Patient Outcomes Through Machine Learning: A Review Of Data Management Strategies in Healthcare

Abstract

This systematic review explores the role of machine learning (ML) in optimizing patient outcomes through effective data management strategies in healthcare systems. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous and transparent review process was conducted, encompassing an initial pool of 560 articles. After systematic screening, eligibility assessment, and full-text reviews, a total of 50 articles were included for detailed analysis. The findings of this review highlight how ML applications are transforming healthcare by enhancing diagnostic accuracy, enabling personalized treatment plans, and supporting predictive analytics for preventive care. Specifically, ML-driven tools have demonstrated significant improvements in disease detection, treatment personalization through the integration of genomic and pharmacological data, and early identification of high-risk patients using predictive analytics. Effective data management practices, including data integration, quality enhancement, and privacy-preserving techniques such as federated learning and differential privacy, were identified as key enablers for these applications. However, challenges such as biases in ML models, ethical concerns, and data interoperability issues remain significant barriers to implementation. The review emphasizes the critical role of robust data governance frameworks and interdisciplinary collaboration in overcoming these barriers and advancing ML applications in healthcare.