1004Technical and Logistical Issues in Incorporating Statistical Process Control into Healthcare-Associated Infection Surveillance Programs

Background. Statistical process control (SPC) can be an effective complement to healthcare-associated infection (HAI) surveillance. Since this use of SPC somewhat differs from its manufacturing origins, some issues warrant exploration to maximize SPC’s value in detecting outbreaks and reducing HAIs. Little empirical investigation has been conducted to develop a framework for managing signals from such a system. Methods. A team of HAI (DU) and SPC (NU) experts retrospectively applied conventional SPC methods to 10 years of de-identified surgical site infection (SSI) data from 40 community hospitals in the Duke Infection Control Outreach Network (DICON), focusing on 8 known SSI outbreaks. We met bi-weekly to review results and issues potentially important to implementing such an approach. Discussions were organized around 3 categories: detection performance and optimization, alternate methods, and logistics of managing resultant outbreak signals. Pilot investigations were conducted using empirical data and Monte Carlo analysis. Results. Shewhart and EWMA control charts detected all 8 outbreaks 0-12 months earlier than originally identified via standard surveillance. The table illustrates the detection of one outbreak 0-3 months early. Issues affecting detection included what baseline data, aggregation level (hospital, surgeon, procedure), and EWMA smoothing factor to use. The value of dual, start-up, and sequential probability ratio SPC methods was identified as worthwhile to investigate.