Statistical Methods for Hospital Monitoring with R (Statistics in Practice) - Hardcover

Morton, Anthony; Mengersen, Kerrie L.; Playford, Geoffrey; Whitby, Michael

Hardcover

ISBN 10: 1118596307 ISBN 13: 9781118596302

Publisher: Wiley, 2013

Synopsis

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance.  This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. 

This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.

This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest

Statistical Methods for Hospital Monitoring with R:

• Provides functions to perform quality improvement and infection management data analysis.
• Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events.
• Provides a summary of key non-statistical aspects of hospital safety and easy to use functions.
• Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring
• Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.

About this title

About the Author

Anthony Morton and Geoffrey Playford, Princess Alexandra Hospital, Brisbane, Australia

Kerrie Mengersen, Science and Engineering Faculty, Queensland University of Technology, Australia

Michael Whitby, Greenslopes Specialist Centre, Queensland, Australia

From the Back Cover

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software.

This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.

This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest

Statistical Methods for Hospital Monitoring with R:

• Provides functions to perform quality improvement and infection management data analysis.
• Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events.
• Provides a summary of key non-statistical aspects of hospital safety and easy to use functions.
• Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring
• Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.

From the Inside Flap

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance.  This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. 

This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.

This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest

Statistical Methods for Hospital Monitoring with R:

• Provides functions to perform quality improvement and infection management data analysis.
• Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events.
• Provides a summary of key non-statistical aspects of hospital safety and easy to use functions.
• Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring
• Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.

Edition details

• Publisher: Wiley
• Publication date: 2013
• Language: English
• ISBN 10: 1118596307
• ISBN 13: 9781118596302
• Binding: Hardcover
• Edition number: 1
• Number of pages: 432