AI Solutions for Healthcare Professionals

Introduction In the ever-evolving field of healthcare, providing relevant and specialized content to healthcare professionals is crucial. To address this need, our team developed a sophisticated content recommendation engine powered by machine learning. This engine personalizes content based on healthcare professionals' specialties and their site interactions, significantly enhancing their engagement and the relevance of the information they receive.

Project Overview

1. Objective The primary objective of this project was to create a content recommendation engine that delivers specialized content to healthcare professionals based on their site views, Google Analytics 4 (GA4) data, and data collected from various sources. The goal was to ensure that each healthcare professional receives content that is most relevant to their interests and high demand in their location, thereby improving their experience and professional knowledge.

2. How It Works – Technology and Components

System Architecture

1. Data Collection

Site Interaction Data: Collected using GA4, including page views, clicks, and time spent on different sections of the website.

Production MySQL Database that feels the transformation that generates the Input data for the AI Module.

Gathered regarding healthcare professionals’ specialties and location IDs.

2. Data Processing

Data Cleaning: Ensured high-quality inputs for the machine learning model.

Feature Extraction: Built a comprehensive dataset representing the interactions and preferences of the healthcare professionals.

Machine Learning Model

1. Recommendation Model: Developed using Python, leveraging collaborative filtering and content-based filtering techniques to predict and recommend the most relevant content to each healthcare professional.

2. Model Trained: Trained on historical interaction data and continuously updated with new data to improve its accuracy.

Details on the AI Module that takes prepped data from GCP and produces the output used by the wallboard creation process.

Input

Content Enhanced
Physicians Enhanced
Updated Locations
Specialty Master
ICD10 Codes
Labs Data
User Content Activity

Ensemble Model

Content Based Filtering
LLM classification
Collaborative filtering

Output

ICD10 Specially Derivation
Scored Content by NPI

Web Application

1. Frontend: Developed a React-based interface to display personalized content recommendations to the creators, which then create specialized wallboards for individual healthcare professionals based on the recommended content.

2. Backend: Built with Node.js, handling data requests and efficiently serving the recommended content.

User Feedback Loop

1. Feedback Mechanism: Integrated into the application, allowing healthcare professionals to rate the recommended content.

2. Continuous Learning: Feedback was fed back into the machine learning model, facilitating continuous improvement.

Implementation Details

1. Frontend (React): Developed an intuitive and user-friendly interface for creators to interact with the platform to gather the recommended content. Implemented dynamic content rendering based on the recommendations provided by the backend.

2. Backend(Node.js): Handled API requests and responses, ensuring seamless communication between the frontend and the machine learning model. Managed user authentication and authorization, ensuring secure access to the application.

3. Machine Learning (Python): Implemented algorithms that consider both collaborative filtering (to leverage patterns from similar users) and content-based filtering (to analyze the content's attributes).

4. User Feedback Integration: Developed a system to collect user feedback on the recommendations. Integrated this feedback into the training data, allowing the model to adapt and improve over time.

Results

1. Enhanced User Engagement

Feature Extraction Built a comprehensive dataset representing the interactions and preferences of the healthcare professionals.

2. Improved Content Relevance

Feedback indicated that healthcare professionals found the recommended content more relevant and useful, enhancing their professional knowledge and practice.

3. Continuous Improvement

The feedback loop enabled continuous refinement of the recommendation model, ensuring that the content remained relevant and up to date.

Conclusion

The development of the content recommendation engine was a significant success, demonstrating the power of machine learning in personalizing content delivery. By leveraging advanced analytics and machine learning, we provided healthcare professionals with valuable, specialized content, improving their overall experience and professional development. This project highlights the potential for future enhancements and applications of machine learning in the healthcare industry.