Building AI-Powered Clinical Knowledge Stores with Azure AI Search
👀 Missed Session 01? Don’t worry—you can still catch up. But first, here’s what AI HLS Ignited is all about: What is AI HLS Ignited? AI HLS Ignited is a Microsoft-led technical series for healthcare innovators, solution architects, and AI engineers. Each session brings to life real-world AI solutions that are reshaping the Healthcare and Life Sciences (HLS) industry. Through live demos, architectural deep dives, and GitHub-hosted code, we equip you with the tools and knowledge to build with confidence. Session 01 Recap: In our first session, we introduced the accelerator MedIndexer - which is an indexing framework designed for the automated creation of structured knowledge bases from unstructured clinical sources. Whether you're dealing with X-rays, clinical notes, or scanned documents, MedIndexer converts these inputs into a schema-driven format optimized for Azure AI Search. This will allow your applications to leverage state-of-the-art retrieval methodologies, including vector search and re-ranking. Moreover, by applying a well-defined schema and vectorizing the data into high-dimensional representations, MedIndexer empowers AI applications to retrieve more precise and context-aware information... The result? AI systems that surface more relevant, accurate, and context-aware insights—faster. 🔍 Turning Your Unstructured Data into Value "About 80% of medical data remains unstructured and untapped after it is created (e.g., text, image, signal, etc.)" — Healthcare Informatics Research, Chungnam National University In the era of AI, the rise of AI copilots and assistants has led to a shift in how we access knowledge. But retrieving clinical data that lives in disparate formats is no trivial task. Building retrieval systems takes effort—and how you structure your knowledge store matters. It’s a cyclic, iterative, and constantly evolving process. That’s why we believe in leveraging enterprise-ready retrieval platforms like Azure AI Search—designed to power intelligent search experiences across structured and unstructured data. It serves as the foundation for building advanced retrieval systems in healthcare. However, implementing Azure AI Search alone is not enough. Mastering its capabilities and applying well-defined patterns can significantly enhance your ability to address repetitive tasks and complex retrieval scenarios. This project aims to accelerate your ability to transform raw clinical data into high-fidelity, high-value knowledge structures that can power your next-generation AI healthcare applications. 🚀 How to Get Started with MedIndexer New to Azure AI Search? Begin with our guided labs to build a strong foundation and get hands-on with the core capabilities. Already familiar with the tech? Jump ahead to the real-world use cases—learn how to build Coded Policy Knowledge Stores and X-ray Knowledge Stores. 🧪 Labs 🧪 Building Your Azure AI Search Index: 🧾 Notebook - Building your first Index Learn how to create and configure an Azure AI Search index to enable intelligent search capabilities for your applications. 🧪 Indexing Data into Azure AI Search: 🧾 Notebook - Ingest and Index Clinical Data Understand how to ingest, preprocess, and index clinical data into Azure AI Search using schema-first principles. 🧪 Retrieval Methods for Azure AI Search: 🧾 Notebook - Exploring Vector Search and Hybrid Retrieval Dive into retrieval techniques such as vector search, hybrid retrieval, and reranking to enhance the accuracy and relevance of search results. 🧪 Evaluation Methods for Azure AI Search: 🧾 Notebook - Evaluating Search Quality and Relevance Learn how to evaluate the performance of your search index using relevance metrics and ground truth datasets to ensure high-quality search results. 🏥 Use Cases 📝 Creating Coded Policy Knowledge Stores In many healthcare systems, policy documents such as pre-authorization guidelines are still trapped in static, scanned PDFs. These documents are critical—they contain ICD codes, drug name coverage, and payer-specific logic—but are rarely structured or accessible in real-time. To solve this, we built a pipeline that transforms these documents into intelligent, searchable knowledge stores. This diagram shows how pre-auth policy PDFs are ingested via blob storage, passed through an OCR and embedding skillset, and then indexed into Azure AI Search. The result: fast access to coded policy data for AI apps. 🧾 Notebook - Creating Coded Policies Knowledge Stores Transform payer policies into machine-readable formats. This use case includes: Preprocessing and cleaning PDF documents Building custom OCR skills Leveraging out-of-the-box Indexer capabilities and embedding skills Enabling real-time AI-assisted querying for ICDs, payer names, drug names, and policy logic Why it matters: This streamlines prior authorization and coding workflows for providers and payors, reducing manual effort and increasing transparency. 🩻 Creating X-ray Knowledge Stores In radiology workflows, X-ray reports and image metadata contain valuable clinical insights—but these are often underutilized. Traditionally, they’re stored as static entries in PACS systems or loosely connected databases. The goal of this use case is to turn those X-ray reports into a searchable, intelligent asset that clinicians can explore and interact with in meaningful ways. This diagram illustrates a full retrieval pipeline where radiology reports are uploaded, enriched through foundational models, embedded, and indexed. The output powers an AI-driven web app for similarity search and decision support. 🧾 Notebook - Creating X-rays Knowledge Stores Turn imaging reports and metadata into a searchable knowledge base. This includes: Leveraging push APIs with custom event-driven indexing pipeline triggered on new X-ray uploads Generating embeddings using Microsoft Healthcare foundation models Providing an AI-powered front-end for X-ray similarity search Why it matters: Supports clinical decision-making by retrieving similar past cases, aiding diagnosis and treatment planning with contextual relevance. 📣 Join Us for the Next Session Help shape the future of healthcare by sharing AI HLS Ignited with your network—and don’t miss what’s coming next! 📅 Register for the upcoming session → AI HLS Ignited Event Page 💻 Explore the code, demos, and architecture → AI HLS Ignited GitHub Repository
