Optimizing Healthcare Systems with Machine Learning-Based Decision Support

Abstract

The integration of Machine Learning (ML) in healthcare decision support systems has the potential to transform the efficiency and effectiveness of medical services. This article explores the application of ML algorithms in healthcare, focusing on clinical decision support, diagnostic assistance, patient management, and predictive analytics. We examine how ML can optimize healthcare workflows, improve clinical outcomes, reduce errors, and enhance patient care. Challenges such as data quality, privacy concerns, and the interpretability of ML models are discussed, along with potential solutions. Furthermore, the article outlines future research directions aimed at overcoming these barriers and maximizing the impact of ML in healthcare systems.