What are the key features of VistAAI?

VistAAI includes a self-hosted Ollama container with Llama 3.2, integrates with VistA data using an MCP server, provides a web UI, and ensures AI recognition and refusal to share confidential patient information.

What is the main benefit of using a self-hosted AI like VistAAI?

A self-hosted AI provides greater control over data privacy and security, as all processing occurs within your own infrastructure.

What are the prerequisites for running VistAAI?

You need Docker and docker-compose installed on your system. Refer to the installation guide for detailed steps.

How do I access the Ollama WebUI?

Once VistAAI is running, you can access the Ollama WebUI (without VistA integration) via http://localhost:8001.

VistAAI is a tool that integrates a self-hosted Ollama AI with VistA model context, allowing you to query patient information securely and efficiently.

VistAAI

Name: VistAAI
Author: RamSailopal

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Integrates a self-hosted Ollama AI with VistA model context for querying patient information.

VistAAI provides a self-hosted AI solution that combines the power of Ollama with VistA's patient data. It leverages an MCP server to facilitate communication between the AI model and a VistA database, allowing users to ask questions about VistA patients and receive relevant information. The setup includes Docker containers for Ollama, a model loader, a VistA instance with fmQL, and the MCP server itself, providing a comprehensive environment for exploring AI-driven healthcare data analysis. A web UI is also included for direct interaction with the Ollama model without the VistA context.

Key Features

01Integrates Ollama with VistA data using an MCP server

02Includes a self-hosted Ollama container with a Llama 3.2 model

03Provides a VistA container with fmQL for data querying

04Offers a web UI for direct interaction with the Ollama model

05Demonstrates AI's ability to recognize and refuse to share confidential patient information

060 GitHub stars

Use Cases

01Querying VistA patient data using natural language

02Exploring the capabilities of AI in understanding and interacting with healthcare databases

03Developing applications that require secure and context-aware access to patient information