Clinical Decision Support Systems and Patient Safety: The Mediating Role of Error Reduction

Abstract

Clinical Decision Support Systems (CDSS) have become a central component of modern healthcare digital transformation, assisting clinicians in making accurate, timely, and evidence based decisions. These systems integrate patient data, clinical guidelines, and predictive analytics to support diagnosis, treatment selection, and medication management. Despite widespread adoption, the impact of CDSS on patient safety varies across healthcare settings. This study examines the relationship between Clinical Decision Support Systems and patient safety, with a focus on the mediating role of error reduction. Drawing on Information Processing Theory, Sociotechnical Systems Theory, and the Swiss Cheese Model of patient safety, the study proposes that CDSS improves patient safety by reducing clinical, diagnostic, and medication errors. A quantitative cross sectional research design is proposed involving physicians, nurses, pharmacists, and healthcare IT professionals. Data are analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) via SmartPLS 4. The findings indicate that CDSS significantly improves patient safety, while error reduction significantly mediates this relationship. The results suggest that CDSS enhances safety outcomes primarily by minimizing human and system related errors. This study contributes to health informatics and patient safety literature by clarifying the mechanism through which CDSS improves healthcare outcomes. The findings provide practical implications for hospital administrators, policymakers, and system designers aiming to optimize CDSS implementation for safer healthcare delivery.