Hey everyone,
I work with Credible Data, where we’re building on Malloy - a modern open source language for describing data relationships and transformations. In a nutshell, Malloy is an analytical language that runs on SQL databases, and provides the ability to define a semantic data model and query it.
I thought it’d be particularly interesting to see where Malloy can help with OMOP queries, so I wrote up a short example of using Malloy to model OMOP relationships, joins, measures, and so on. The example uses one of the synthetic OMOP datasets.
I’m curious to learn from the community whether this type of reusable semantic model could be useful to accelerate querying OMOP data, prevent errors, and especially now where we may want AI agents to run such queries directly.
The blog post is here: Making Healthcare Data AI-Ready | Credible Blog
Would value any thoughts, feedback or comments.