Machine Learning Approaches in Remote Patient Monitoring and Healthcare QA

Abstract

This paper presents an intelligent remote patient monitoring (RPM) system that integrates machine learning for predictive alerting and quality assurance (QA). The proposed framework builds on established principles of leveraging AI and IoT technologies to enhance RPM for healthcare systems. It extends prior system architectures by embedding anomaly detection algorithms, patient-specific risk models, and test case prioritization methods for clinical QA. The system was evaluated using simulated patient data, demonstrating significant improvements in early warning accuracy, reductions in QA cycle times, and enhanced compliance with healthcare software quality standards. These findings highlight the adaptability and impact of advanced AI-enabled frameworks in transforming healthcare infrastructure, reinforcing the importance of integrating predictive and QA mechanisms for improved patient outcomes.