Document data extraction

The document data extractor allows Unstructured to extract the data from your source documents into a format that you define, in addition to having Unstructured extract the data in a format that uses Unstructured’s default document elements and metadata.

To show how the document data extractor works, take a look at the following sample sales invoice PDF. This file is one of the sample files that is available directly from the workflow designer in the Unstructured use interface (UI). The file’s content is as follows:

If you run a workflow that references this file, by default Unstructured extracts the invoice’s data in a format similar to the following. This format is based on Unstructured’s default document elements and metdata (note that the ellipses in this output indicate omitted fields for brevity):

[
  {
    "type": "Title",
    "element_id": "f2f0f022-ea3c-48a9-baa9-53fdc4f0a327",
    "text": "INVOICE",
    "metadata": {
      "filetype": "application/pdf",
      "languages": [
        "eng"
      ],
      "page_number": 1,
      "filename": "invoice.pdf",
      "data_source": {}
    }
  },
  {
    "type": "Table",
    "element_id": "42725d08-2909-4397-8ae0-63e1ee76c89b",
    "text": "INVOICE NO: INVOICE DATE: PAYMENT DUE: BILL TO: 658 12 MAY 2024 12 JUNE 2024 BRIGHTWAVE LLC, 284 MARKET STREET, SAN FRANCISCO, CA 94111",
    "metadata": {
      "text_as_html": "<table><thead><tr><th>INVOICE NO:</th><th>INVOICE DATE:</th><th>PAYMENT DUE:</th><th>BILL TO:</th></tr></thead><tbody><tr><td>658</td><td>12 MAY 2024</td><td>12 JUNE 2024</td><td>BRIGHTWAVE LLC, 284 MARKET STREET, SAN FRANCISCO, CA 94</td></tr></tbody></table>",
      "filetype": "application/pdf",
      "languages": [
        "eng"
      ],
      "page_number": 1,
      "...": "..."
    }
  },
  {
    "type": "Table",
    "element_id": "3a40bded-a85a-4393-826e-9a679b85a8f7",
    "text": "ITEM QUANTITY PRICE TOTAL Office Desk (Oak wood, 140x70 cm) 2 $249 $498 Ergonomic Chair (Adjustable height & lumbar support) 3 $189 $567 Whiteboard Set (Magnetic, 90x60 cm + 4 markers) 2 $59 $118 SUBTOTAL $1,183 VAT (19%) $224.77 TOTAL $1,407.77",
    "metadata": {
      "text_as_html": "<table><thead><tr><th>ITEM</th><th>QUANTITY</th><th>PRICE</th><th>TOTAL</th></tr></thead><tbody><tr><td>Office Desk (Oak wood, 140x70 cm)</td><td></td><td>$249</td><td>$498</td></tr><tr><td>Ergonomic Chair (Adjustable height &amp; lumbar support)</td><td></td><td>$189</td><td>$567</td></tr><tr><td rowspan=\"4\">Whiteboard Set (Magnetic, 90x60 cm + 4 markers)</td><td></td><td>$59</td><td>$118</td></tr><tr><td></td><td>SUBTOTAL</td><td>$1,183</td></tr><tr><td></td><td>VAT (19%)</td><td>$224.77</td></tr><tr><td></td><td>TOTAL</td><td>$1,407.77</td></tr></tbody></table>",
      "filetype": "application/pdf",
      "languages": [
        "eng"
      ],
      "page_number": 1,
      "...": "..."
    }
  }
]

In the preceding output, the text fields for the Table elements contain the raw text of the table, and the text_as_html field contains corresponding HTML representations of the table. However, you might also want the table’s information output as an invoice field with, among other details, each of the invoice’s line items having a description, quantity, price, and total field. However, neither the default Unstructured text nor table_as_html fields present the tables in this way by default.

