Preface
This book is about making better decisions in healthcare using data. Every day, healthcare teams track data: waiting times, infection rates, mortality, patient experience. These data go up and down. The challenge is knowing when and how to act.
Act too quickly, and you risk chasing noise. Wait too long, and you may miss an important signal to improve care. Take the wrong action and you risk making things worse.
Statistical Process Control (SPC) provides a practical way to navigate this uncertainty.
At its core, SPC is built on a simple but powerful idea: all processes vary, and that variation arises from two fundamentally different sources – common cause and special cause. Understanding this distinction is crucial to help us interpret data more reliably and act more effectively.
SPC brings these idea’s to life through simple visual tools – run charts and control charts. Yet despite their conceptual simplicity, applying these tools correctly can be challenging. As noted in a systematic review of SPC in healthcare:
… although SPC charts may be easy to use even for patients, clinicians, or managers without extensive SPC training, they may not be equally simple to construct correctly. To apply SPC is, paradoxically, both simple and difficult at the same time.
– Thor et al. (2007)
This book addresses that paradox. It provides a clear, practical guide to constructing and using SPC charts appropriately in healthcare, using modern software and reproducible methods.
Who this book is for?
This book is for anyone working with healthcare data who wants to use SPC in practice. While it is particularly suited to data scientists and analysts, we recognise that our readers may include, healthcare professionals, educators, students, patients and improvers. If you are involved in monitoring or improving healthcare processes, this book, as highlighted below, is for you.
What sets this book apart?
This is a hands-on, step-by-step guide to producing SPC charts correctly and confidently. What makes this book distinctive is its practical, healthcare-focused and modern approach.
Healthcare focus
Unlike general SPC texts, this book is grounded in real healthcare applications, including patient safety, clinical outcomes, and operational performance. It complements the companion book, Statistical Process Control: Elements of Improving Quality and Safety in Healthcare (Mohammed 2024), by focusing on practical implementation.Practical and applied
This is not a theoretical text. You will find: worked examples, real datasets, and step-by-step guidance. The aim is to help you understand SPC and use it confidently in practice to improve healthcare.Comprehensive coverage
From foundational principles to advanced SPC techniques, this book covers it all. It is designed to cater to both beginners and experienced data scientists, ensuring everyone finds value.Additional resources
To support your learning, we provide: R scripts, datasets, and a dedicated GitHub repository for continued learning and collaboration.
These resources are designed to help you apply what you learn and continue developing your skills.
Prerequisites
No prior knowledge of SPC is required. However, some familiarity with the R programming language will be helpful.
If you are new to R, we recommend resources such as:
Learn R Programming with Johns Hopkins University, an excellent series of video tutorials covering all aspects of R programming – everything you need (and more) for this book;
R Programming for Data Science, Roger D. Peng’s companion book to the R Programming video series;
NHS-R Community, which provides excellent free learning materials.
Why R?
Because SPC sits at the intersection of statistics, computing, and visualisation, our software of choice is R – widely regarded as the lingua franca of statistical computing.
R is free, open-source, and widely used. More importantly, it supports:
Transparency and reproducibility
Every step – from raw data to final chart – can be documented and reproduced.Automation
Routine tasks such as generating reports, dashboards, and visualisations can be automated, saving time and reducing error.
However, although we use R throughout, the principles in this book apply regardless of the software you use.
How to use this book?
New to SPC?: Start with Chapter 1 to understand variation.
Want to get started quickly?: Jump to Chapters 5 and 6 to begin creating charts.
Looking to deepen your understanding?: Later chapters cover more advanced topics and chart types.
Need code and data?: Visit the GitHub repository for additional materials and updates.
We hope this book proves valuable, and we welcome your feedback for ongoing improvement.