How to Build a Dynamic ICD-10 Code Extractor Using Langflow and AI

A Step-by-Step Guide for Healthcare Professionals

In healthcare, managing medical records and ensuring accurate ICD-10 code assignments is critical for both diagnosis and billing. However, manually extracting these codes from unstructured text—such as doctor-patient progress notes—can be tedious and prone to errors. Using AI-powered automation can streamline this process, making it faster, more accurate, and scalable.

We’ll go beyond the basics and show how to build a dynamic ICD-10 code extractor using Langflow. By incorporating more components like data validation, context enrichment, and external APIs, we’ll create an intelligent pipeline that automates the extraction of ICD-10 codes while ensuring accuracy and flexibility.

Let’s dive into the technical details and explore how we can combine AI, memory management, external APIs, and dynamic prompts to create a powerful ICD-10 code extractor.

1. Why Choose Langflow for Dynamic Workflows?

Langflow provides an intuitive, visual interface that allows you to create complex AI workflows without heavy coding. Its modular components—such as Memory, Prompts, LLMs, APIs, and Logic Flows—allow for flexibility in how you process, analyze, and output data. For healthcare automation, this makes it a perfect tool to design dynamic workflows that can adapt to varied input formats, external data sources, and user-specific contexts.

By leveraging Langflow, we can create a highly dynamic system that can handle:

• Unstructured data inputs (progress notes, symptoms)
• Contextual data enrichment (using past patient data)
• External data validation (ICD-10 code validation from official databases)
• Advanced AI integration (LLM processing for complex medical text)

2. Architecture of the Dynamic ICD-10 Code Extractor

Let’s build a more dynamic ICD-10 code extraction workflow. The architecture now includes additional components like data validation, context enrichment via memory, and logic branching to handle different input scenarios (e.g., structured vs. unstructured data).

Key Components:

1. Memory Component: Enriches inputs with patient history.
2. Prompt Component: Generates dynamic prompts based on input type (structured/unstructured).
3. OpenAI Model: Processes medical text for ICD-10 extraction.
4. External API for Validation: Validates ICD-10 codes against an external database.
5. Logic Flow for Data Handling: Dynamically adjusts workflows based on input complexity.
6. Output Component: Displays validated ICD-10 codes and additional context (symptoms, past diagnoses).

3. Step-by-Step Implementation in Langflow

Step 1: Contextual Memory Enrichment

First, we use Langflow’s Memory Component to store and access patient history. This adds context to the current progress note and helps the AI model better understand the patient’s medical background. For example, if a patient has a history of COPD, the model can prioritize respiratory-related ICD-10 codes when analyzing new symptoms.

• How it works: The memory component is linked to the patient’s previous visits and stored symptoms/diagnoses.
• Example:Past note: "Patient has COPD and chronic hypertension."New note: "Patient reports shortness of breath and worsening cough."

The memory component helps the model infer that the shortness of breath is likely related to the patient’s existing COPD diagnosis.

Memory Schema:

sql
---------------------------------------
CREATE TABLE patient_history ( 
patient_id INT PRIMARY KEY, 
previous_conditions TEXT[], 
past_icd_codes TEXT[] 
);        

This table stores each patient’s medical history, making it easier to enrich future inputs with relevant context.

Step 2: Dynamic Input Component – Handling Structured and Unstructured Data

Next, we configure the Input Component to handle both structured and unstructured progress notes. In real-world settings, doctors may enter symptoms in free text or select them from predefined forms. Langflow allows us to branch workflows based on the input format.

• Unstructured Input: Direct free-text input, e.g., "The patient complains of chest pain and fatigue."
• Structured Input: A form with predefined fields for symptoms and diagnoses, which are easier to extract but require different prompt formatting.

Using Langflow’s Logic Flow Component, we can dynamically route the input to the correct processing path:

• If the input is structured, the prompt is customized for form-based extraction.
• If the input is unstructured, the prompt is designed for NLP-based analysis.

Logic Flow: 
IF input_type == "structured" 
THEN use form_prompt 
ELSE use free_text_prompt        

Step 3: Dynamic Prompt Creation Based on Input Type

The Prompt Component generates a dynamic query for the AI model, customized according to the input type (structured or unstructured).

• For Structured Data: "Extract ICD-10 codes for the following structured diagnoses: {diagnosis_field}."
• For Unstructured Data: "Analyze the following progress note and extract relevant ICD-10 codes: {progress_note}."

