Runwork
Langchain.vectorStorePGVector Langchain.textSplitterTokenSplitter Langchain.embeddingsGoogleGemini StickyNote ManualTrigger Code +2 more

Vectorize Medical Procedures for Semantic Search with TUSS, Gemini & pgVector

Transform traditional medical procedure lists into a searchable intelligence layer by generating semantic vector embeddings with Google Gemini. By automating the pipeline from TUSS data ingestion to pgvector storage, this workflow empowers healthcare platforms to deliver highly accurate, meaning-based search results. It bridges the gap between raw clinical terminology and intuitive data retrieval.

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What This Recipe Does

The Vectorize TUSS Table automation transforms static healthcare terminology data into an intelligent, AI-ready knowledge base. In the complex world of Brazilian healthcare billing, finding the correct TUSS (Terminologia Unificada da Saúde Suplementar) codes is often a manual and error-prone process. This automation processes large medical tables, breaking them down into manageable segments and preparing them for semantic search. By converting standard medical descriptions into vectorized data, your organization can move beyond simple keyword matching. This allows for the creation of intelligent tools that understand the context of medical procedures, significantly reducing administrative friction. The result is a more efficient billing cycle, fewer rejected insurance claims, and a powerful foundation for building AI-driven clinical or administrative assistants. This workflow ensures that your healthcare data is not just stored, but is actively working to improve operational accuracy and speed.

What You'll Get

Complete App

Forms, dashboards, and UI components ready to use

Automated Workflows

Background automations that run on your schedule

API Endpoints

REST APIs for external integrations

Connected Integrations

Langchain.vectorStorePGVector, Langchain.textSplitterTokenSplitter, Langchain.embeddingsGoogleGemini, StickyNote, ManualTrigger configured and ready

How It Works

  1. 1

    Click "Start Building" and connect your accounts

    Runwork will guide you through connecting Langchain.vectorStorePGVector and Langchain.textSplitterTokenSplitter

  2. 2

    Describe any customizations you need

    The AI will adapt the recipe to your specific requirements

  3. 3

    Preview, test, and deploy

    Your app is ready to use in minutes, not weeks

Who Uses This

Frequently Asked Questions

How does this improve the accuracy of medical billing?

By vectorizing the TUSS table, the system can perform semantic searches. This means it finds the correct codes based on the meaning of the text, even if the doctor's notes use different wording than the official terminology.

Can I update the database when new TUSS versions are released?

Yes. You can run this automation whenever the regulatory body updates the TUSS table to ensure your AI tools and billing systems are always using the most current standards.

Does this work with other medical terminologies like ICD-10?

While designed for TUSS, the underlying logic is compatible with any structured medical table. You can easily adapt the workflow to vectorize ICD-10, CPT, or internal hospital procedure catalogs.

What is the benefit of splitting the data into batches?

Processing data in batches ensures that large medical tables are handled reliably without hitting system limits, providing a stable way to migrate thousands of rows into an AI-ready format.

Importing from n8n?

This recipe uses nodes like Langchain.vectorStorePGVector, Langchain.textSplitterTokenSplitter, Langchain.embeddingsGoogleGemini, StickyNote and 4 more. With Runwork, you don't need to learn n8n's workflow syntax—just describe what you want in plain English.

Langchain.vectorStorePGVector Langchain.textSplitterTokenSplitter Langchain.embeddingsGoogleGemini StickyNote ManualTrigger Code SplitInBatches Langchain.documentDefaultDataLoader

Based on n8n community workflow. View original

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Langchain.vectorStorePGVector Langchain.textSplitterTokenSplitter Langchain.embeddingsGoogleGemini StickyNote +4

Vectorize Medical Procedures for Semantic Search with TUSS, Gemini & pgVector

The Vectorize TUSS Table automation transforms standard medical billing data into an AI-ready knowledge base. In the healthcare industry, navigating the Unified Supplementary Health Communications (TUSS) table is often a manual and error-prone process. This workflow automates the conversion of complex medical procedure descriptions into high-dimensional vectors, enabling semantic search capabilities within your healthcare applications. By processing these records in controlled batches, the system ensures data integrity and high performance even with large datasets. This automation eliminates the need for exact keyword matching, allowing staff to find the correct billing codes using natural language descriptions. The primary outcome is a significant reduction in billing errors and a faster administrative cycle for hospitals, clinics, and insurance providers. Implementing this solution allows your organization to leverage modern AI tools to interpret medical data more effectively, ensuring compliance with regulatory standards while improving operational efficiency across your entire billing department.

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