, Practical Healthcare Statistics
with Examples in Python and R
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 effi-
ciency 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 tech-
nique 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 experi-
ence. 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 practi-
cal resource for analysts, data scientists, health researchers, and others look-
ing to make informed, data-driven decisions in healthcare.
Michael Korvink serves as Principal, Research and Innovation 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 agen-
cies. Michael has over 20 years of experience in the healthcare and phar-
maceutical 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 Humanity.
,
, Practical Healthcare
Statistics with Examples
in Python and R
A Guide for the Uninitiated
Michael Korvink
with Examples in Python and R
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 effi-
ciency 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 tech-
nique 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 experi-
ence. 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 practi-
cal resource for analysts, data scientists, health researchers, and others look-
ing to make informed, data-driven decisions in healthcare.
Michael Korvink serves as Principal, Research and Innovation 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 agen-
cies. Michael has over 20 years of experience in the healthcare and phar-
maceutical 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 Humanity.
,
, Practical Healthcare
Statistics with Examples
in Python and R
A Guide for the Uninitiated
Michael Korvink
