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The Use of Statistical Process Control Charts in Hospital Epidemiology

Published online by Cambridge University Press:  21 June 2016

John A. Sellick Jr.*
Affiliation:
Division of Infectious Diseases, State University of New York at Buffalo and Buffalo General Hospital, Buffalo, New York
*
Department of Medicine, Buffalo General Hospital, 100 High St., Buffalo, NY 14203

Extract

Hospital epidemiologists rely on sound scientific and analytical principles in the conduct of surveillance, studies, investigations, etc. The demonstration of differences in occurrence of events (eg, nosocomial infections) in different time periods generally has used traditional hypothesis testing statistical models. However, repetitive hypothesis testing is impractical for frequent analysis of accumulating data, especially when there is no apparent outbreak. Clearly, a statistical procedure that simplifies hypothesis testing to detect acute variations in certain occurrences is desirable. Ongoing analysis of trends also would be desirable.

Many United States hospitals have espoused continuous quality improvement (CQI) or similarly identified programs as a means to improve both patient care and operating efficiency. With this trend, the scientific method has become interjected into all aspects of patient management, and no longer is confined to formal research studies. Many scientific quality management techniques that long have been used in industrial settings have been applied to healthcare settings. Noteworthy is the use of statistical methods to describe the variation in processes, which is known as statistical process control (SPC). The term statistical quality control (SQC) often is used interchangeably, but some authors refer to SQC only when statistical methods also are used to improve a process. This article will describe the basic theory and simple application of SPC in hospital epidemiology.

Type
Beyond Infection Control: The New Hospital Epidemiology
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1993

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