Taking a closer look: using statistical process control to identify patterns of improvement in a quality-improvement collaborative. Issue 6 (1st July 2010) - Timmerman, Tracey; Verrall, Tanya; Clatney, Lisa; Klomp, Helena; Teare, Gary
Abstract : Background: Published reports suggest that there is considerable variation in improvement capacity and capability among participants in quality improvement collaboratives. Generating knowledge about why these complex initiatives do or do not work in different contexts requires both qualitative and quantitative approaches. Time-series analysis using line graphs and statistical process control is a rigorous quantitative approach with relatively unexplored potential in evaluating complex quality improvement interventions. Aim: The purpose of this study was to apply and illustrate the use of line graphs and statistical process control to identify variation in improvement among practices participating in the Saskatchewan Chronic Disease Management Collaborative. Methods: The authors used line graphs and regression analysis to determine whether improvement occurred at the aggregate level, and small multiples, rational ordering and rational subgrouping to examine differences in the level and rate of improvement among practices. Results: Small multiples allowed us to observe qualitative differences in patterns of improvement among practices. Stratifying data for all subgroups on one control chart using rational subgrouping provided quantitative evidence for these differences. Stratification by administrative health region using rational ordering showed consistent differences, indicating that the health region in which the practice was located may play a role in these differences. Results from the study are being used to inform a purposive sampling strategy for interviews with participants to explore why these differences occurred.