Abstract
In this month’s Hospital Pediatrics, Liao et al share their team’s journey to improve the accuracy of their institution’s electronic health record (EHR) problem list. They presented their results as statistical process control (SPC) charts, which are a mainstay for visualization and analysis for improvers to understand processes, test hypotheses, and quickly learn their interventions’ effectiveness. Although many readers might understand that 8 consecutive points above or below the mean signifies special cause variation resulting in a centerline “shift,” there are many more special cause variation rules revealed in these charts that likely provided valuable real-time information to the improvement team. These “signals” might not be apparent to casual readers when looking at the complete data set in article form. Shewhart first introduced SPC charts to the world with the publication of Economic Control of Quality of Manufactured Product in 1931. Although control charts were initially used more broadly in industrial settings, health care providers have also recently begun to understand that the use of SPC charts is vital in improvement work.(,) Deming, often seen as the “grandfather” of quality improvement (QI), saw SPC charts as vital to understanding variation as part of his well-known Theory of Profound Knowledge, outlined in his book The New Economics for Industry Government, Education. Improvement science harnesses the scientific method in which improvers create and rapidly test hypotheses and learn from their data to determine if their hypotheses are correct. This testing is central to the Model for Improvement’s plan-do-study-act cycle. Liao et al nicely laid out their hypotheses in a key driver diagram, and they tested these hypotheses with multiple interventions. In the following paragraphs, we will walk through some of their SPC charts to demonstrate how this improvement team was gaining valuable knowledge about their hypotheses through different types of special cause variation long before they had 8 points to reveal shifts. We recommend readers have the charts from the original article (OA) available for reference. A fundamental concept in improvement science is understanding the difference between common cause and special cause variation. By understanding how to apply these concepts to your data, you will more quickly identify when a change has occurred and whether action should be taken. The authors’ SPC charts reveal examples of both common cause and special cause variation.