Speaker
Description
Quality assessment in healthcare frequently relies on quality indicators based on follow-up data tracking patient outcomes after treatment. However, conventional cohort-based indicators require complete follow-up, which can result in substantial lag between data collection and analysis. To enable more timely yearly assessment, we propose a period-based approach, in which all data collected within a defined time period (in our case one calendar year) are evaluated jointly. This way we consider each follow-up event in exactly one annual evaluation, ensuring a clear allocation of follow-up events to a single reporting year. This design results in left-truncated and right-censored survival data, which requires the use of specific statistical methods for the analysis. If there are no relevant patient-related risk faktors, we use the Kaplan-Meier estimator. Otherwise, we use risk adjusted rates, that take truncation and censoring into account when estimating the risk of each case. If the duration between the treatment and the follow-up event is of interest, we estimate hazard ratios. Finally, we demonstrate how Bayesian models for these indicators can be used to quantify uncertainty and to identify hospital providers with poor performance.
42858802004