Apps on Azure Blog

5 MIN READ

AI Transcription & Text Analytics for Health

hannahabbott

Microsoft

Jan 15, 2026

Driving Innovation in Healthcare with Azure AI: Real-Time Transcription and Clinical Insights

Industry Challenge

Healthcare organizations depend on qualitative research, patient interviews, and clinical documentation to improve care delivery. Traditional transcription services often create bottlenecks:

Manual Processes: Require manual uploads and lack automation.
Delayed Turnaround: Transcripts can take days, slowing research and decision-making.
Limited Integration: Minimal interoperability with EMR systems or analytics platforms.
Cost Inefficiencies: Pricing models that scale poorly for large volumes.

The need for real-time, HIPAA-compliant transcription and integrated analytics has never been greater.

Azure AI Solution Overview

Azure provides a comprehensive, cloud-native transcription and analytics pipeline that addresses these challenges head-on. By leveraging Azure AI Services, organizations can:

Transcribe audio/video recordings in real time.
Process PDFs and text documents for structured data extraction.
Apply Text Analytics for Health to identify medical entities and structure data into FHIR format.
Generate summaries and insights using cutting edge LLMs including Azure OpenAI.

This approach accelerates workflows, improves compliance, and reduces costs compared to traditional transcription vendors.

Azure Speech Service Options

Azure Speech Service offers multiple transcription modes to fit different scenarios:

Real-Time Transcription: Converts live audio streams into text instantly for telehealth sessions and interviews.
Batch Transcription: Processes large volumes of pre-recorded audio asynchronously for research studies.
Fast Transcription: Optimized for quick turnaround on short recordings for rapid documentation needs.

Azure Text Analytics for Health

One of the most powerful components of this solution is Azure AI Language – Text Analytics for Health, which transforms raw text into structured clinical insights. Key capabilities include:

Named Entity Recognition (NER):

Automatically identifies clinical entities such as symptoms, diagnoses, medications, procedures, and anatomy from transcripts and documents.

Relation Extraction:

Detects relationships between entities (e.g., linking a medication to its dosage or a condition to its treatment), enabling richer context for clinical decision-making.

Entity Linking to UMLS Codes:

Maps recognized entities to Unified Medical Language System (UMLS) concepts, ensuring interoperability and standardization across healthcare systems.

Assertion Detection:

Determines the status of an entity (e.g., present, absent, conditional, or hypothetical), which is critical for accurate interpretation of patient data.

These features allow healthcare organizations to move beyond simple transcription and unlock structured, actionable insights that can feed downstream analytics and reporting.

Other Azure Resources

Azure AI Document Intelligence – Extracts structured data from PDFs and scanned documents.
Azure OpenAI Service – Summarizes transcripts and generates clinical insights.
Azure Storage & Functions – Securely stores raw and processed data; orchestrates workflows for transcription and analytics.

Integration with Microsoft Fabric OneLake

Once FHIR JSON output is generated from Text Analytics for Health, it can be stored in Microsoft Fabric OneLake. This unlocks powerful downstream capabilities:

Unified Data Lake: Centralized storage for structured healthcare data.
Analytics & Reporting: Use Fabric’s Lakehouse and Power BI to build dashboards for clinical research trends, patient outcomes, and operational metrics.
AI-Driven Insights: Combine transcription data with other datasets for predictive modeling and advanced analytics.

This integration ensures that transcription and clinical insights are not siloed—they become part of a broader data ecosystem for research and decision-making.

Why Azure Stands Out

Compared to other transcription solutions in the market, Azure offers:

Real-Time Processing: Immediate access to transcripts versus multi-day turnaround.
Integrated Analytics: Built-in medical entity recognition and AI summarization.
Compliance & Security: HIPAA-ready architecture with enterprise-grade governance.
Cost Efficiency: Pay-as-you-go pricing with elastic scaling for large datasets.
End-to-End Data Flow: From transcription to Fabric OneLake for analytics.

Step-by-Step Deployment Guide

As part of the Azure Field team working in the Healthcare and Life Sciences industry, this challenge has emerged as a common theme among organizations seeking to modernize transcription and analytics workflows. To assist organizations exploring Azure AI solutions to address these challenges, the following demo application was developed by Solution Engineer Samuel Tauil and Cloud & AI Platform Specialist Hannah Abbott. This application is intended to allow organizations to quickly stand up and test these Azure services for their needs and is not intended as a production-ready solution.

This Azure-powered web application demonstrates how organizations can modernize transcription and clinical insights using cloud-native AI services. Users can upload audio files in multiple formats, which are stored in Azure Storage and trigger an Azure Function to perform speech-to-text transcription with speaker diarization. The transcript is then enriched through Azure Text Analytics for Health, applying advanced capabilities like named entity recognition, relation extraction, UMLS-based entity linking, and assertion detection to deliver structured clinical insights. Finally, Azure OpenAI generates a concise summary and a downloadable clinical report, while FHIR-compliant JSON output seamlessly integrates with Microsoft Fabric OneLake for downstream analytics and reporting—unlocking a complete, scalable, and secure solution for healthcare data workflows.

The following video clip uses AI-generated dialog for a fictitious scenario to demonstrate the capabilities of the sample application.

Sample application developed by Samuel Tauil Microsoft Solution Engineer (25) Samuel Tauil | LinkedIn

Prerequisites

Azure Subscription
GitHub account
Azure CLI installed locally (optional, for manual deployment)

1. Fork the Repository

GitHub - samueltauil/transcription-services-demo: Azure Healthcare Transcription Services Demo - Speech-to-text with Text Analytics for Health for HIPAA-compliant medical transcription

2. Create Azure Service Principal for GitHub Actions

Copy the JSON output.

3. Add GitHub Secrets (Settings → Secrets and variables → Actions):

AZURE_CREDENTIALS: Paste the service principal JSON from step 2

4. Run the deployment workflow:

Go to Actions tab → "0. Deploy All (Complete)"
Click "Run workflow"
Enter your resource group name and Azure region
Click "Run workflow"

5. After infrastructure deploys, add these additional secrets:

AZURE_FUNCTIONAPP_NAME: The function app name (shown in workflow output)
AZURE_STATIC_WEB_APPS_API_TOKEN: Get from Azure Portal → Static Web App → Manage deployment token