The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.

Background/Objectives: Healthcare service quality is a critical dimension of patient safety and operational performance. Traditional assessment approaches are often limited in their ability to support prediction, which has increased interest in machine learning (ML) models. This systematic review assessed the effectiveness of ML algorithms in predicting healthcare service quality metrics, with emphasis on their applications, comparative performance and implementation challenges. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, CINAHL, and ScienceDirect for studies published between 1 January 2020 and 30 July 2025. Following title/abstract screening and full-text review, 49 studies were included. Studies were grouped into conventional/ensemble ML and deep learning categories based on the primary model class analyzed in each article. Results: The reviewed studies focused mainly on acute clinical and operational outcomes, especially length of stay (27.0%), mortality (23.0%), and readmission rates (18.0%), while subjective, patient-centered metrics received less attention. Conventional and ensemble ML models, particularly RF and XGBoost, were frequently reported, while deep learning models were used in more complex prediction tasks. Conclusions: The evidence suggests that well-validated and interpretable ML models can support healthcare quality prediction. However, important challenges remain regarding implementation, validation, generalizability, and data heterogeneity.