> ## Documentation Index > Fetch the complete documentation index at: https://docs.corti.ai/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart & First Request > Get started with the Corti Medical Coding API Completing this quickstart will get you a working code prediction response from the Corti Medical Coding API. You can then explore the full API and use cases from there. ## Prerequisites * **A Corti Console account** — sign up at the [Corti Console](https://console.corti.app/) to get started. * **An API client** — your tenant name, `clientId`, and `clientSecret` are all found in your API client configuration. See [Creating API clients](https://docs.corti.ai/authentication/creating_clients) to set one up. POST a clinical note to the code prediction endpoint. The `system` field determines which classification is applied. Your environment (`eu` or `us`) is set in your API client — see [Creating API clients](https://docs.corti.ai/authentication/creating_clients). | Parameter | Type | Required | Description | | --------- | ---------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------- | | `system` | `string[]` | Yes | One or more coding systems. Up to 4 per request. See [Coding Systems](/coding/coding-systems) for all options and recommended pairings. | | `context` | `object[]` | Yes | The clinical input to code. Each item is a `text` string or a `documentId` referencing a document stored in Corti. | | `filter` | `object` | No | Restrict predictions to specific codes or categories. See [Code Prediction](/coding/introduction) for details. | ```bash title="JavaScript" theme={null} npm install @corti/sdk # or yarn add @corti/sdk # or pnpm add @corti/sdk ``` ```bash title="C# .NET" theme={null} dotnet add package Corti.Sdk # Alternatively, in your .csproj: # ``` ```ts title="JavaScript" theme={null} import { CortiClient } from "@corti/sdk"; // Replace these with your values const CLIENT_ID = ""; const CLIENT_SECRET = ""; const ENVIRONMENT = ""; const TENANT = ""; const client = new CortiClient({ environment: ENVIRONMENT, tenantName: TENANT, auth: { clientId: CLIENT_ID, clientSecret: CLIENT_SECRET, }, }); ``` ```csharp title="C# .NET" theme={null} using Corti; // Replace these with your values const string CLIENT_ID = ""; const string CLIENT_SECRET = ""; const string ENVIRONMENT = ""; const string TENANT = ""; var client = new CortiClient( tenantName: TENANT, environment: ENVIRONMENT, auth: CortiClientAuth.ClientCredentials( clientId: CLIENT_ID, clientSecret: CLIENT_SECRET) ); ``` ```ts title="JavaScript" theme={null} const response = await client.codes.predict({ system: ["icd10cm-inpatient"], context: [ { type: "text", text: "Discharge Summary: 82-year-old male admitted with acute STEMI, treated with primary PCI to LAD with drug-eluting stent placement. Hospital course complicated by cardiogenic shock requiring vasopressors for 48 hours. Also managed acute on chronic systolic heart failure (EF 25%), type 2 diabetes with peripheral neuropathy, and stage 3 CKD. Discharged to skilled nursing facility on dual antiplatelet therapy, beta blocker, ACE inhibitor, and insulin.", }, ], }); ``` ```csharp title="C# .NET" theme={null} var response = await client.Codes.PredictAsync(new CodesGeneralPredictRequest { System = [CommonCodingSystemEnum.Icd10CmInpatient], Context = [ new CommonTextContext { Type = CommonTextContextType.Text, Text = "Discharge Summary: 82-year-old male admitted with acute STEMI, treated with primary PCI to LAD with drug-eluting stent placement. Hospital course complicated by cardiogenic shock requiring vasopressors for 48 hours. Also managed acute on chronic systolic heart failure (EF 25%), type 2 diabetes with peripheral neuropathy, and stage 3 CKD. Discharged to skilled nursing facility on dual antiplatelet therapy, beta blocker, ACE inhibitor, and insulin.", }, ], }); ``` ```python title="Python" theme={null} import requests # Replace these with your values ENVIRONMENT = "" TENANT = "" TOKEN = "" response = requests.post( f"https://api.{ENVIRONMENT}.corti.app/v2/tools/coding/", headers={ "Authorization": f"Bearer {TOKEN}", "Tenant-Name": TENANT, "Content-Type": "application/json", }, json={ "system": ["icd10cm-inpatient"], "context": [ { "type": "text", "text": "Discharge Summary: 82-year-old male admitted with acute STEMI, treated with primary PCI to LAD with drug-eluting stent placement. Hospital course complicated by cardiogenic shock requiring vasopressors for 48 hours. Also managed acute on chronic systolic heart failure (EF 25%), type 2 diabetes with peripheral neuropathy, and stage 3 CKD. Discharged to skilled nursing facility on dual antiplatelet therapy, beta blocker, ACE inhibitor, and insulin.", } ], }, ) response.raise_for_status() result = response.json() ``` ```bash title="cURL" theme={null} # Replace these with your values ENVIRONMENT="" TENANT="" TOKEN="" curl -X POST "https://api.