title: Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects.
abstract: To measure the effect of quality improvement interventions, it is appropriate to use analysis methods that measure data over time. Examples of such methods include statistical process control analysis and interrupted time series with segmented regression analysis. This article compares the use of statistical process control analysis and interrupted time series with segmented regression analysis for evaluating the longitudinal effects of quality improvement interventions, using an example study on an evaluation of a computerized decision support system.
authors.affiliation: School of Health Sciences, University of Borås, Borås, Sweden (Dr Andersson Hagiwara); School of Health Sciences, Jönköping University, Jönköping, Sweden (Dr Andersson Gäre); and Division of Quality Technology and Management, Linköping University, Linköping, Sweden (Dr Elg).
authors.forename: Magnus
authors.initials: M
authors.lastname: Andersson Hagiwara
authors.forename: Boel
authors.initials: B
authors.lastname: Andersson Gäre
authors.forename: Mattias
authors.initials: M
authors.lastname: Elg
mesh_terms: D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D065186:Interrupted Time Series Analysis; D008297:Male; D058996:Quality Improvement; D012044:Regression Analysis