Advanced Statistics: Applying Statistical Process Control Techniques to Emergency Medicine: A Primer for Providers

Callahan, Charles D.; Griffen, David

doi:10.1111/j.1553-2712.2003.tb00634.x

Cited by 15 publications

(19 citation statements)

References 13 publications

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“…Finally, control charts are well suited for reporting to non-statistical end-users due to their ease of design and ease of interpretation. They have been designed so that they can be easily plotted using SPC software applications or spreadsheet programs installed on most computers, 5 and the visual nature of the control chart lends itself to easy interpretation, while also serving as a simple and straightforward communication method.…”

Section: Discussionmentioning

confidence: 99%

“…The UCL and LCL represent three standard deviations (SDs) around the spread of data (the mean), and define the range of natural variation in performance expected when the process is in "statistical control". The three SD limits, termed "three-sigma control limits" in SPC jargon, are based on statistical theory which dictates that 99.73% of all data points are expected to fall within this range when a process is stable and unchanged, 5,6 and the performance of the process normally distributed. Any values falling within these control limits are therefore likely to represent common cause variation.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…In accordance with other statistical tests for special cause variation recommended in the literature, 5,7,8 the following four tests were selected when the control charts for this study were constructed in MINITAB: one point more than three SDs from the centre line; a run of seven or more consecutive points on one side of the centre line; seven or more consecutive points in a continual ascending or descending trend; and 14 consecutive points in a repeated up and down pattern. In MINITAB, labels on the control chart indicate which test was violated for each value where special cause variation is detected.…”

Section: Statistical Analysesmentioning

confidence: 99%

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Statistical process control part 2: using statistical process control to measure process improvement in patient flow

Chetter

2009

Aust. Health Review

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THE FIRST ARTICLE in this series, ?Statistical process control part 1: a primer for using statistical process control in health care process improvement? 1 (in this issue of the Journal), introduced the basic concepts of statistical process control (SPC) and its main tool, the control chart. While this set of techniques was originally developed in the manufacturing sector, there is growing realisation in recent years that SPC (and also other quality improvement techniques, such as Six Sigma and lean thinking) can be successfully applied to health care quality improvement.2 The reason for this is that SPC is a potent and powerful, yet simple tool for tracking, and detecting any variation in, process performance over time; which creates the opportunity for health professionals to promptly respond to any improvement or deterioration in the process. Perhaps the most valuable feature of SPC techniques however, is the ability to place a change in the outcome of a process in close temporal proximity to the redesign and improvement of the process. This means SPC can reliably evaluate the effectiveness of quality improvement initiatives implemented at the front line of health service delivery, despite the complexities of the hospital system and the challenges this often poses for health services research (for example, the inability to use robust research designs). The purpose of this companion article is to therefore demonstrate the practical application of SPC in a health care organisation. Specifically, the technique of control charting was used to track the impact of patient flow process improvement interventions in a public hospital, in the hope that this will exemplify to health care professionals the value and simplicity in applying SPC as part of their daily work.

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Section: Discussionmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

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Statistical process control part 2: using statistical process control to measure process improvement in patient flow

Chetter

2009

Aust. Health Review

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“…Statistical process control provides both a means of visualization and a scientific solution to the problem of process management, and it has been successfully applied in emergency medicine, occupational medicine, infection control, surgery and public health surveillance [6][7][8][9][10]. It is also used for quality assurance [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Statistical process control as a tool for controlling operating room performance: retrospective analysis and benchmarking

Chen

Chang

et al. 2010

Evaluation Clinical Practice

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“…Patients are surveyed via mail after their visits and asked to rate aspects of their ED care. Multiple variables potentially affect the satisfaction of a patient 2,5–14 and a high level of variation in patient satisfaction scores for an ED is not surprising 15 . Furthermore, when sample sizes are small (i.e., few surveys are returned), it is often difficult to determine when an individual physician is an outlier in patient satisfaction or within expected variation.…”

mentioning

confidence: 99%

Application of Statistical Process Control to Physician‐specific Emergency Department Patient Satisfaction Scores: A Novel Use of the Funnel Plot

Griffen

Callahan

Markwell

et al. 2012

Academic Emergency Medicine

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Objectives: Emergency department (ED) patient satisfaction remains a high priority for many hospitals. Patient surveys are a common tool for measuring patient satisfaction, and process improvement efforts are aimed at improving patient satisfaction scores. In some institutions, patient satisfaction scores can be calculated for each emergency physician (EP). ED leaders are faced with the task of interpreting individual as well as group physician scores to identify opportunities for improvement. Analysis of these data can be challenging because of the relatively small numbers of returned surveys assignable to a single physician, variable numbers of surveys returned for each physician and high standard deviations (SDs) for individual physician scores. The objective was to apply statistical process control methodology to analyze individual as well as group physician patient satisfaction scores. The novel use of funnel plots to interpret individual physician patient satisfaction scores, track individual physician scores over two successive 8-month periods, and monitor physician group performance is demonstrated.Methods: Patient satisfaction with physicians was measured using Press Ganey surveys for a 65,000-volume ED over two successive 8-month periods. Using funnel plots, individual physician patient satisfaction scores were plotted against the number of surveys completed for each physician for each 8-month period. Ninety-fifth and 99th percentile control limits were displayed on the funnel plots to illustrate individual physician patient satisfaction scores that are within, versus those that are outside of, expected random variation. Control limits were calculated using mean patient satisfaction scores and SDs for the entire group of physicians. Additional funnel plots were constructed to demonstrate changes in individual physicians' patient satisfaction scores as a function of increasing numbers of returned surveys and to illustrate changes in the group's patient satisfaction scores between the first and second 8-month intervals after the institution of process improvement efforts aimed at improving patient satisfaction.Results: For the first 8-month period, 34,632 patients were evaluated in and discharged from the ED, with 581 surveys returned for 21 physicians. The mean (±SD) overall group physician patient satisfaction score was 81.8 (±24.7). Returned surveys per physician ranged from 2 to 58. For the second period, 34,858 patients were evaluated and discharged from the ED, with 670 patient satisfaction surveys returned for 20 physicians. The mean (±SD) overall physician score for all surveys returned during the second period was 85.0 (±22.2). Returned surveys per physician ranged from 8 to 65. Conclusions:The application of statistical control methodology using funnel plots as a means of analyzing ED group and physician patient satisfaction scores was possible. The authors believe that using funnel plots to analyze scores graphically can rapidly help determine the significance of individual physician p...

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Advanced Statistics: Applying Statistical Process Control Techniques to Emergency Medicine: A Primer for Providers

Cited by 15 publications

References 13 publications

Statistical process control part 2: using statistical process control to measure process improvement in patient flow

Statistical process control part 2: using statistical process control to measure process improvement in patient flow

Statistical process control as a tool for controlling operating room performance: retrospective analysis and benchmarking

Application of Statistical Process Control to Physician‐specific Emergency Department Patient Satisfaction Scores: A Novel Use of the Funnel Plot

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