previous Encounters
Clinical Guidelines
Hospital Protocols
Drug References
Laboratory Reports

This layer helps reduce hallucinations and enables evidence-backed recommendations.

3. Multi-Agent Clinical Reasoning Layer

A Clinical Reasoning Orchestrator coordinates multiple specialized agents:

Agent	Responsibility
Patient Intake Agent	Extract patient information
Context Retrieval Agent	Retrieve medical context
Diagnostic Reasoning Agent	Generate diagnostic hypotheses
Medication Safety Agent	Detect risks and interactions
Prescription Agent	Generate prescriptions
Follow-Up Agent	Generate care instructions
Evidence Validation Agent	Validate recommendations

4. Healthcare Interoperability Layer

Generates:

FHIR R4 Resources
SNOMED CT Standardization
Structured Clinical Records

All outputs remain under physician control through Human-in-the-Loop approval workflows.

🔄 End-to-End Workflow

Doctor + Patient Conversation
            ↓
Conversation Intelligence Layer
            ↓
Foundry IQ Retrieval
            ↓
Clinical Reasoning Orchestrator
            ↓
Diagnostic Reasoning Agent
            ↓
Medication Safety Validation
            ↓
Evidence Validation
            ↓
FHIR Record Generation
            ↓
Doctor Review & Approval
            ↓
Hospital Information System

🤖 Multi-Agent Reasoning Workflow

Docvoxia implements a Planner → Executor → Verifier architecture.

Step 1: Patient Intake Agent

Extracts:

Symptoms
Medical History
Current Medications
Allergies
Demographics

Step 2: Context Retrieval Agent

Queries Foundry IQ for:

Previous Encounters
Clinical Guidelines
Hospital SOPs
Drug References

Step 3: Diagnostic Reasoning Agent

Performs:

Symptom Analysis
Differential Diagnosis
Confidence Scoring
Clinical Hypothesis Generation

Step 4: Medication Safety Agent

Validates:

Drug Interactions
Contraindications
Allergy Risks
Dosage Issues

Step 5: Evidence Validation Agent

Verifies all recommendations against:

Retrieved Knowledge
Clinical Guidelines
Drug References

Every recommendation is grounded and explainable.

🏥 Example Workflow

Patient

"I have fever, sore throat, and body pain for the last three days. I am diabetic and currently taking Metformin."

Foundry IQ Retrieves

Previous diabetic history
Clinical fever guidelines
Drug reference information

Diagnostic Reasoning Agent

Possible Diagnosis:

Viral Fever
Upper Respiratory Infection

Confidence: 91%

Medication Safety Agent

Checks:

Existing diabetes medication
Drug interactions
Contraindications

Evidence Validation Agent

Confirms recommendations against retrieved medical knowledge.

Output

FHIR Record
Structured Prescription
Follow-Up Instructions
Safety Report

🛡️ Responsible AI & Safety

Healthcare requires strong safeguards.

Docvoxia includes:

Human-in-the-Loop Approval
Medication Safety Validation
Explainable Recommendations
Audit Trails
Evidence Grounding
Consent Tracking
Structured Clinical Reasoning

AI recommendations are advisory and never replace clinical judgment.

🧬 FHIR & Healthcare Standards

Docvoxia generates standardized healthcare outputs using:

FHIR R4 Resources

Patient
Encounter
Observation
Condition
MedicationRequest
CarePlan

Terminology Standards

SNOMED CT

This ensures interoperability across healthcare systems.

⚙️ Technology Stack

AI & Agents

Microsoft Foundry
Foundry IQ
Azure OpenAI GPT-4o
LangGraph
Multi-Agent Architecture

Backend

Python
FastAPI
Pydantic
SQLAlchemy

Data

PostgreSQL
Redis
Azure AI Search

Infrastructure

Docker
Azure Container Apps
OpenTelemetry

📂 Project Structure

docvoxia/

├── app/
│   ├── api/
│   ├── agents/
│   │   ├── patient_intake/
│   │   ├── context_retrieval/
│   │   ├── diagnostic_reasoning/
│   │   ├── medication_safety/
│   │   ├── prescription/
│   │   ├── follow_up/
│   │   └── evidence_validation/
│   │
│   ├── orchestrator/
│   ├── knowledge/
│   ├── fhir/
│   ├── safety/
│   ├── database/
│   ├── services/
│   ├── schemas/
│   └── models/
│
├── tests/
│
├── docs/
│
├── docker/
│
├── scripts/
│
├── deployments/
│
├── specs/
│
├── docvoxia_system_architecture.png
│
└── README.md

📊 Hackathon Alignment

This project was built for:

Microsoft Agents League

Reasoning Agents Track

The solution demonstrates:

Multi-Agent Collaboration
Multi-Step Reasoning
Foundry IQ Grounding
Clinical Decision Intelligence
Human Oversight
Explainable AI
Enterprise-Ready Healthcare Integration

🔮 Future Roadmap

Real-time streaming consultations
Work IQ integration
Fabric IQ integration
Clinical Evaluation Benchmarks
Healthcare MCP Tools
Hosted Agents in Microsoft Foundry
Advanced Hospital Workflow Automation

🙌 Acknowledgements

Built using:

Microsoft Foundry
Foundry IQ
Azure OpenAI
LangGraph
FHIR R4
SNOMED CT

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