An Evaluation of Machine Learning Applications in Strengthening Decision Support Systems for Effective Clinical and Administrative Healthcare Governance

Abstract

This paper presents a comprehensive analytical framework for evaluating the effectiveness of machine learning models within decision support systems for healthcare management. We investigate the complex interplay between algorithmic design, data quality, and practical implementation constraints within both clinical and administrative contexts. Our methodology combines empirical analysis of performance metrics with theoretical assessments of computational efficiency and explainability requirements. The research demonstrates that ensemble-based approaches incorporating gradient boosting and deep learning architectures consistently outperform traditional statistical methods in identifying high-risk patients and optimizing resource allocation, achieving 17.4\% higher precision and 21.3\% improved recall rates. However, we identify significant challenges regarding transparency in model reasoning and decision boundaries, particularly in high-stakes clinical scenarios. We further analyze the impact of data heterogeneity and missingness on model robustness, demonstrating that federated learning approaches can maintain performance while addressing privacy concerns. This work contributes to the growing literature on healthcare analytics by providing a structured evaluation framework that balances technical performance with practical implementation considerations, enhancing the adoption potential of machine learning solutions in real-world healthcare environments.