Prevention of Surgical Site Infection using Statistical Process Control Charts

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 DESCRIPTION (provided by applicant): Surgical site infections (SSIs) are the most common and most costly healthcare-associated infections in the US. While most hospitals have greatly improved compliance with important process measures, increased compliance has not led to decreased rates of SSI. As a result, innovative strategies to prevent SSI are greatly needed. Feedback of SSI data to surgical personnel is a cornerstone of SSI prevention, but traditional feedback methods require aggregation of measurements over time and are slow. Changes in rates of SSI, including those reflecting outbreaks, often are detected several months after the rate first changed. Statistical process control (SPC) is a branch of statistics that combines time series analysis methods with graphical presentation of data to determine whether the current variation of a process represents "common cause" natural variation or "special cause" unnatural variation due to circumstances that have not previously been inherent in the process. In other words, SPC methods help separate a true signal from "noise." The overall objective of this proposal is to determine the clinical effectiveness of SPC methods to prevent SSI. First, we will optimize SPC methods to identify trends in rates of surgical site infections and provide feedback to surgeons (Specific Aim 1). We will then determine if this approach decreases the rates of surgical site infections using a multicenter cluster randomized trial design (Specific Aim 2). This proposal will capitalize on the strengths of a unique, innovative, and previously successful collaboration between investigators in the Duke Infection Control Outreach Network (DICON) and the Healthcare Systems Engineering Institute (HSyE). This collaborative investigation will combine several programmatic strengths, including expertise in SSI, expertise in SPC theory and its application to patient safety issues, and a pre-existing and successful platform for performing multicenter trials. Our central hypothesis is that rapid identification of increases of rates of SSI through optimized SPC methods coupled with immediate feedback will lead to decreases in the rates of SSI. The proposed research is innovative because it represents a new and substantive departure from the status quo of SSI surveillance and feedback. The contribution of this study will be significant because it will lead to a new strategy to decrease rates of SSI, thereby improving the health and safety of the US population.