By using the document data extractor in your Unstructured workflows, you could have Unstructured extract the invoice’s data in a format similar to the following (ellipses indicate omitted fields for brevity):

[
  {
    "type": "DocumentData",
    "element_id": "4321ede0-d6c8-4857-817b-bb53bd37b743",
    "text": "",
    "metadata": {
      "...": "...",
      "extracted_data": {
        "invoice": {
          "invoice_no": "658",
          "invoice_date": "12 MAY 2024",
          "payment_due": "12 JUNE 2024",
          "bill_to": "BRIGHTWAVE LLC, 284 MARKET STREET, SAN FRANCISCO, CA 94",
          "payment_information": {
            "account_name": "OFFICEPRO SUPPLIES INC.",
            "bank_name": "CHASE BANK",
            "account_no": "123456789"
          },
          "terms_conditions": "Payment is due within 30 days of the invoice date. Late payments may incur a 1.5% monthly finance charge, and re- turned checks are subject to a $25 fee.",
          "notes": "Thank you for choosing OfficePro Supplies! For any billing inquiries, please email billing@office- prosupplies.com or call [phone].",
          "items": [
            {
              "description": "Office Desk (Oak wood, 140x70 cm)",
              "quantity": 2,
              "price": 249,
              "total": 498
            },
            {
              "description": "Ergonomic Chair (Adjustable height & lumbar support)",
              "quantity": 3,
              "price": 189,
              "total": 567
            },
            {
              "description": "Whiteboard Set (Magnetic, 90x60 cm + 4 markers)",
              "quantity": 2,
              "price": 59,
              "total": 118
            }
          ],
          "subtotal": 1183,
          "vat": 224.77,
          "total": 1407.77
        }
      }
    }
  },
  {
    "type": "Title",
    "element_id": "f2f0f022-ea3c-48a9-baa9-53fdc4f0a327",
    "text": "INVOICE",
    "metadata": {
      "filetype": "application/pdf",
      "languages": [
        "eng"
      ],
      "page_number": 1,
      "filename": "invoice.pdf",
      "data_source": {}
    }
  },
  {
    "...": "..."
  }
]

In the preceding output, the first document element, of type DocumentData, has an extracted_data field within metadata that contains a representation of the document’s data in the format that you specify. Beginning with the second document element and continuing until the end of the document, Unstructured also outputs the document’s data as a series of Unstructured’s default document elements and metadata as it normally would.

To use the document data extractor, in addition to your source documents you must provide an extraction guidance prompt and an extraction schema.

An extraction guidance prompt is like a prompt that you would give to a RAG chatbot. This prompt guides Unstructured on how to extract the data from the source documents. For this invoice example, the prompt might look like the following:

Extract the invoice data into the provided JSON schema.
Be precise and copy values exactly as written (e.g., dates, amounts, account numbers).
For line items, include each product or service with its description, quantity, unit price, and total.
Do not infer or omit fields—if a field is missing, leave it blank.
Ensure numeric fields use numbers only (no currency symbols).

An extraction schema is a JSON-formatted schema that defines the structure of the data that Unstructured extracts. The schema must conform to the OpenAI Structured Outputs guidelines, which are a subset of the JSON Schema language.

For this invoice example, the schema might look like the following. Notice in this schema the following components:

• The top-level invoice object contains nested strings, arrays, and objects such as invoice_no, invoice_date, payment_due, bill_to, payment_information, terms_conditions, notes, items, subtotal, vat, and total.
• The nested payment_information object contains nested strings such as account_name, bank_name, and account_no.
• The nested items array contains a series of strings, integers, and numbers such as description, quantity, price, and total.

Here is the schema:

