Iguana + AI: A Smarter Way to Build Healthcare Integrations
We're entering an era where AI isn't just a buzzword, but it’s quickly becoming part of everyday development. Tools like ChatGPT and Cursor are helping developers write code faster, generate documentation, and automate the tasks that once slowed integration projects down.
And because of its open, code-based design, Iguana is uniquely positioned to take advantage of this new world of AI-assisted development.
Why Iguana Works Well with AI
Many integration engines rely on locked-down, drag-and-drop interfaces that hide the real logic beneath layers of configuration. They might be simple to start with, but they are difficult to extend and nearly impossible for AI tools to work with.
Iguana takes a different approach.
Its Translator IDE gives developers full access to the Lua code behind every integration. You can open, modify, and work with each script directly. This level of transparency allows AI tools to truly complement your workflow by reading, generating, and refining logic within the same framework you already use.
Using AI to Streamline Development
AI tools are great at handling the repetitive parts of development:
- Creating boilerplate code
- Drafting mapping logic
- Generating documentation
- Suggesting structure based on patterns you already use
However, AI can only help effectively when it has a clear and open framework to work within, and that is exactly what Iguana provides.
By using AI tools like ChatGPT or Cursor alongside Iguana's Translator, developers can speed up setup and mapping while maintaining full control of their integrations.
Example: Building a FHIR-to-Database Mapping with AI
To illustrate this, our team created a demo showing how AI can help create a new FHIR-to-database mapping in Iguana.
In the demo, the existing mappings for Patient, Encounter, and Immunization were already built following best practices. When it was time to add a new Condition (Problem) FHIR resource, we used AI to handle the heavy lifting.
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ChatGPT (0:10) was used to review the client's FHIR specifications and produced a clear mapping blueprint that showed how each FHIR field should align with the database columns.
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Cursor (9:10) was then used, to take that blueprint and automatically create the Lua mapping and utility files inside the Iguana Translator project, following the same structure and naming conventions as the existing code.
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Iguana (12:00) then recognized the new resource right away, processed the sample FHIR data, and generated database-ready output without any manual coding.
The result was a new FHIR mapping created in minutes and fully compatible with the existing framework.
AI Helps You Build Faster, and Iguana Keeps It Running
AI can help generate, refactor, and document code. Iguana runs that code, manages the workflows, and ensures that every message moves reliably from start to finish.
They are not competing technologies, they work together.
AI speeds up development, while Iguana provides the architecture and stability that turn that development into real, working integrations.
The Future of Integration Development
In today's fast-moving environment, the best approach is to combine the adaptability of AI with the reliability of Iguana.
Developers can let AI handle the repetitive groundwork while they focus on testing, validation, and solving real integration challenges. It is faster, cleaner, and more sustainable, and it builds on the openness that has always defined how Iguana works.
Iguana remains the integration platform. AI simply helps you build with it faster.
