Continuous quality improvement efforts have become a central focus of leading health care organizations.The transplant community has been a pioneer in periodic review of clinical outcomes to ensure the optimal use of limited donor organs. Through data collected from the Organ Procurement and Transplantation Network (OPTN) and analyzed by the Scientific Registry of Transplant Recipients (SRTR), transplantation professionals have intermittent access to specific, accurate and clinically relevant data that provides information to improve transplantation. Statistical process control techniques, including cumulative sum charts (CUSUM), are designed to provide continuous, realtime assessment of clinical outcomes. Through the use of currently collected data, CUSUMs can be constructed that provide risk-adjusted program-specific data to inform quality improvement programs. When retrospectively compared to currently available data reporting, the CUSUM method was found to detect clinically significant changes in center performance more rapidly, which has the potential to inform center leadership and enhance quality improvement efforts.

In order to monitor a medical center's survival outcomes using simple plots, we introduce a risk-adjusted Observed-Expected (O-E) Cumulative SUM (CUSUM) along with monitoring bands as decision criterion.The proposed monitoring bands can be used in place of a more traditional but complicated V-shaped mask or the simultaneous use of two one-sided CUSUMs. The resulting plot is designed to simultaneously monitor for failure time outcomes that are "worse than expected" or "better than expected." The slopes of the O-E CUSUM provide direct estimates of the relative risk (as compared to a standard or expected failure rate) for the data being monitored. Appropriate rejection regions are obtained by controlling the false alarm rate (type I error) over a period of given length. Simulation studies are conducted to illustrate the performance of the proposed method. A case study is carried out for 58 liver transplant centers. The use of CUSUM methods for quality improvement is stressed.

Standardized mortality ratios (SMRs) reported by Medicare compare mortality at individual dialysis facilities with the national average, and are currently adjusted for race. However, whether the adjustment for race obscures or clarifies disparities in quality of care for minority groups is unknown. Cox model-based SMRs were computed with and without adjustment for patient race for 5920 facilities in the United States during 2010. The study population included virtually all patients treated with dialysis during this period. Without race adjustment, facilities with higher proportions of black patients had better survival outcomes; facilities with the highest percentage of black patients (top 10%) had overall mortality rates approximately 7% lower than expected. After adjusting for within-facility racial differences, facilities with higher proportions of black patients had poorer survival outcomes among black and non-black patients; facilities with the highest percentage of black patients (top 10%) had mortality rates approximately 6% worse than expected. In conclusion, accounting for within-facility racial differences in the computation of SMR helps to clarify disparities in quality of health care among patients with ESRD. The adjustment that accommodates within-facility comparisons is key, because it could also clarify relationships between patient characteristics and health care provider outcomes in other settings.

We develop a Weighted CUmulative SUM (WCUSUM) to evaluate and monitor pre-transplant waitlist mortality of facilities in the context where transplantation is considered to be dependent censoring. Waitlist patients are evaluated multiple times in order to update their current medical condition as reflected in a time dependent variable called the Model for End-Stage Liver Disease (MELD) score. Higher MELD scores are indicative of higher pre-transplant death risk. Moreover, under the current liver allocation system, patients with higher MELD scores receive higher priority for liver transplantation. To evaluate the waitlist mortality of transplant centers, it is important to take this dependent censoring into consideration. We assume a ‘standard’ transplant practice through a transplant model and utilize Inverse Probability Censoring Weights (IPCW) to construct a weighted CUSUM. We evaluate the properties of a weighted zero-mean process as the basis of the proposed weighted CUSUM. We then discuss a resampling technique to obtain control limits. The proposed WCUSUM is illustrated through the analysis of national transplant registry data.