I am adding SMS text message to the original code to help with patients have easier access to the discharge summaries

import os import base64 import markdown from openai import OpenAI from pdf2image import convert_from_path from io import BytesIO from flask import Flask, render_template, request, redirect, url_for, flash, session, make_response from form import EditForm from flask_ckeditor import CKEditor from datetime import datetime, timedelta from xhtml2pdf import pisa

app = Flask(name) ckeditor = CKEditor(app)

UPLOAD_FOLDER = 'static/asset/' app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER app.config['SECRET_KEY']= os.environ.get("SECRET_KEY")

Set session lifetime to 60 minutes

app.permanent_session_lifetime = timedelta(minutes=60)

client = OpenAI( api_key=os.environ.get("OPENAI_DISCHARGE_API") )

def generate_unique_filename(): timestamp = datetime.now().strftime("%Y%m%d%H%M%S") return f"{timestamp}.pdf"

encoded_images = []

convert pdf file to a list of images

def pdf_to_encoded_imgs(pdf_path): global encoded_images images = convert_from_path(pdf_path)

encoded_images = []  # Clear the list before appending new images

for image in images:
    # convert image to bytes
    buffered = BytesIO()
    image.save(buffered, format='JPEG')

    # encode the image to Base64
    img_str = base64.b64encode(buffered.getvalue()).decode()

    # append the encoded string to the list
    encoded_images.append(img_str)

return encoded_images

def send_request(encoded_images, custom_prompt): # send request to ChatGPT response = client.chat.completions.create( model="gpt-4-vision-preview", messages=[ { "role": "user", "content": [ { "type": "text", "text": custom_prompt, }, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{encoded_images[0]}", }, }, ], } ], max_tokens=4000, )

return response.choices[0].message.content

@app.route("/", methods=['GET', 'POST']) def home(): global encoded_images if request.method == 'POST': if 'pdf' not in request.files: flash('No file part') return redirect(url_for('home')) file = request.files['pdf'] if file.filename == '': flash('No selected file') return redirect(url_for('home')) if file and file.filename.endswith('.pdf'): filename = generate_unique_filename() file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) encoded_images = pdf_to_encoded_imgs(f'static/asset/{filename}') return redirect(url_for('get_clinic')) return render_template('index.html')

@app.route("/clinic", methods=['GET', 'POST']) def get_clinic(): global encoded_images content = send_request(encoded_images, clinical_prompt) content = content.replace('\n', '
') content = markdown.markdown(content) session['content'] = content session['is_clinic'] = True return redirect(url_for('get_summary'))

@app.route('/patient-friendly', methods=['GET', 'POST']) def get_patient_friendly(): global encoded_images content = send_request(encoded_images, patient_friendly_prompt) content = content.replace('\n', '
') content = markdown.markdown(content) session['content'] = content session['is_clinic'] = False return redirect(url_for('get_summary'))

@app.route("/summary", methods=['GET', 'POST']) def get_summary(): content = session.get('content', None) is_clinic = session.get('is_clinic', None) return render_template('edit.html', content=content, is_edit=False, is_clinic=is_clinic)

@app.route("/edit", methods=['GET', 'POST']) def edit(): content = session.get('content', None) form = EditForm( body = content ) if form.validate_on_submit(): content = form.body.data session['content'] = content flash("You've updated the discharge summary") return redirect(url_for('get_summary')) return render_template('edit.html', is_edit=True, form=form, content=content)

@app.route('/download', methods=['GET']) def download_pdf(): content = session.get('content', None) if content: # Convert HTML content to PDF using xhtml2pdf pdf_data = generate_pdf(content) if pdf_data: response = make_response(pdf_data) response.headers['Content-Type'] = 'application/pdf' response.headers['Content-Disposition'] = 'attachment; filename=discharge_summary.pdf' return response else: flash('Failed to generate PDF.') else: flash('No content available for download.') return redirect(url_for('get_summary'))

Define patient friendly prompt and clinical prompt

patient_friendly_prompt = ("Given a set of clinical notes from a patient's medical record, produce a clear and concise medical discharge summary. The summary should succinctly include the following core components:\n\n" "1. Reason for Admission: Summarize the primary cause or event leading to the patient's hospitalization.\n" "2. Key Investigations and Results: Summarize important diagnostic tests conducted during the hospital stay and their outcomes and why they were done.\n" "3. Procedures Performed: Briefly list any medical or surgical procedures the patient underwent during their stay, highlight why this was done.\n" "4. Primary and Secondary Diagnoses: Briefly state the main and any secondary diagnoses made during the hospitalization.\n" "5. Medication Changes: Note any changes to the patient's medication regimen during their stay and why these were done.\n" "6. Plan for Follow-Up: Include briefly appointments, tests, or treatments scheduled after discharge for ongoing care as mention in the clinical notes.\n" "The summary generated should be presented to a non-medical patient in second person, avoid formatting the summary as a letter. Therefore, medical jargon should be explained succinctly within a parenthesis next to the medical term. This should be easy to understand but not lack detail on what term or procedure is being explained." "Please generate the core components into 6 short concise paragraphs. The discharge summary should be legible and easy to read. Keep this summary specific to the patient and do not omit any important details from the original clinical note such as diagnosis, values from tests and procedures, reasons for medication changes, plans for follow up.")

