Smart PDF Processing: Transform Any Document into Actionable Data

Step 1: Monitor for New Lab Reports

Node: Google Drive Trigger

Settings:

• Trigger On: Changes Involving a Specific Folder
• Folder to Watch: Select your designated barcode upload folder
• Watch For: File Created

What This Does: Automatically detects when medical staff or lab systems upload new PDF reports to your designated Google Drive folder and starts the processing workflow.

Success Looks Like: The trigger activates immediately when a lab report is uploaded, capturing the file information and download link for processing.

Important: Make sure the file sharing setting is set to “Anyone with the link” so that PDF.co can access the file.

Step 2: Convert PDF to Structured Data

Node: PDF.co API → Convert from PDF

Settings:

• Convert Type: PDF to JSON (Simple)
• URL: {{ $json.webContentLink }}

What This Does: Transforms the PDF lab report into structured JSON format, preserving the layout and making text extraction possible while maintaining the relationship between data elements.

Success Looks Like: PDF.co returns structured JSON containing all text elements, tables, and formatting information from the medical report.

Step 3: Download Converted Data

Node: HTTP Request

Settings:

• Method: GET
• URL: {{ $json.url }} (from the conversion step)

What This Does: Downloads the converted JSON data from PDF.co's servers so it can be processed and analyzed by the next steps.

Success Looks Like: The structured lab report data is downloaded and ready for text extraction and AI analysis.

Step 4: Extract Text from Document Structure

Node: Code (JavaScript)

JavaScript Code: Get The Code Here

What This Does: Processes the complex JSON structure to extract readable text from the medical report, handling nested data structures and preserving important formatting and spacing.

Key Logic:

• Navigates through document pages and row/column structures
• Extracts text from each data cell
• Maintains proper spacing and line breaks
• Creates a clean text version for AI analysis

Success Looks Like: Clean, readable text extracted from the lab report containing all patient information, test results, and clinical notes.

Step 5: AI Analysis of Lab Results

Node: OpenAI → Message a Model

Settings:

• Model: GPT-4.1-MINI (or available)
• Prompt: Custom medical analysis prompt for extracting:
  • Patient demographics (name, DOB, age, sex, patient ID)
  • Test details (specimen type, collection date, ordering physician)
  • All lab values with results, units, reference ranges, and abnormal flags
  • Critical values and physician notifications
  • Clinical assessment of abnormal findings
• Message: (Sample)

Analyze this document and extract structured information:

{{$node["Code"].json["extractedText"]}}

Please extract and structure the following information in JSON format:
1. Document type and key identifiers
2. Important dates and references
3. Key data points with values and context
4. Critical information requiring attention
5. Overall document summary

Return only valid JSON with a clear structure.

What This Does: Uses advanced AI to understand the medical context, identify critical lab values, extract structured data, and provide clinical insights about abnormal findings.

Success Looks Like: AI returns structured JSON containing all relevant medical data with proper categorization and clinical context.

Step 6: Structure Data for Google Sheets

Node: Code (JavaScript)

JavaScript Code: Get The Code Here

What This Does: Transforms the AI's JSON response into a flat structure suitable for Google Sheets, mapping complex nested data to individual columns.

Success Looks Like: Clean, structured data ready for spreadsheet insertion with all medical information properly categorized and formatted.

Step 7: Save to Medical Database

Node: Google Sheets → Append Row

Settings:

• Document From List:Select your lab reports tracking spreadsheet
• Sheet From List: Choose the appropriate sheet (usually Sheet1)
• Column Mapping: Maps all extracted data to corresponding spreadsheet columns

What This Does: Adds each processed lab report as a new row in your medical database, creating a comprehensive tracking system for patient lab results and critical values.

Success Looks Like: Each lab report appears as a structured row in your spreadsheet organized for easy analysis and reporting.