This allows for greater flexibility in handling a variety of input formats, making the extractor more versatile.

Step 4: Advanced Language Model Processing

Langflow’s Model Component is configured to use OpenAI’s GPT-4 for understanding the nuances of medical text. The language model processes the input, applying natural language understanding to extract the relevant ICD-10 codes.

• What makes it dynamic: The model adjusts its response based on the context provided by the memory (patient history) and the input type (structured or unstructured). It generates ICD-10 codes, prioritizing diagnoses related to the patient’s history or newly reported symptoms.

Example of AI Output:

• Input: "Patient has been coughing heavily for the past two weeks. History of COPD."
• Extracted ICD-10 Codes:J44.1 – Chronic obstructive pulmonary disease with acute exacerbationR05 – Cough

Step 5: External ICD-10 Code Validation via API

To ensure accuracy, we add an API Component to validate the extracted ICD-10 codes against an external database. This ensures that the codes are correct and up to date with the latest medical standards.

• External API: We can use official APIs like WHO’s ICD-10 API or internal hospital databases to verify that the codes match the correct descriptions.
• How it works: After the AI model generates the codes, they are passed to the API for validation. The response includes the official description of each ICD-10 code.

Example API Call:

bash
--------------
curl -X GET "https://icd.api.who.int/api/v1/icd/lookup?code=J44.1" -H "Authorization: Bearer <API_TOKEN>"        

Response:

json
--------------------------------------------------------------------------------
{ 
"code": "J44.1", 
"description": "Chronic obstructive pulmonary disease with acute exacerbation" 
}        

Step 6: Logic Flow for Data Handling and Error Management

Using Logic Flow Components, we implement conditional logic to handle various scenarios in the workflow:

• Error Handling: If the ICD-10 code generated by the AI model does not match any valid codes from the external API, an error message is returned. This triggers an alternate prompt asking the AI to review the input and refine its extraction process.
• Branching for Additional Inputs: If the AI detects ambiguous symptoms or conflicting diagnoses, it triggers a prompt to request additional input from the user (doctor).

plaintext
------------------------------------------------------------------------------
IF api_validation == "invalid_code"
THEN trigger_review_flow
ELSE display_output        

This error management ensures the extractor is both dynamic and robust, capable of handling edge cases or incomplete data.

Step 7: Output Component with Additional Context

Finally, the Output Component presents the validated ICD-10 codes along with additional context, such as:

• Description of the diagnosis.
• Relevant patient history (e.g., previous conditions and ICD-10 codes).
• Confidence level of the AI’s diagnosis (based on API validation and logic flows).

This contextual output helps healthcare professionals verify the results and ensure that all necessary conditions are considered.

Example output:

plaintext
------------------------------------------------------------------
ICD-10 Codes Extracted:
1. J44.1 - Chronic obstructive pulmonary disease with acute exacerbation
2. R05 - Cough

Patient History:
- Previous Conditions: COPD, Hypertension
- Past ICD-10 Codes: J44.9, I10        

4. Database Integration and Schema Design

To support the dynamic nature of the workflow, we design a more flexible database schema that stores both structured and unstructured input, along with validated ICD-10 codes and metadata.

Database Schema:

sql
--------------------------------------------------------------------------------
CREATE TABLE icd_extractions (
    extraction_id SERIAL PRIMARY KEY,
    patient_id INT,
    encounter_date TIMESTAMP,
    input_type VARCHAR(20), -- Structured or Unstructured
    progress_note TEXT,
    icd_codes TEXT[], -- Array to store extracted ICD-10 codes.
    validation_status BOOLEAN,
    additional_context JSONB -- Stores metadata such as confidence levels or patient history.
);        

This schema ensures that the entire extraction process is documented and available for auditing or future reference.

5. Frontend and API Integration

To provide a seamless experience, we expose the Langflow workflow via an API endpoint. The frontend, built with React or Vue.js, allows doctors to submit progress notes, view extracted ICD-10 codes, and verify the results with patient history.

Key Features:

• Real-time ICD-10 code extraction.
• Validation status shown alongside extracted codes.
• Interactive prompts that allow doctors to provide additional context if needed.

By combining multiple components—memory, dynamic prompts, LLMs, API integration, and logic flows—Langflow empowers us to build a dynamic ICD-10 code extractor tailored for healthcare professionals. This workflow automates a traditionally manual process, making it faster, more accurate, and adaptable to various input scenarios.

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