Turn medical procedure codes into an AI-searchable database

Converts medical procedure listings into AI embeddings so staff can search by meaning, not just keywords.

How the work actually flows

A straight line. Runs once per one per procedure entry.

Pattern: Sequence (1) ยท Multiple Instances with a priori Design-Time Knowledge (13)

flowchart TD trig[\"procedure data available to import"\]:::trigdata s0[["clean and prepare descriptions"]]:::mi s1["generate semantic embeddings"]:::svc s2[("store embeddings in database")]:::store trig --> s0 s0 --> s1 s1 --> s2 out[/"searchable procedure database built"/]:::out pay{{"staff find procedures by meaning"}}:::pay s2 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itAn outside serviceRuns once per itemA record or sheetResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Knowledge Base & RAG
Connects
Google GeminiPostgreSQL
Featured in

The problem it solves

Searching a medical procedure list by exact keyword means staff miss results that use slightly different wording for the same procedure. That forces people to guess at phrasing or scroll through long lists to find what they need.

Who it fits

Healthcare organizations or software teams that need smarter search across a medical procedure database.

How it works

  1. The system imports medical procedure entries from your source data
  2. Each description is cleaned and prepared for processing
  3. Google Gemini converts each procedure into a semantic embedding
  4. The embeddings are stored in a searchable database
What you get

Procedure records staff can find by meaning

Your medical procedure listings become searchable by meaning, so staff can find the right entry even without knowing the exact wording.

What you get

A searchable database where staff can find procedures by meaning instead of exact wording.

What you need

A PostgreSQL database with pgvector, a source of procedure data, and a Google Gemini API key.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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