Predictive AI for Hospital Readmission Risk

Inspiration

Hospital readmissions are a major challenge in healthcare, leading to increased costs and burdening both patients and providers. We wanted to leverage AI and FHIR-based data interoperability to create a predictive solution that helps hospitals and healthcare providers proactively manage high-risk patients and improve patient outcomes.

What it does

Predictive AI for Hospital Readmission Risk analyzes patient health data from FHIR-compliant sources to predict the likelihood of readmission. The system provides risk scores and alerts to healthcare providers, enabling early interventions, personalized patient care, and reduced hospital readmissions.

How we built it

AI-Driven Predictions – Machine learning model predicts readmission risk. FHIR Integration – Seamlessly fetches patient data from standardized healthcare sources. Actionable Insights – Provides real-time risk scores for proactive decision-making. Secure & Scalable – Uses authentication and authorization mechanisms via OAuth2 and JWT.

Backend: FastAPI for AI model inference and FHIR integration. Machine Learning: Trained a Random Forest Classifier with real and synthetic patient data. FHIR Data Integration: Leveraged MeldRx API to securely fetch patient records. Authentication: Implemented OAuth2 authentication to access patient data securely. Cloud Deployment: Designed for cloud compatibility for real-world scalability.

Challenges we ran into

FHIR Data Access Issues: Setting up API authentication and authorization with MeldRx was a learning curve. Large Model Files: Managing and deploying large AI models required optimizing storage and memory usage. Handling Missing Data: Real-world patient data often contains missing values; we had to preprocess and normalize the data effectively.

Accomplishments that we're proud of

Successfully built and trained an AI model with >80% accuracy in predicting readmission risks. Implemented secure authentication & authorization using OAuth2 and JWT. Integrated FHIR-compliant patient data seamlessly into our AI model. Developed a scalable architecture that can be expanded to other healthcare prediction models.

What we learned

FHIR API Authentication – Setting up secure API calls with OAuth2 authentication. Healthcare Data Handling – Challenges in real-world patient data processing. Machine Learning Model Optimization – Balancing accuracy and interpretability for healthcare AI. Deployment Strategies – Managing AI models efficiently in cloud environments.

What's next for Predictive AI for Hospital Readmission Risk

This project is just the beginning! We envision a future where AI-driven insights empower healthcare providers to take proactive action, improve patient outcomes, and reduce unnecessary hospital readmissions.

Built With

• node.js
• python
• react