> ## Documentation Index
> Fetch the complete documentation index at: https://docs.altohealth.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Extraction

> Extract structured data from unstructured medical documents

## Overview

Alto Health's data extraction automatically converts unstructured medical referrals into structured, actionable data. Our AI Assistant processes documents in any format and extracts clinical entities with high accuracy.

## Example Response

When extraction completes, Alto Health sends a webhook notification with the extracted data. Here's an example of the webhook payload:

```json theme={null}
{
  "event": "task.completed",
  "data": {
    "job_id": "62f42de2-2490-4b91-bc61-5dd1cffd6af7",
    "status": "completed",
    "record_id": "692c5c41a0e641f56952aeb9",
    "entities": {
      "entities": [
        {
          "entityName": "patientName",
          "entityValue": "Rosa Brown",
          "boundingBox": {
            "left": 0.1166,
            "top": 0.3684,
            "width": 0.4419,
            "height": 0.0171,
            "page": 1,
            "original_page": 1
          },
          "confidence": "0.987"
        },
        {
          "entityName": "patientGender",
          "entityValue": "female",
          "boundingBox": {
            "left": 0.1168,
            "top": 0.4745,
            "width": 0.7099,
            "height": 0.0177,
            "page": 1,
            "original_page": 1
          },
          "confidence": "0.870"
        },
        {
          "entityName": "referrer",
          "entityValue": "",
          "boundingBox": {},
          "confidence": "1.000"
        }
      ]
    },
    "error": null,
    "extracted_metadata": null,
    "chunks": [
      {
        "text": "Dear Dr. Montgomery",
        "grounding": [
          {
            "page": 1,
            "boundingBox": {
              "left": 0.119755,
              "top": 0.315407,
              "width": 0.178067,
              "height": 0.016624
            },
            "polygon": [
              {"x": 71.482, "y": 265.572},
              {"x": 177.502, "y": 266.443},
              {"x": 177.394, "y": 279.569},
              {"x": 71.374, "y": 278.698}
            ]
          }
        ],
        "chunk_type": "text",
        "chunk_id": "block_OpAaG8eWJW-97LHR9fPon",
        "rotation_angle": 0.0
      },
      {
        "text": "Re: Referral for Rosa Brown [Date of Birth: 30/01/1952]",
        "grounding": [
          {
            "page": 1,
            "boundingBox": {
              "left": 0.119778,
              "top": 0.346499,
              "width": 0.453744,
              "height": 0.016137
            }
          }
        ],
        "chunk_type": "text",
        "chunk_id": "block_umlvx3IvACuYOIKznNms9",
        "rotation_angle": 0.0
      }
    ],
    "extracted_markdown": "Dear Dr. Montgomery\n\nRe: Referral for Rosa Brown [Date of Birth: 30/01/1952]\n\nI am referring Rosa to your clinic for further evaluation and management.\n\n### 1. Introduction\n\nRosa is a 72 years old female who lives alone and is ECOG 2, and presented in my clinic with redness and swelling in right breast for 4 months\n\n### 2. Clinical Details\n\nRosa presented with redness and swelling in right breast since 4 months..."
  }
}
```

### Response Fields

| Field                                  | Type        | Description                                                         |
| -------------------------------------- | ----------- | ------------------------------------------------------------------- |
| `event`                                | string      | Event type - `task.completed` when extraction finishes              |
| `data.job_id`                          | string      | Unique identifier for the extraction job                            |
| `data.status`                          | string      | Job status - `completed`, `processing`, or `failed`                 |
| `data.record_id`                       | string      | Record ID from the document upload                                  |
| `data.entities`                        | object      | Extracted clinical entities                                         |
| `data.entities.entities`               | array       | Array of extracted entity objects                                   |
| `data.entities.entities[].entityName`  | string      | Name of the extracted entity (e.g., `patientName`, `patientGender`) |
| `data.entities.entities[].entityValue` | string      | Extracted value for the entity                                      |
| `data.entities.entities[].boundingBox` | object      | Location of the entity in the document (coordinates)                |
| `data.entities.entities[].confidence`  | string      | Confidence score (0-1) - higher is more confident                   |
| `data.chunks`                          | array       | Document chunks with text and location information                  |
| `data.chunks[].text`                   | string      | Extracted text from the chunk                                       |
| `data.chunks[].grounding`              | array       | Location information (page, bounding box, polygon)                  |
| `data.chunks[].chunk_type`             | string      | Type of chunk - `text`, `section_heading`, etc.                     |
| `data.extracted_markdown`              | string      | Full document content in markdown format                            |
| `data.error`                           | string/null | Error message if extraction failed, otherwise null                  |

<Note>
  **Confidence Scores:** Values closer to 1.0 indicate higher confidence. A score of 1.000 for an empty value typically means Alto Health is confident the entity is not present in the document.
</Note>

<Tip>
  **Bounding Boxes:** Each extracted entity includes coordinates showing where it was found in the document. Use these for visual verification or highlighting in your UI.
</Tip>