Applying Quality Improvement Science to Patient Safety: Strategies, Frameworks, and Sustainable Solutions

Abstract

Healthcare is a constantly evolving field enriched by new technologies, medications, and treatment methods. However, these continuous innovations also introduce new complexities that can pave the way for medical errors to arise. As a result, quality of care and patient safety are always at stake, highlighting the imperative to set up processes to avoid errors in healthcare at any cost. This need for systematic approaches has led to the adoption of Quality Improvement Science (QIS), which deals with the early identification of problems and suggests ways to prevent them in a proactive manner. This study explores the principles of QIS as applied to patient safety, examining various approaches and proposing strategies to implement effective solutions. It further investigates methods for constant quality improvement, emphasizing the roles of technology and human resources in enhancing healthcare quality and patient safety. In particular, it studies how artificial intelligence (AI) strengthens information gathering and organization to provide practical insights. Furthermore, this study discusses the enablers and barriers to successful implementation of these quality improvement processes. Crucially, this paper provides a comprehensive and actionable framework for selecting appropriate QIS tools and indicators, developed through a structured synthesis of QIS literature and represented as decision flows that enable systematic care delivery problem identification and analysis.