Turning Unstructured Healthcare Data into Answers with Retrieval Augmented Generation

Care teams, product managers, and operations leaders across healthcare live inside documents: contracts, benefit summaries, clinical policies, internal runbooks, and email threads. These artifacts carry critical business logic, yet they are largely unstructured and scattered across repositories. Finding a precise answer often means opening multiple PDFs, searching manually, and asking colleagues to “remember where that clause was.” It’s slow, brittle, and hard to scale.

To address this, our AI Platform team built a retrieval-augmented generation (RAG) layer that uses AI agents to reason over unstructured content at scale. Instead of treating each content type as a custom integration, we now empower customers to use a single platform that can ingest, enrich, index, and serve knowledge from contracts, product documentation, release notes, and more.

Healthcare Runs on Documents—But Finding Answers Is Often a Big Challenge

Our starting point was a simple but pervasive problem: “I know this answer exists somewhere in a document, but I can’t find it quickly.” We heard from our customers that this is one of the most common statements across all types of teams, including those responsible for contracts, pricing, implementations, and customer communication.

We wanted a solution that was:

1. Unstructured first – Worked across unstructured content without requiring any schema upfront.
2. Safe for healthcare – Could be safely used in regulated healthcare contexts.
3. Composable – Was reusable across multiple products and workflows rather than built as a one-off feature.

The first wave of use cases includes contract, policy, and product documentation question-and-answer resources for internal users.

A Healthcare-Ready AI Platform for Unstructured Content

Our new AI-powered RAG capability is designed as a platform service, not a single UI, and is part of a broader vision for modern, intelligent health plan technology. At its core, it provides a small, opinionated set of features that product teams can compose into their own experiences:

• Natural language Q&A over documents - Users pick a corpus (for example, “Client X Contracts 2025” or “Product Release Notes”) and ask natural language questions. Responses are concise, grounded, and come with citations to the underlying pages or paragraphs.
• Context-aware chat - A conversational interface keeps context across turns, allowing users to drill deeper (“Show me where you found that” or “Explain the contract terms to me”).
• Traceability and safety controls - Every answer includes citations, and audit logs are stored. This makes it easier for users to validate responses and for teams to adopt the system in workflows that require human review.

Because the platform is API-driven, feature teams can embed these capabilities in different places: internal tools, client-facing portals, or operational dashboards—all backed by the same RAG layer.

Under the Hood: How HealthEdge’s RAG Platform Works

The architecture follows a classic RAG pattern designed for multi-tenant use.

Ingestion and enrichment - Content lands in our blob storage module, either through bulk loads or product-specific pipelines. An event-driven ingestion service listens for new or updated blobs and orchestrates:

• Extracting text from documents and scanned content.
• Chunking content into overlapping segments with a fixed character length, so long documents can be searched efficiently while keeping enough local context for the model to answer questions accurately.
• Enriching with metadata (tenant, application, document name).
• Generating vector embeddings for each chunk—numerical representations of the text that capture its meaning—so we can perform semantic search, not just keyword matching.

The enriched chunks and metadata are then pushed into AI search indexes—specialized data structures optimized for search, which store both full-text and vector representations. Per-tenant isolation is handled via index boundaries and metadata filters, ensuring that each client’s content remains logically and operationally separate.

Retrieval and generation - For each user query, the retrieval service:

• Resolves the tenant and corpus to the correct index scope.
• Returns a compact set of passages with metadata and citation handles.
• Invokes an AI agent with a prompt that includes the most relevant passages, conversation history, and system instructions focused on citation, faithfulness, and tone.

All calls travel through guardrails, including content safety and prompt injection checks, and are fully instrumented with logging and observability. Because product teams integrate with the RAG platform via a stable API, we can change models, tweak prompts, or introduce new retrieval strategies behind the scenes without affecting downstream consumers.

Early Wins: Faster Answers, Safer Decisions, and Shared Infrastructure

While still early in rollout, we are already seeing tangible benefits in pilot teams:

• Time-to-answer – Routine contract questions that previously took several minutes now typically get resolved in a single query and follow-up.
• Consistent, auditable responses – Citations and logs provide a clear trail from an answer back to specific clauses.
• Reusable building block – Instead of building bespoke Q&A for each project, product teams can plug into a single RAG service with configuration for their domain.

Equally important, teams are no longer building parallel, one-off RAG implementations. They can focus on product-specific UX while the platform team centrally evolves retrieval quality, observability, and guardrails.

Turning Institutional Knowledge into Actionable Answers

Unstructured documents are where much of our institutional knowledge lives, but they have historically been hard to search, compare, and operationalize. By building a RAG capability, we’ve created a common layer that can turn those documents into actionable, explainable answers.

As we expand, we’re focusing on making answers even more grounded and consistent, strengthening evaluation of retrieval and response quality, deeper integration into existing workflows, and support for additional content types. But the core idea remains simple: meet users where they already work.

To follow HealthEdge’s AI strategy in greater detail, visit the Resources section of our website, www.healthedge.com.

Contact HealthEdge to learn how our AI solutions can streamline your provider data management operations.