In healthcare provider profiling, accurately assessing hospital performance is crucial for informed decision-making and quality improvement. Traditional approaches rely heavily on parametric regression models for risk adjustment, but these methods often fail to account for between-center heterogeneity and may produce biased estimates, especially in the presence of low event rates or small...
Although not without controversy, readmission is entrenched as a hospital quality metric with statistical analyses generally based on fitting a logistic-Normal generalized linear mixed model. Such analyses, however, ignore death as a competing risk, although doing so for clinical conditions with
high mortality can have profound effects; a hospital’s seemingly good performance for readmission...
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...
Improved predictions of quality of care indicators in the tail of its distribution
Els Goetghebeur, Ghent University
Standard mixed methods have been popular for evaluating performance across care centers in terms of indicators that summarize residents’ outcomes. Their results tend to lack power, however, for the detection of poor performance [1]. This stems from regression to the...
Background: There is an increasing interest in making use of patient reported outcome measures for provider comparisons, However, guidance on the choice of outcomes and selection of variables for case-mix adjustment for specific patient groups is lacking.
Material: In the ACRF-pred study 973 patients from 19 different clinics were followed after arthroscopic rotator cuff repair for 24...