{
  "type": "object",
  "properties": {
    "invoice": {
      "type": "object",
      "properties": {
        "invoice_no": {
          "type": "string",
          "description": "Unique invoice number assigned to this bill"
        },
        "invoice_date": {
          "type": "string",
          "description": "Date the invoice was issued"
        },
        "payment_due": {
          "type": "string",
          "description": "Payment due date for the invoice"
        },
        "bill_to": {
          "type": "string",
          "description": "The name and address of the customer being billed"
        },
        "payment_information": {
          "type": "object",
          "properties": {
            "account_name": {
              "type": "string",
              "description": "The account holder's name receiving payment"
            },
            "bank_name": {
              "type": "string",
              "description": "Bank where payment should be sent"
            },
            "account_no": {
              "type": "string",
              "description": "Recipient bank account number"
            }
          },
          "required": ["account_name", "bank_name", "account_no"],
          "additionalProperties": false
        },
        "terms_conditions": {
          "type": "string",
          "description": "Terms and conditions of the invoice, including penalties for late payment"
        },
        "notes": {
          "type": "string",
          "description": "Additional notes provided by the issuer"
        },
        "items": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "description": {
                "type": "string",
                "description": "Description of the item or service"
              },
              "quantity": {
                "type": "integer",
                "description": "Quantity of the item purchased"
              },
              "price": {
                "type": "number",
                "description": "Price per unit of the item"
              },
              "total": {
                "type": "number",
                "description": "Total cost for the line item (quantity * price)"
              }
            },
            "required": ["description", "quantity", "price", "total"],
            "additionalProperties": false
          }
        },
        "subtotal": {
          "type": "number",
          "description": "Subtotal before taxes"
        },
        "vat": {
          "type": "number",
          "description": "Value-added tax amount"
        },
        "total": {
          "type": "number",
          "description": "Final total including taxes"
        }
      },
      "required": [
        "invoice_no",
        "invoice_date",
        "payment_due",
        "bill_to",
        "payment_information",
        "items",
        "subtotal",
        "vat",
        "total"
      ],
      "additionalProperties": false
    }
  },
  "required": ["invoice"],
  "additionalProperties": false
}

To generate a starter extraction guidance prompt and extraction schema, you could for example send a prompt such as the following, along with a representative sample of your source documents, to a RAG chatbot such as ChatGPT, Claude, Google Gemini, or Perplexity AI:

Please create a schema I can use to leverage an LLM for structured data extraction from the file I have just given you. 
It should adhere to OpenAI's JSON mode format. Here is an example of one I have used before for a different project:

{
  "type": "object",
  "properties": {
    "plants": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "The name of the plant"
          },
          "sunlight": {
            "type": "string",
            "description": "The sunlight requirements for the plant (e.g., 'Direct', 'Bright Indirect - Some direct')"
          },
          "water": {
            "type": "string",
            "description": "The watering instructions for the plant (e.g., 'Let dry between thorough watering', 'Water when 50-60% dry')"
          },
          "humidity": {
            "type": "string",
            "description": "The humidity requirements for the plant (e.g., 'Low', 'Medium', 'High')"
          }
        },
        "required": ["name", "sunlight", "water", "humidity"],
        "additionalProperties": false
      }
    }
  },
  "required": ["plants"],
  "additionalProperties": false
}

In addition, please provide a guidance prompt that will help ensure the most accurate extraction possible.

Using the document data extractor

1. Add a Document Data Extractor node to your existing Unstructured workflow. This node must be added immediately after the Partitioner node in the workflow. To add this node, in the workflow designer, click the + (add node) button, click Transform, and then click Document Data Extractor.

2. Click the newly added Document Data Extractor node to select it.

3. In the node’s settings pane, on the Details tab, specify the following:

   a. For Extraction Guidance Prompt, enter the text of your extraction guidance prompt.
   b. Click Edit Code, enter the text of your extraction schema, and then click Save Changes. The text you entered will appear in the Schema box.
   

4. Continue building your workflow as desired.

5. To see the results of the document data extractor, do one of the following:

   • If you are using a local file as input to your workflow, click Test immediately above the Source node. The results will be displayed on-screen in the Test output pane.
   • If you are using source and destination connectors for your workflow, run the workflow, monitor the workflow’s job, and then examine the results in your destination location.

Limitations

The document data extractor does not work with the Pinecone destination connector. This is because Pinecone has strict limit on the amount of metadata that it can manage. These limits are below the threshold of what the document data extractor typically needs for the amount of metadata that it manages.

Saving the extracted data separately

There might be cases where you want to save the contents of the extracted_data field separately from the rest of Unstructured’s JSON output. To do this, you could use a Python script such as the following. This script works with one or more Unstructured JSON output files that you already have stored on the same machine as this script. Before you run this script, do the following:

• To process all Unstructured JSON files within a directory, change None for input_dir to a string that contains the path to the directory. This can be a relative or absolute path.

• To process specific Unstructured JSON files within a directory or across multiple directories, change None for input_file to a string that contains a comma-separated list of filepaths on your local machine, for example "./input/2507.13305v1.pdf.json,./input2/table-multi-row-column-cells.pdf.json". These filepaths can be relative or absolute.

