Clinical prediction models have the potential to improve health care by supporting earlier diagnosis, guiding treatment decisions, and enabling more personalised care. Yet despite the rapid growth of prediction modelling research, only a small proportion of models are ultimately used in clinical practice.
This book addresses that gap by guiding readers from defining a clinical problem, identifying or developing suitable models, to evaluating their real-world impact and implementing prediction-model–based innovations in health care.
Published by Maastricht University Press as an open-access textbook
This book is freely available to download or read online, with an affordable print-on-demand edition for readers who prefer a physical copy.
Book details
ISBN
9789403865706
Publication date (online)
18-03-2026
Copyright and license
© 2026 The Authors – The content of this work is licensed under a CC BY 4.0 International License.
Book Description
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.
Key Features
- Covers the full pathway from idea to impact, guiding readers from identifying a clinical problem to developing, evaluating, and implementing prediction model–based innovations
- Integrates methodological foundations with real-world health care practice, illustrated with clinical examples throughout the book
- Accessible to readers with a basic background in statistical modelling, making complex methods accessible to a broad audience
- Didactic structure with learning objectives and practical examples, making the book suitable for graduate-level teaching
- Addresses responsible use of prediction models, including bias, transparency, and implementation challenges
- Open Access publication, freely available via Maastricht University Press
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
The authors progress beyond simply developing accurate models to address how they can be transformed into comprehensive clinical tools […] for real-world impact.
Prof. Niels Peek, University of Cambridge
This book provides a valuable resource for health care professionals and researchers seeking accessible guidance to prediction model development, validation, and impact assessment. It may assist in minimizing research waste by increasing attention to good practice and implementation aspects.
Prof. Ewout W. Steyerberg, University Medical Center Utrecht
Target audiences and educational use
The book is suitable for graduate-level courses on predictive modelling, clinical epidemiology, and AI in healthcare, and is written for an international audience.
- Master’s and PhD students in health sciences, medicine, epidemiology, and data science
- Researchers and educators in clinical epidemiology, health data science, and evidence-based medicine
- Clinicians and health professionals working with prediction models or AI-based decision support
Press and media
For press inquiries and media requests, please contact epid-cpm@maastrichtuniversity.nl.
Author photos and additional information are available on request.
Publication details and metadata
| Title | Improving Health Care with Clinical Prediction Models |
|---|---|
| Subtitle | From Idea to Impact |
| Authors | Luc J.M. Smits, Sander M.J. van Kuijk, Laure Wynants |
| Cover | Bas Verhage |
| Pages | |
| DOI | 10.26481/mup.2603 – https://doi.org/10.26481/mup.2603 |
| Landing page | https://library.maastrichtuniversity.nl/resources/maastricht-university-press/catalog/improving-health-care-with-clinical-prediction-models/ |
| Online version | https://flipbooks.maastrichtuniversitypress.nl/cpm |
| https://umlib.nl/mup.2603.pdf | |
| License | CC BY – https://creativecommons.org/licenses/by/4.0/ |
| Copyright | The Authors |
| Publisher | Maastricht University Press |
| Publication place | Maastricht |
| Published online | 18-03-2026 |
| ISBN Hardcover | 9789403865706 |
| ISBN Softcover | 9789403870304 |
| Language | English |
| Subject |
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| Keywords |
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| Funding |
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How to cite
Please use the following citation, depending on your citation style:
APA
Smits, L., van Kuijk, S., & Wynants, L. (2026). Improving Health Care with Clinical Prediction Models: From Idea to Impact. Maastricht University Press. https://doi.org/10.26481/mup.2603
Harvard
Smits, L., van Kuijk, S. and Wynants, L. (2026) Improving health care with clinical prediction models: From idea to impact. Maastricht University Press. https://doi.org/10.26481/mup.2603
MLA
Smits, Luc, Sander van Kuijk, and Laure Wynants. Improving Health Care with Clinical Prediction Models: From Idea to Impact. Maastricht University Press, 2026. https://doi.org/10.26481/mup.2603.
Vancouver
Smits L, van Kuijk S, Wynants L. Improving health care with clinical prediction models: From idea to impact. Maastricht University Press; 2026. DOI: 10.26481/mup.2603.
Chicago
Smits, Luc, Sander van Kuijk, and Laure Wynants. Improving Health Care with Clinical Prediction Models: From Idea to Impact. Maastricht University Press, 2026. https://doi.org/10.26481/mup.2603.
IEEE
L. Smits, S. van Kuijk, and L. Wynants, Improving health care with clinical prediction models: From idea to impact, Maastricht University Press, Maastricht, The Netherlands, 2026. doi: 10.26481/mup.2603.
Contact
Contact the authors
The authors would welcome hearing from readers about their ideas, feedback, or experiences with this book. Please feel free to contact them at epid-cpm@maastrichtuniversity.nl
Contact Maastricht University Press
For all inquiries and comments about the book publication, please contact us via mup@maastrichtuniversity.nl
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