Skip to navigation
Get started

Quickstart

Normalize your first clinical term in five minutes.
1

Install an SDK

npm install @imo-demo/precision-normalize
2

Set your credentials

Export the client credentials issued to you in the Developer Portal:

export IMO_CLIENT_ID="your-client-id"
export IMO_CLIENT_SECRET="your-client-secret"

The SDK exchanges these for a JWT automatically and refreshes it before it expires. See Authentication for the underlying flow.

3

Normalize a term

import { ImoHealthClient } from "@imo-demo/precision-normalize";
const client = new ImoHealthClient({
  clientId: process.env.IMO_CLIENT_ID!,
  clientSecret: process.env.IMO_CLIENT_SECRET!,
});
const result = await client.normalize.normalize({
  clientRequestId: "quickstart-001",
  preferences: { threshold: 0.5, size: 3 },
  requests: [
    { recordId: "1", domain: "Problem", inputTerm: "chest pain" },
    { recordId: "2", domain: "Problem", inputTerm: "type 2 diabetes" },
  ],
});
for (const record of result.requests ?? []) {
  const top = record.response?.items?.[0];
  console.log(record.recordId, "->", top?.lexicalTitle, top?.score);
}
4

Read the response

Each record echoes your record_id and carries a ranked items array. The score is a confidence value from 0–1, with 1 an exact match.

{
  "summary": { "count": 1, "error_count": 0 },
  "request_id": "7f3c1a90-5f2e-4d1b-9c8a-1b2c3d4e5f60",
  "client_request_id": "quickstart-001",
  "response_time": 42,
  "requests": [
    {
      "record_id": "1",
      "domain": "Problem",
      "input_term": "chest pain",
      "response": {
        "match_field": "input_term",
        "domain": "Problem",
        "explanation": "The input term 'chest pain' was matched to the IMO lexical 'Chest pain' with a score of 1.0.",
        "items": [
          {
            "lexical_code": "3763",
            "lexical_title": "Chest pain",
            "score": 1.0,
            "certainty": "High"
          }
        ]
      }
    }
  ]
}

Set preferences.size to cap how many candidate lexicals come back per record, and preferences.threshold to drop low-confidence matches entirely.

Where to go next

Matching preferences

Thresholds, term-vs-code priority, transformations, and filters.

Add clinical context

Improve Problem-domain matches with age, sex, and ICD-10 history.

Close the loop

Report which results your users picked.