  If input_dir and input_file are both set to something other than None, then the input_dir setting takes precedence, and the input_file setting is ignored.

• For the output_dir parameter, specify a string that contains the path to the directory on your local machine that you want to send the extracted_data JSON. If the specified directory does not exist at that location, the code will create the missing directory for you. This path can be relative or absolute.

import asyncio
import os
import json

async def process_file_and_save_result(input_filename, output_dir):
    with open(input_filename, "r") as f:
        input_data = json.load(f)

    if input_data[0].get("type") == "DocumentData":
        if "extracted_data" in input_data[0]["metadata"]:
            extracted_data = input_data[0]["metadata"]["extracted_data"]

            results_name = f"{os.path.basename(input_filename)}"
            output_filename = os.path.join(output_dir, results_name)

            try:
                with open(output_filename, "w") as f:
                    json.dump(extracted_data, f)
                print(f"Successfully wrote 'metadata.extracted_data' to '{output_filename}'.")
            except Exception as e:
                print(f"Error: Failed to write 'metadata.extracted_data' to '{output_filename}'.")
        else:
            print(f"Error: Cannot find 'metadata.extracted_data' field in '{input_filename}'.")
    else: 
        print(f"Error: The first element in '{input_filename}' does not have 'type' set to 'DocumentData'.")
        

def load_filenames_in_directory(input_dir):
    filenames = []
    for root, _, files in os.walk(input_dir):
        for file in files:
            if file.endswith('.json'):
                filenames.append(os.path.join(root, file))
                print(f"Found JSON file '{file}'.")
            else:
                print(f"Error: '{file}' is not a JSON file.")

    return filenames

async def process_files():
    # Initialize with either a directory name, to process everything in the dir,
    # or a comma-separated list of filepaths.
    input_dir   = None # "path/to/input/directory"
    input_files = None # "path/to/file,path/to/file,path/to/file"

    # Set to the directory for output json files. This dir 
    # will be created if needed.
    output_dir = "./extracted_data/"

    if input_dir:
        filenames = load_filenames_in_directory(input_dir)
    else:
        filenames = input_files.split(",")

    os.makedirs(output_dir, exist_ok=True)

    tasks = []
    for filename in filenames:
        tasks.append(
            process_file_and_save_result(filename, output_dir)
        )

    await asyncio.gather(*tasks)

if __name__ == "__main__":
    asyncio.run(process_files())

Additional examples

In addition to the preceding invoice example, here are some more examples that you can adapt for your own use.

Caring for houseplants

Using the following image file:

An extraction schema for this file might look like the following:

{
  "type": "object",
  "properties": {
    "plants": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "The name of the plant"
          },
          "sunlight": {
            "type": "string",
            "description": "The sunlight requirements for the plant (e.g., 'Direct', 'Bright Indirect - Some direct')"
          },
          "water": {
            "type": "string",
            "description": "The watering instructions for the plant (e.g., 'Let dry between thorough watering', 'Water when 50-60% dry')"
          },
          "humidity": {
            "type": "string",
            "description": "The humidity requirements for the plant (e.g., 'Low', 'Medium', 'High')"
          }
        },
        "required": ["name", "sunlight", "water", "humidity"],
        "additionalProperties": false
      }
    }
  },
  "required": ["plants"],
  "additionalProperties": false
}

An extraction guidance prompt for this file might look like the following:

Extract the plant information for each of the plants in this document.

And Unstructured’s output would look like the following:

