A Comprehensive Review of Data-Driven Models for Patient Safety and Care Quality

Abstract

The technology in patient care has changed patient safety and care quality by providing models that improve diagnosis and health service delivery and reduce risks. Recognizing the increasing use of modeling in healthcare to enhance patient outcomes, this review examines machine learning, artificial intelligence, and statistical modeling of patient outcomes. We discuss how these models conduct ponderous data computing and interpret vast amounts of electronic health records, clinical data, and patient-reported outcomes for predictive analytics, early warning for safety concerns, and identifying qualities that need enhancements. The paper also provides vital limitations like data privacy, model interpretability, and compatibility with existing health information technology systems(McCaffrey & Boudreaux, 2019). A brief overview of the most recent developments is presented, along with case studies that demonstrate how these models are used in clinical practice to minimize adverse occurrences, optimize patient-care pathways, and improve the efficiency of treatment delivery. This review offers direction for the research agenda and the expanded application of data to improve patient safety and quality care across multiple healthcare contexts.