clinical_prompt = ("Given a set of clinical notes from a patients medical record, produce a clear and concise medical discharge summary. The summary should succinctly include the following 10 core components:\n\n" "1. Reason for Admission: Summarize the primary cause or event leading to the patient's hospitalization.\n" "2. Relevant Past Medical and Surgical History: Include any significant past illnesses and surgeries that are pertinent to the current condition.\n" "3. Social Context: Outline the patient's social situation, including smoking and alcohol history, family, living conditions, and support systems, if relevant.\n" "4. Key Investigations and Results: Detail important diagnostic tests conducted during the hospital stay and their outcomes.\n" "5. Procedures Performed: List any medical or surgical procedures the patient underwent during their stay.\n" "6. Primary and Secondary Diagnoses: State the main and any secondary diagnoses made during the hospitalization.\n" "7. Medication Changes: Note any changes to the patient's medication regimen during their stay. Highlight why the medication has been changed\n" "8. Medications to be Reviewed by the GP: Identify medications that require follow-up or review by the general practitioner.\n" "9. GP Actions Following Discharge: Specify actions or monitoring the GP should undertake post-discharge.\n" "10. Plan for Follow-Up: Include appointments, tests, or treatments scheduled after discharge for ongoing care.\n" "Organize the summary with 10 clear headings for each core component, ensure there are sub-bullet points with clear and concise information, maintaining the clarity and brevity of the discharge summary.\n" "Please also ensure that the summary highlights What the plan is for the patient post discharge, including any follow-up appointments, changes to medication, tests, or treatments that are scheduled.")

def generate_pdf(html_content): buffer = BytesIO() pisa_status = pisa.CreatePDF(html_content, dest=buffer) if pisa_status.err: return None else: pdf_data = buffer.getvalue() buffer.close() return pdf_data

if name == 'main': app.run(debug=True, port=5000)

#updating this to help formulate discharge summaries using AI- coming soon

Discharge Summary Generator

This project is a Flask-based web application designed to generate medical discharge summaries from uploaded PDF files. It provides two options for generating summaries:

• Clinical discharge summary (detailed, professional format for healthcare providers)
• Patient-friendly discharge summary(simplified and accessible format for patients).

Features

• Upload PDF documents containing clinical notes.
• Extract content from PDFs and process them using OpenAI’s GPT-4 model.
• Generate:
  • Detailed discharge summaries for clinicians.
  • Simplified summaries for patients.
• Edit generated summaries before finalizing.
• Download summaries as PDFs.
• Optional SMS notifications using Twilio for patient updates.

Prerequisites

System Requirements

• Python 3.8 or later.
• Flask and associated libraries.
• Twilio account (optional for SMS functionality).

Environment Variables

Set the following environment variables in your system:

• SECRET_KEY: Secret key for Flask sessions.
• OPENAI_DISCHARGE_API: OpenAI API key.
• TWILIO_ACCOUNT_SID: Twilio account SID (for SMS functionality).
• TWILIO_AUTH_TOKEN: Twilio authentication token (for SMS functionality).
• TWILIO_PHONE_NUMBER: Twilio phone number for sending SMS.

Installation

1. Clone the repository: bash git clone https://github.com/your-repo/discharge-summary-generator.git cd discharge-summary-generator

2. Create and activate a Python virtual environment: bash python -m venv venv source venv/bin/activate # On Windows, use venv\Scripts\activate

3. Install dependencies: bash pip install -r requirements.txt

4. Configure environment variables in a .env file (or export them manually).

5. Run the application: bash python app.py

Usage

1. Access the application at http://localhost:5000.
2. Upload a PDF file containing clinical notes.
3. Choose to generate either a clinical or patient-friendly summary.
4. Edit the summary if necessary.
5. Download the finalized summary as a PDF or notify the patient via SMS (optional).

File Structure

• app.py: Main application logic.
• templates/: HTML templates for rendering web pages.
• static/asset/: Folder for storing uploaded PDFs.
• form.py: Flask-WTF form definitions.
• requirements.txt: Dependencies for the application.

Key Functions

pdf_to_encoded_imgs(pdf_path)`

Converts PDF pages into base64-encoded images for processing.

send_request(encoded_images, custom_prompt)

Sends encoded images and prompts to OpenAI’s GPT-4 model for generating summaries.

generate_pdf(html_content)

Converts HTML content into a downloadable PDF using xhtml2pdf.

send_sms(message)

Sends SMS notifications using Twilio (optional).

Prompts for Summaries

Patient-Friendly Summary

Generates a concise, easy-to-read discharge summary using layman’s terms.

Clinical Summary

Generates a detailed discharge summary with professional formatting and 10 clear headings.

Dependencies

• Flask
• OpenAI
• markdown
• pdf2image
• xhtml2pdf
• Flask-WTF
• Flask-CKEditor
• Twilio

Contributing

1. Fork the repository.
2. Create a new branch for your feature/bugfix.
3. Submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.