An optimized predictor for patient health records while ensuring HIPAA compliance

Abstract

The increasing digitization of healthcare has led to predictive analytics becoming an essential tool for early risk detection and personalized patient care. This project introduces an optimized predictor for Patient Healthcare Records. This is a microservices-driven, AI-based system architected to analyze patient data while maintaining HIPAA (Health Insurance Portability and Accountability Act), ensuring scalability through Dockerized deployment. The system functions through three main phases: (1) Data processing through Optical Character Recognition (OCR), which extracts text and refines patient data from medical records; (2) Health Risk Prediction utilizing a Hidden Markov Model (HMM) for sequential health analysis and Neural Networks for predictive modeling; and finally, (3) Secure storage and Recommendations where the predictions are organized in a structured PostgreSQL database and accessed via a web/mobile platform built with HTML and CSS. This design guarantees effective, privacy-conscious, and AI-enabled healthcare analytics, delivering real-time insights for healthcare professionals and providing them with a streamlined, scalable, and secure method for health risk prediction, supporting proactive medical decision-making.