In programs monitoring surgical quality, risk-adjusted control charts have been used widely to detect changes in surgical performance. Ignoring the survival time may lead to information loss and thus attenuate the monitoring efficiency. However, previous methods based on survival time information only focus on detecting the change in average risk. The stability of surgical performance measured by scale parameters could be of interest in monitoring. In this study, we extend the risk-adjusted monitoring approach to include survival outcomes, in which both the location and scale parameters are monitored simultaneously. Based on the weighted score test for the Cox model, we propose to use an exponentially weighted moving average chart to monitor changes in average surgical risk and the existence of its variance, which could be of interest in practical surgical monitoring programs. Simulation results indicate that the proposed method detects changes in the variance of surgical performance and small shifts in surgical risk more efficiently than existing cumulative sum methods. In addition, the proposed method shows good efficiency for various magnitudes of shift. The proposed chart was applied to a data set from the Surgical Outcome Monitoring and Improvement Program in Hong Kong, identifying an improvement in a hospital's outcomes.