Topic
Run chart
About: Run chart is a research topic. Over the lifetime, 125 publications have been published within this topic receiving 2041 citations.
Papers published on a yearly basis
Papers
TL;DR: An analytical tool commonly used by professionals in quality improvement but underutilised in healthcare, the run chart has wide potential application in healthcare for practitioners and decision-makers.
Abstract: Background Those working in healthcare today are challenged more than ever before to quickly and efficiently learn from data to improve their services and delivery of care. There is broad agreement that healthcare professionals working on the front lines benefit greatly from the visual display of data presented in time order.
Aim To describe the run chart—an analytical tool commonly used by professionals in quality improvement but underutilised in healthcare.
Methods A standard approach to the construction, use and interpretation of run charts for healthcare applications is developed based on the statistical process control literature.
Discussion Run charts allow us to understand objectively if the changes we make to a process or system over time lead to improvements and do so with minimal mathematical complexity. This method of analyzing and reporting data is of greater value to improvement projects and teams than traditional aggregate summary statistics that ignore time order. Because of its utility and simplicity, the run chart has wide potential application in healthcare for practitioners and decision-makers. Run charts also provide the foundation for more sophisticated methods of analysis and learning such as Shewhart (control) charts and planned experimentation.
510 citations
TL;DR: In this paper, a computer program for the optimal economic design of an X control chart is presented, where the mean time between process shifts is an exponentially distributed random variable, and the program finds the sample size, control limit width and interval between samples that minimize the expected total costs per unit time.
Abstract: A computer program for the optimal economic design of an X control chart is presented. A single assignable cause system is assumed, where the mean time between process shifts is an exponentially distributed random variable. Given fixed and variable sampling costs, the costs of investigating action signals, the penalty cost of production in the out-of-control state, and other parameters describing process performance, the program finds the sample size, control limit width and interval between samples that minimize the expected total costs per unit time.
124 citations
1 Jan 1998
TL;DR: In this paper, the authors proposed a single control chart to monitor both the center and the spread for variables data, which is shown to be just as effective as the combination of X-bar chart and R chart.
Abstract: Control chart techniques have been widely used in industries to monitor a process in quality improvement. Whenever we deal with variables data, we usually employ a combination of X-bar chart and R chart (or S chart) to monitor both the center and the spread of the process. In this paper, we propose a simple alternative, that is, we design a single chart to monitor both the center and the spread for variables data. When compared with the combination of X-bar chart and Sc hart, the proposed chart is shown to be just as ef fective. An example is given to show how to use this new chart.
104 citations
TL;DR: The shift and crossings rules are effective in detecting shifts and drifts in process centre over time while keeping the false signal rate constant around 5% and independent of the number of data points in the chart.
Abstract: Background
A run chart is a line graph of a measure plotted over time with the median as a horizontal line. The main purpose of the run chart is to identify process improvement or degradation, which may be detected by statistical tests for non-random patterns in the data sequence.
Methods
We studied the sensitivity to shifts and linear drifts in simulated processes using the shift, crossings and trend rules for detecting non-random variation in run charts.
Results
The shift and crossings rules are effective in detecting shifts and drifts in process centre over time while keeping the false signal rate constant around 5% and independent of the number of data points in the chart. The trend rule is virtually useless for detection of linear drift over time, the purpose it was intended for.
78 citations
TL;DR: A specific statistic (the Standardized Infection Ratio) and specific techniques that could make control charts valuable and practical tools for infection control are suggested.
73 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 4 |
| 2020 | 4 |
| 2019 | 4 |
| 2018 | 5 |
| 2017 | 9 |
| 2016 | 4 |