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AI Clinical Notes Summariser

A Flask-based web application that demonstrates how AI can transform clinical notes into structured summaries, as well as providing proposed codes, while aligning with UK clinical coding standards.

Overview

From the user input, this project produces:

  • Structured clinical summaries
  • Key diagnoses and co-morbidities
  • Procedure identification
  • Suggested ICD-10 (UK) and OPCS-4 codes

Tech Stack

  • Backend: Python, Flask
  • Frontend: HTML, CSS, Jinja templating
  • AI Integration: Designed for LLM APIs
  • Version Control: Git, GitHub

Features

  • User input is submitted via a web form
  • AI powered summary is displayed underneath
  • Prompting breaks the summary down into clear sections:
    • Summary
    • Diagnoses
    • Procedures
    • Code Suggestions
  • Query history page to review past queries for each session
  • Sample input buttons for quick testing of the application
  • Input validation and error handling
  • Basic UI styling

Example Use Case

This application simulates how AI could support clinical coding workflows by assisting with the interpretation of unstructured medical notes and suggesting relevant coding outputs.

Project Structure

ai-clinical-summariser/
│
├── app/
│ ├── init.py
│ ├── routes.py
│ ├── services/
│ │ └── ai_service.py
│ ├── templates/
│ │ ├── index.html
│ │ └── history.html
│ └── static/
│   └── styles.css
│
├── config.py
├── run.py
├── requirements.txt
├── .env (not included)
└── README.md

Running the Application

The application is deployed and accessible here:

Running Locally

  • Create a '.env' file in the root directory, and code the below into it (using your own API code)
  • OPENAI_API_KEY=your_api_key_here
  • Install dependencies:
    • pip install -r requirements.txt
  • Run the application:
    • python run.py
  • Then open in browser:

Motivation

The motivation behind this particular project was to combine both my current and aspiring careers into one application. It also is a result of my growing interest in AI and its potential applications across all fields. This served as a good learning project and an introduction to working with AI.

Honest Review of The AI Output

As someone who has worked in the clinical coding field for a long time, I can say from the tests I have performed that the summaries of diagnoses and procedures are very good, and the codes are somewhat accurate. However there are a number of errors that are made in sequencing, selection, and omission of codes. Overall though the results are impressive given the basic nature of this application.

Future Improvements

  • Database storage for improved query history
  • Enhanced UI
  • More in depth AI prompt engineering
  • Authentication for individual User sessions