Early Recognition and Response to Increases in Surgical Site Infections Using Optimized Statistical Process Control Charts: The Early 2RIS Study

Overview

The purpose of this quality improvement study is to measure the effectiveness of surveillance using optimized statistical process control (SPC) methods and feedback on rates of surgical site infection (SSI) compared to traditional surveillance and feedback. The primary objective is to determine if hospital clusters randomized to receive feedback from optimized SPC surveillance methods collectively have lower rates of SSI compared to hospital clusters randomized to receiving feedback from traditional surveillance methods. Secondary objectives are 1) to estimate and compare the number of signals identified using optimized SPC methods and traditional surveillance methods; 2) to estimate and compare the time and effort required to investigate signals generated using optimized SPC methods and traditional surveillance methods; and 3) to estimate the number and proportion of false-positive signals identified using optimized SPC methods and traditional surveillance methods. The Early 2RIS study will be a prospective, multicenter cluster randomized controlled trial using stepped wedge design. The active component of the quality improvement study will be performed in 29 DICON hospitals over three years, from March 2017 through February 2020. Clusters randomized to intervention will receive feedback on increasing rates of SSI identified through optimized SPC methods. This intervention is expected to decrease the subsequent rate of SSIs by closing the feedback loop on SSI outcomes. Participating study hospitals will all be members of DICON, a network of 43 community hospitals in North Carolina, South Carolina, Georgia, Florida, and Virginia that provides community hospitals access to consultative services from infection prevention experts, data analyses and benchmarking, and educational materials designed by faculty from Duke. This study is considered part of routine quality improvement measures and a part of previously established agreements between DICON and the community hospitals. Data flow and communication are outlined in detail in approved protocols determined to be exempt research by the DUHS IRB. Briefly, existing clinical data are extracted from participating hospitals' electronic medical record into discrete files according to DICON specifications. Then a de-identification process removes direct patient identifiers into a limited dataset. The majority of data collection will occur through methods already developed and utilized by study hospitals. In brief, each hospital routinely submits limited datasets to the DICON Surgical Surveillance Database, including the following variables: hospital, type of procedure, patient identifier, date of procedure, age, sex, surgeon identifier, start/stop times, ASA score, wound class, risk index, SSI (Yes/No), date of infection, type of SSI, location at diagnosis and organism. No identifiable patient or surgeon data are transmitted to the DICON Surgical Database. Data definitions and data collection methods are standardized across DICON hospitals. Following signal adjudication, additional data will be collected in a REDCap database to document actions and rationale.