Statistical Process Control: No Hits, No Runs, No Errors?

Abstract

A novel intervention or new clinical program must achieve and sustain its operational and clinical goals. To demonstrate successfully optimizing health care value, providers and other stakeholders must longitudinally measure and report these tracked relevant associated outcomes. This includes clinicians and perioperative health services researchers who chose to participate in these process improvement and quality improvement efforts (“play in this space”). Statistical process control is a branch of statistics that combines rigorous sequential, time-based analysis methods with graphical presentation of performance and quality data. Statistical process control and its primary tool—the control chart—provide researchers and practitioners with a method of better understanding and communicating data from health care performance and quality improvement efforts. Statistical process control presents performance and quality data in a format that is typically more understandable to practicing clinicians, administrators, and health care decision makers and often more readily generates actionable insights and conclusions. Health care quality improvement is predicated on statistical process control. Undertaking, achieving, and reporting continuous quality improvement in anesthesiology, critical care, perioperative medicine, and acute and chronic pain management all fundamentally rely on applying statistical process control methods and tools. Thus, the present basic statistical tutorial focuses on the germane topic of statistical process control, including random (common) causes of variation versus assignable (special) causes of variation: Six Sigma versus Lean versus Lean Six Sigma, levels of quality management, run chart, control charts, selecting the applicable type of control chart, and analyzing a control chart. Specific attention is focused on quasi-experimental study designs, which are particularly applicable to process improvement and quality improvement efforts.

Project SPRUCE: Saving Our Planet by Reducing Carbon Emissions, a Pediatric Anesthesia Sustainability Quality Improvement Initiative

“…29 Aggregate measures are generated to analyze the data, including those derived from SPC methods originally developed by Walter Shewhart. 30,31 Shewhart’s theory of variation distinguishes random or common cause variation, which is intrinsic to the system, from nonrandom or special cause variation (SCV). We present data in Shewhart I charts, in which the upper and lower control limits were defined as 3 sigmas above and below the mean.…”

“…Use of SPC charts allows for rapid adjustments based on the ability to observe changes in real time and is appropriate for quality improvement methodology. 31 This is not considered a research study and is not making inferences about populations outside of our facility.…”

Completion of an Enhanced Recovery Program in a Pediatric Ambulatory Surgery Center: A Quality Improvement Initiative

“…22 Aggregate measures were generated to analyze, including those derived from statistical process control (SPC) methods. 30,31 Shewhart's theory of variation distinguishes random or common cause variation, which is intrinsic to the system from nonrandom or special cause variation (SCV). 30 Two types of SPC charts were used.…”

Outcomes for 41 260 pediatric surgical patients with opioid‐free anesthesia: One center's experience

“…Indeed, our We utilized SPC charts because this analytical solution provides actionable display of data over time and supports timely local decision-making. 28,62,63 No supplemental statistical analyses were performed to adjust for possible confounders, generate effect estimates, or determine impact assessment. 63,64 This is appropriate for QI methodology since we are not making inferences about causality or populations outside our facility.…”