Project Summary/Abstract Contemporary surgical quality improvement (QI) programs provide hospitals with episodic feedback. There are two main limitations associated with this approach. The first is the time required for data abstraction, analysis, and subsequent feedback creating a built-in time lag between when a hospital is having a decrement in performance and when the hospital receives this information. The second is the inability of current episodic analytic methods to explicitly account for the impact of time as variable (i.e.: the occurrence of a cluster of events in a short period of time). Episodic data put hospitals' local QI teams at a disadvantage because their response to declining performance must be reactive rather than proactive. Furthermore, patients treated in the interval between when performance is declining and when this decrement is appreciated are potentially at risk for exposure to suboptimal perioperative care processes. The cumulative sum (CUSUM) is an industrial statistical process control method traditionally used for monitoring the quality of a production process and for ensuring that process is operating efficiently, effectively, and at full potential. The CUSUM is well-suited for ascertaining variation over time for rare outcomes, is resilient to multiple statistical testing, and explicitly accounts for the impact of time as a variable. It has been used for health care evaluation and shown to provide early, meaningful, performance-based feedback. The objective of this proposal is to compare the effectiveness of a risk-adjusted, time-to- event CUSUM for the early detection of hospitals with outlier performance in terms of 30-day morbidity and mortality relative to the standard of episodic observed-to-expected (O-E) methodology used by the VA Surgical Quality Improvement Program (VASQIP) and the American College of Surgeons National Surgical Quality Improvement Program. Using national VASQIP data, we intend to: 1.) evaluate whether the CUSUM identifies similar outlier hospitals relative to the O-E approach and to compare differences in timing for outlier identification; 2.) estimate the number of at risk surgical procedures and post-operative patient days using CUSUM compared to O-E hospital performance evaluation. The data from this proposal will inform a future prospective evaluation of the CUSUM within the VA's existing surgical QI framework and infrastructure with the ultimate goal being eventual integration of CUSUM into VASQIP reporting. Providing hospitals with more timely data would benefit local QI efforts and could shift the paradigm away from a reactive response to episodic data to a proactive approach based on a more real-time evaluation of performance. Identifying opportunities to correct errant care processes when performance is declining rather than when it has already reached an unacceptable level could decrease the number of surgical patients at risk for unintended outcomes, decrease the costs associated with correcting errant care processes and treating potentially preventable morbidity, and make surgical care safer.