[
  {
    "type": "DocumentData",
    "element_id": "3be179f1-e1e5-4dde-a66b-9c370b6d23e8",
    "text": "",
    "metadata": {
      "...": "...",
      "extracted_data": {
        "plants": [
          {
            "name": "Krimson Queen",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low"
          },
          {
            "name": "Chinese Money Plant",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low - Medium"
          },
          {
            "name": "String of Hearts",
            "sunlight": "Direct - Bright Indirect",
            "water": "Let dry between thorough watering",
            "humidity": "Low"
          },
          {
            "name": "Marble Queen",
            "sunlight": "Low- High Indirect",
            "water": "Water when 50 - 80% dry",
            "humidity": "Low - Medium"
          },
          {
            "name": "Sansevieria Whitney",
            "sunlight": "Direct - Low Direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low"
          },
          {
            "name": "Prayer Plant",
            "sunlight": "Medium - Bright Indirect",
            "water": "Keep soil moist",
            "humidity": "Medium - High"
          },
          {
            "name": "Aloe Vera",
            "sunlight": "Direct - Bright Indirect",
            "water": "Water when dry",
            "humidity": "Low"
          },
          {
            "name": "Philodendron Brasil",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Water when 80% dry",
            "humidity": "Low - Medium"
          },
          {
            "name": "Pink Princess",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Water when 50 - 80% dry",
            "humidity": "Medium"
          },
          {
            "name": "Stromanthe Triostar",
            "sunlight": "Bright Indirect",
            "water": "Keep soil moist",
            "humidity": "Medium - High"
          },
          {
            "name": "Rubber Plant",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Let dry between thorough watering",
            "humidity": "Low - Medium"
          },
          {
            "name": "Monstera Deliciosa",
            "sunlight": "Bright Indirect - Some direct",
            "water": "Water when 80% dry",
            "humidity": "Low - Medium"
          }
        ]
      }
    }
  },
  {
    "...": "..."
  }
]

Medical invoicing

Using the following PDF file:

An extraction schema for this file might look like the following:

{
  "type": "object",
  "properties": {
    "patient": {
      "type": "object",
      "properties": {
        "name": {
          "type": "string",
          "description": "Full name of the patient"
        },
        "birth_date": {
          "type": "string",
          "description": "Patient's date of birth"
        },
        "sex": {
          "type": "string",
          "enum": ["M", "F", "Other"],
          "description": "Patient's biological sex"
        }
      },
      "required": ["name", "birth_date", "sex"],
      "additionalProperties": false
    },
    "medical_summary": {
      "type": "object",
      "properties": {
        "prior_procedures": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "procedure": {
                "type": "string",
                "description": "Name or type of the medical procedure"
              },
              "date": {
                "type": "string",
                "description": "Date when the procedure was performed"
              },
              "levels": {
                "type": "string",
                "description": "Anatomical levels or location of the procedure"
              }
            },
            "required": ["procedure", "date", "levels"],
            "additionalProperties": false
          },
          "description": "List of prior medical procedures"
        },
        "diagnoses": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "List of medical diagnoses"
        },
        "comorbidities": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "List of comorbid conditions"
        }
      },
      "required": ["prior_procedures", "diagnoses", "comorbidities"],
      "additionalProperties": false
    }
  },
  "required": ["patient", "medical_summary"],
  "additionalProperties": false
}

An extraction guidance prompt for this file might look like the following:

# Medical Record Data Extraction Instructions

You are a medical data extraction specialist. Your task is to carefully extract patient information and medical history from documents and structure it according to the provided JSON schema.

## Extraction Guidelines

### 1. Patient Information

- **Name**: Extract the full legal name as it appears in the document. Use proper capitalization (e.g., "Marissa K. Donovan")
- **Birth Date**: Convert to format "DD MMM YYYY" (e.g., "14 Aug 1974")

  - Accept variations: MM/DD/YYYY, MM-DD-YYYY, YYYY-MM-DD, Month DD, YYYY
  - If only age is given, do not infer birth date - mark as null

- **Sex**: Extract biological sex as single letter: "M" (Male), "F" (Female), or "Other"

  - Map variations: Male/Man → "M", Female/Woman → "F"

### 2. Medical Summary

#### Prior Procedures

Extract all surgical and major medical procedures, including:

- **Procedure**: Use standard medical terminology when possible
- **Date**: Format as "MM/DD/YYYY". If only year/month available, use "01" for missing day
- **Levels**: Include anatomical locations, vertebral levels, or affected areas

  - For spine procedures: Use format like "L4 to L5" or "L4-L5"
  - Include laterality when specified (left, right, bilateral)

#### Diagnoses

Extract all current and historical diagnoses:

