The Use of Statistical Process Control Charts in Hospital Epidemiology

Abstract

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.

Strategies to Prevent Healthcare-Associated Infections through Hand Hygiene

“…Hand hygiene data may be displayed on dashboards that provide the most recent or cumulative hand hygiene adherence rates compared with a target rate 249 or statistical process control charts that show data trends over time and whether changes in rates are due to specific interventions or normal variation. 250 Some automated monitoring systems have the ability to give real-time displays of unit hand hygiene adherence, providing some incentive for improvement on a shift-by-shift basis. 5.…”

Uso de diagramas de controle na vigilância epidemiológica das infecções hospitalares

“…The construction of graphs that exclude outbreak periods was based on the fact that such periods increase baseline NI occurrence rates, consequently widening endemic control limits, and thus obscuring other probable outbreaks and uncommon trends, as described by Sellick 14 (1993) and Benneyan 1,2 (1993). In the present study, the exclusion of July 2000 from NI rate calculation reduced mean incidence from 20.0 to 18.0 per thousand patient days.…”

“…These observations also reveal that significance tests have the ability to determine whether or not chance is a likely explanation for the difference in the values obtained. 1,2,14 There are many different types of variables with different probabilities of distribution, such as me asured values, counts, fractions, and rates. For each of these situations there is an appropriate graph model, usually referred to using letters np, p, c, u, X, and S. Graph type selection depends on the statistical distribution of the probabilities described by the studied variable: X and S for data with normal distribution, c and u for Poisson distribution, and np and p for binomial distribution.…”

Catheter‐related bloodstream infections in hematology

“…[112][113][114][115] One way to classify these is according to the nature and expected probability distribution of the measurement data. Determining which chart is applicable in particular situations can be dependent simply on how data are collected.…”