Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non-Random Variation in Health Care Processes

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.

Contributors

Robert K. Hills: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2014

Publication date (Electronic): 25 November 2014

Volume: 9

Issue: 11

Affiliations

[1 ]Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

[2 ]University of Aalborg, Danish Center for Healthcare Improvements, Department of Business and Management, Aalborg, Denmark

Cardiff University, United Kingdom

Author notes

* E-mail: jacob.anhoej@ 123456regionh.dk

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: JA AVO. Performed the experiments: JA AVO. Analyzed the data: JA AVO. Contributed reagents/materials/analysis tools: JA AVO. Wrote the paper: JA AVO.

Article

Publisher ID: PONE-D-14-08548

DOI: 10.1371/journal.pone.0113825

PMC ID: 4244133

PubMed ID: 25423037

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Page count

Pages: 13

Funding

The authors have no support or funding to report.

Categories

Subject: Research Article

Subject: Computer and Information Sciences

Subject: Computerized Simulations

Subject: Engineering and Technology

Subject: Production Engineering

Subject: Production Control

Subject: Medicine and Health Sciences

Subject: Epidemiology

Subject: Epidemiological Methods and Statistics

Subject: Epidemiological Statistics

Subject: Health Care

Subject: Health Care Quality

Subject: Health Services Research

Subject: Quality of Care

Subject: Physical Sciences

Subject: Mathematics

Subject: Statistics (Mathematics)

Subject: Statistical Methods

Subject: Statistical Hypothesis Testing

Subject: Statistical Inference

Subject: Statistical Theories

Subject: Research and Analysis Methods

Subject: Mathematical and Statistical Techniques