Practical Healthcare Statistics with Examples in Python and R: A Guide for the Uninitiated

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Michael Korvink

Practical Healthcare Statistics with Examples in Python and R

A Guide for the Uninitiated

Michael Korvink

$126

Paperback

Forthcoming
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English

CRC Press
13 August 2025

Epidemiology & medical statistics; Probability & statistics

Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming.

The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis.

The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data.

These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare.

By: Michael Korvink
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9781041001416
ISBN 10: 104100141X
Pages: 228
Publication Date: 13 August 2025
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Forthcoming

1. An Overview of Healthcare Data 2. Healthcare Measures 3. Hypothesis Testing 4. Confidence Intervals 5. Regression Modeling 6. Advanced Regression Modeling 7. Measures of Disease Frequency and Association 8. Standardization 9. Time-to-event Analysis

Michael Korvink serves as Principal, Thought Leadership at Premier, Inc. and is a member of the graduate teaching faculty in the Public Health Sciences department at the University of North Carolina (UNC) at Charlotte. In his current role at Premier, Michael is responsible for collaborative research across health systems, academic institutions, and government agencies. Michael has over 20 years of experience in the healthcare and pharmaceutical industry, and publishes regularly on research methods related to quality, safety, and efficiency of care. Michael holds a Master of Arts from UNC Charlotte, is a Professional Accredited Statistician (PStat) through the American Statistical Association, and is pursuing a doctorate in public health at the Medical College of Wisconsin’s Institute for Health and Equity.