National surgical quality improvement (QI) programs, such as the Veterans Affairs (VA) Surgical Quality Improvement Program (VASQIP) and the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP), collect, analyze, and provide hospitals with institution-level, risk-adjusted data on perioperative morbidity and mortality in an episodic (e.g., quarterly) fashion. Hospitals in turn use these data to inform their local QI efforts. While this episodic approach to feedback is a convention in nearly all national surgical QI programs, it is associated with two important limitations: 1) a time lag between when hospital performance is declining and when notification is provided at the end of the quarterly data ascertainment period; 2) a limited ability to detect potentially meaningful clusters of adverse events that may go unrecognized with episodic evaluation strategies. One potential alternative analytic strategy that could supplement and enhance current approaches to surgical QI is the cumulative sum (CUSUM). The CUSUM is a statistical process control method that has been utilized in the industrial setting to provide real-time monitoring of the quality of production processes. In health care, the CUSUM has been used to ascertain when outcome variation is approaching an unacceptable level and is well-suited for detecting relatively small, yet persistent, changes in a given outcome over time. It is currently used by the United Network for Organ Sharing Membership and Profession Standard Committee, a regulatory body for transplant surgery in the United States, for monitoring hospital-level post-transplant patient and graft long-term survival in real-time. In addition, our group’s ongoing work has shown the CUSUM consistently provides early, meaningful, performance-based institutional feedback regarding perioperative outcomes. The overall goal of this proposal is to build upon our prior work by exploring how to optimize the CUSUM for use within national surgical QI programs to enable early and accurate detection of hospitals with poor performance. Using national VASQIP data, our goals are to: 1) evaluate whether multiple CUSUM signals in a given quarter decreases false positive hospital detection; 2) explore whether a continuously running CUSUM (over multiple quarters) decreases false positive hospital detection; 3) describe hospital factors associated with false positive and false negative CUSUM signaling. This work will help to inform a future prospective evaluation of the CUSUM and eventual implementation withing VASQIP. Leveraging VASQIP’s existing infrastructure to provide institutions with more real-time, actionable data could ultimately result in the detection of previously unappreciated suboptimal care processes and save patients from preventable morbidity and/or mortality.