Dynamic Risk-Adjusted Survival Time Monitoring for Medical Performance Surveillance

Music City Center 

Effective monitoring of medical performance is crucial for improving healthcare quality. By identifying deteriorating performance early, prospective monitoring systems enable prompt investigations and timely corrective actions, ultimately reducing complications and mortality rates. Given this importance, post-treatment outcomes, such as survival times, are typically collected over time, leading to continuous data streams. Many existing methods for monitoring survival times focus on detecting proportional increases in hazard rates, which limits their ability to identify a broader range of performance changes, including non-proportional increases and changes in the relationships between survival times and risk factors. To address this gap, we develop a dynamic risk-adjusted survival time monitoring method for medical performance surveillance. Its key feature is the use of a newly proposed dynamic Cox model, which allows both the baseline hazard and the regression coefficients to vary over time, providing an accurate representation of the temporal dynamics in medical processes. Both theoretical and numerical studies demonstrate the effectiveness of our method in practice.

Medical performance

Survival times

Monitoring

Dynamic Cox model

Risk adjustment

Healthcare quality 

Biometrics Section