${ENVIRONMENT}.corti.app/v2/tools/coding/" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Tenant-Name: ${TENANT}" \ -H "Content-Type: application/json" \ -d '{ "system": ["icd10cm-inpatient"], "context": [ { "type": "text", "text": "Discharge Summary: 82-year-old male admitted with acute STEMI, treated with primary PCI to LAD with drug-eluting stent placement. Hospital course complicated by cardiogenic shock requiring vasopressors for 48 hours. Also managed acute on chronic systolic heart failure (EF 25%), type 2 diabetes with peripheral neuropathy, and stage 3 CKD. Discharged to skilled nursing facility on dual antiplatelet therapy, beta blocker, ACE inhibitor, and insulin." } ] }' ``` The SDK handles OAuth 2.0 token acquisition and refresh automatically using your `clientId` and `clientSecret` — no need to manage bearer tokens manually. A successful response returns three top-level fields: * **`codes`** — the model's high-confidence predictions. Each entry includes the `code`, a human-readable `display`, `evidences` (the exact passages from the note that support the prediction, with `contextIndex`, `text`, and `start`/`end` character offsets), and `alternatives` (other codes the model considered for the same finding). * **`candidates`** — lower-confidence codes worth reviewing. Intended for coder review rather than automatic assignment. * **`usageInfo`** — credits consumed for this request. ```json theme={null} { "codes": [ { "system": "icd10cm-outpatient", "code": "E11649", "display": "Type 2 diabetes mellitus with hypoglycemia without coma", "evidences": [ { "contextIndex": 0, "text": "occasional mild hypoglycemia", "start": 90, "end": 118 } ], "alternatives": [ { "code": "E1165", "display": "Type 2 diabetes mellitus with hyperglycemia" } ] }, { "system": "icd10cm-outpatient", "code": "M1711", "display": "Primary osteoarthritis, right knee", "evidences": [ { "contextIndex": 0, "text": "bilateral knee pain consistent with osteoarthritis", "start": 174, "end": 224 } ] } ], "candidates": [ { "system": "icd10cm-outpatient", "code": "Z794", "display": "Long-term (current) use of insulin" } ], "usageInfo": { "creditsConsumed": 1.5 } } ``` Most US encounters require more than one coding system. Pass multiple values in `system` — for example, pairing ICD-10-CM with CPT for an outpatient visit. The response includes codes from all requested systems in the same `codes` array, each tagged with its `system` value. ```ts title="JavaScript" theme={null} const response = await client.codes.predict({ system: ["icd10cm-outpatient", "cpt"], context: [ { type: "text", text: "Subjective: 58-year-old male presents for routine diabetes management. HbA1c is 7.2%. Reports occasional mild hypoglycemia. Currently on metformin 1000mg twice daily. Also notes bilateral knee pain worsening over past 3 months.\n\nAssessment and Plan: 1. Type 2 diabetes mellitus — well controlled on current regimen, continue metformin, recheck HbA1c in 3 months. 2. Bilateral knee osteoarthritis — refer to orthopedics, start topical diclofenac. 3. Hypoglycemia episodes — counseled on timing of meals relative to medication.", }, ], }); ``` ```csharp title="C# .NET" theme={null} var response = await client.Codes.PredictAsync(new CodesGeneralPredictRequest { System = [CommonCodingSystemEnum.Icd10CmOutpatient, CommonCodingSystemEnum.Cpt], Context = [ new CommonTextContext { Type = CommonTextContextType.Text, Text = "Subjective: 58-year-old male presents for routine diabetes management. HbA1c is 7.2%. Reports occasional mild hypoglycemia. Currently on metformin 1000mg twice daily. Also notes bilateral knee pain worsening over past 3 months.\n\nAssessment and Plan: 1. Type 2 diabetes mellitus — well controlled on current regimen, continue metformin, recheck HbA1c in 3 months. 2. Bilateral knee osteoarthritis — refer to orthopedics, start topical diclofenac. 3. Hypoglycemia episodes — counseled on timing of meals relative to medication.", }, ], }); ``` ```python title="Python" theme={null} import requests # Replace these with your values ENVIRONMENT = "" TENANT = "" TOKEN = "" response = requests.post( f"https://api.{ENVIRONMENT}.corti.app/v2/tools/coding/", headers={ "Authorization": f"Bearer {TOKEN}", "Tenant-Name": TENANT, "Content-Type": "application/json", }, json={ "system": ["icd10cm-outpatient", "cpt"], "context": [ { "type": "text", "text": "Subjective: 58-year-old male presents for routine diabetes management. HbA1c is 7.2%. Reports occasional mild hypoglycemia. Currently on metformin 1000mg twice daily. Also notes bilateral knee pain worsening over past 3 months.\n\nAssessment and Plan: 1. Type 2 diabetes mellitus — well controlled on current regimen, continue metformin, recheck HbA1c in 3 months. 2. Bilateral knee osteoarthritis — refer to orthopedics, start topical diclofenac. 3. Hypoglycemia episodes — counseled on timing of meals relative to medication.", } ], }, ) response.raise_for_status() result = response.json() ``` ```bash title="cURL" theme={null} # Replace these with your values ENVIRONMENT="" TENANT="" TOKEN="" curl -X POST "https://api.${ENVIRONMENT}.corti.app/v2/tools/coding/" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Tenant-Name: ${TENANT}" \ -H "Content-Type: application/json" \ -d '{ "system": ["icd10cm-outpatient", "cpt"], "context": [ { "type": "text", "text": "Subjective: 58-year-old male presents for routine diabetes management. HbA1c is 7.2%. Reports occasional mild hypoglycemia. Currently on metformin 1000mg twice daily. Also notes bilateral knee pain worsening over past 3 months.\n\nAssessment and Plan: 1. Type 2 diabetes mellitus — well controlled on current regimen, continue metformin, recheck HbA1c in 3 months. 2. Bilateral knee osteoarthritis — refer to orthopedics, start topical diclofenac. 3. Hypoglycemia episodes — counseled on timing of meals relative to medication." } ] }' ``` See [Coding Systems](/coding/coding-systems) for recommended pairings by encounter type. ## Next steps Learn about the endpoint, supported features, and coding system options. Understand the request schema, response fields, evidence spans, and filtering options. Encounter coding, CDI review, HCC capture, and revenue cycle workflows. Please [contact us](https://help.corti.app) if you need help getting started or run into issues with your first request.