18 maart 2026
Improving Health Care with Clinical Prediction Models
Clinical prediction models are increasingly used to support health care decisions, helping clinicians estimate disease risk, guide treatment choices, and tailor care to individual patients. At the same time, advances in data science and artificial intelligence have led to a rapid expansion of prediction modelling research. Yet despite this growth, only a small proportion of published models ever reach clinical practice. Improving Health Care with Clinical Prediction Models – From Idea to Impact shows how you can bridge this gap. The book guides readers through the full pathway of prediction modelling in health care: from defining a meaningful clinical problem and identifying existing models to developing, validating, evaluating, and implementing prediction model–based innovations. Combining methodological guidance with real-world clinical examples, the book explains not only how to build robust prediction models but also how to ensure they become useful tools in everyday care. Written in an accessible style and requiring only a basic understanding of statistical modelling, it provides students, researchers, and clinicians with a practical roadmap for turning prediction models into innovations that are useful, usable, and used. What this book helps you do This book takes you through the complete process of clinical prediction modelling, from the first idea for a model to its implementation in health care practice. Along the way, readers learn how to: Define a meaningful clinical problem and identify where prediction models can improve health care Find and critically evaluate existing prediction models before deciding to develop a new one Handle missing data using methods such as multiple imputation Develop and validate prediction models using established regression approaches Apply advanced regression and artificial intelligence methods for clinical prediction modelling Select models with real-world impact potential and translate them into practical decision tools Evaluate the impact of prediction model–based innovations using decision modelling and empirical studies Implement prediction model–based innovations in clinical practice and new health care settings