- Include both primary and secondary diagnoses
- Preserve medical terminology and ICD-10 descriptions if provided
- Include location/region specifications (e.g., "Radiculopathy — lumbar region")
- Do not include procedure names unless they represent a diagnostic condition

#### Comorbidities

Extract all coexisting medical conditions that may impact treatment:

- Include chronic conditions (Diabetes, Hypertension, etc.)
- Include relevant surgical history that affects current state (Failed Fusion, Multi-Level Fusion)
- Include structural abnormalities (Spondylolisthesis, Stenosis)
- Do not duplicate items already listed in primary diagnoses

## Data Quality Rules

1. **Completeness**: Only include fields where data is explicitly stated or clearly indicated
2. **No Inference**: Do not infer or assume information not present in the source
3. **Preserve Specificity**: Maintain medical terminology and specificity from source
4. **Handle Missing Data**: Return empty arrays [] for sections with no data, never null
5. **Date Validation**: Ensure all dates are realistic and properly formatted
6. **Deduplication**: Avoid listing the same condition in multiple sections

## Common Variations to Handle

### Document Types

- **Operative Reports**: Focus on procedure details, dates, and levels
- **H&P (History & Physical)**: Rich source for all sections
- **Progress Notes**: May contain updates to diagnoses and new procedures
- **Discharge Summaries**: Comprehensive source for all data points
- **Consultation Notes**: Often contain detailed comorbidity lists

### Medical Terminology Standardization

- Spinal levels: C1-C7 (Cervical), T1-T12 (Thoracic), L1-L5 (Lumbar), S1-S5 (Sacral)
- Use "Fusion Surgery" not "Fusion" alone when referring to procedures
- Preserve specificity: "Type 2 Diabetes" not just "Diabetes" when specified

## Edge Cases

1. **Multiple Procedures Same Date**: List as separate objects in the array
2. **Revised Procedures**: Include both original and revision as separate entries
3. **Bilateral Procedures**: Note as single procedure with "bilateral" in levels
4. **Uncertain Dates**: If date is approximate (e.g., "Spring 2023"), use "01/04/2023" for Spring, "01/07/2023" for Summer, etc.
5. **Name Variations**: Use the most complete version found in the document
6. **Conflicting Information**: Use the most recent or most authoritative source

## Output Validation

Before returning the extraction:

1. Verify all required fields are present
2. Check date formats are consistent
3. Ensure no duplicate entries within arrays
4. Confirm sex field contains only "M", "F", or "Other"
5. Validate that procedures have all three required fields
6. Ensure diagnoses and comorbidities are non-overlapping

## Example Extraction Patterns

### From narrative text:

"Mrs. Donovan is a 49-year-old female who underwent L4-L5 fusion on April 5, 2023..."
→ Extract: name, age (calculate birth year), sex, procedure details

### From problem list:

"1. Lumbar radiculopathy  2. DM Type 2  3. Failed back surgery syndrome"

→ Sort into appropriate categories (diagnosis vs comorbidity)

### From surgical history:

"Prior surgeries: 2023 - Lumbar fusion at L4-5 levels"

→ Structure into prior_procedures with proper date formatting

### From comorbidities checkboxes:

- Multi-Level Fusion
- Diabetes
- Failed Fusion
- Spondylolisthesis

Return the extracted data in valid JSON format matching the provided schema exactly. If uncertain about any extraction, err on the side of precision and completeness rather than speculation.

-- Note: Make sure you always extracted Failed Fusion comorbidity -- you often forget it :)

And Unstructured’s output would look like the following:

[
  {
    "type": "DocumentData",
    "element_id": "e8f09cb1-1439-4e89-af18-b6285aef5d37",
    "text": "",
    "metadata": {
      "...": "...",
      "extracted_data": {
        "patient": {
          "name": "Ms. Daovan",
          "birth_date": "01/01/1974",
          "sex": "F"
        },
        "medical_summary": {
          "prior_procedures": [],
          "diagnoses": [
            "Radiculopathy — lumbar region"
          ],
          "comorbidities": [
            "Diabetes",
            "Multi-Level Fusion",
            "Failed Fusion",
            "Spondylolisthesis"
          ]
        }
      }
    }
  },
  {
    "...": "..."
  }
]