Evaluating quality of hospital care using time-to-event endpoints based on patient follow-up data

Abstract

Revisions of hip and knee arthroplasty implants and cardiac pacemakers pose a large medical and economic burden for society. Consequently, the identification of health care providers with potential for quality improvements regarding the reduction of revision rates is a central aim of quality assurance in any healthcare system. Even though the time span between initial and possible subsequent operations is a classical time-to-event endpoint, hospital-specific quality indicators are in practice often measured as revisions within a fixed follow-up period and subsequently analyzed by traditional methods like proportions or logistic regression. Methods from survival analysis, in contrast, allow the inclusion of all observations, i.e. also those with early censoring or events, and make thus more efficient and more timely use of the available data than traditional methods. This may be obvious to a statistician but in an applied context with historic traditions, the introduction of more complicated methods needs a clear presentation of their added value. We demonstrate how standard survival methods like the Kaplan-Meier estimator and a multiplicative hazards model outperform traditional methods with regard to the identification of performance outliers. Following that, we use the proposed methods to analyze 640,000 hip and knee replacement operations with about 13,000 revisions between 2015 and 2016 in more than 1200 German hospitals in the annual evaluation of quality of care. Based on the results, performance outliers are identified which are to be further investigated qualitatively with regard to their provided quality of care and possible necessary measures for improvement. Survival analysis is a sound statistical framework for analyzing data in the context of quality assurance and survival methods outperform the statistical methods that are traditionally used in this area.

Benchmarking survival outcomes: A funnel plot for survival data

“…The method of Hengelbrock and Höhle 11 also works with a ratio , as does our approach, and could therefore in principle also be used as input for a funnel plot. Their expected is defined differently from ours; it is defined as , the sum over individuals in the center of the cumulative patient-specific hazards evaluated at the observed time points, assuming no center differences.…”

“…Also Hengelbrock and Höhle 11 discuss benchmarking for time-to-event outcomes in the context of revisions of hip and knee arthroplasty implants and cardiac pacemakers. They propose two types of indicators, one of which is based on the center-specific survival function, the other based on a proportional hazards model with a multiplicative center-specific effect.…”

Hospital profiling using Bayesian decision theory

“…One reason for the weak relationship between PPV and values of α and ρ could be that the validation of results classified as outliers is a relatively informal process with plenty of heterogeneity and, hence, cannot serve as a gold standard for the classification of true outliers (20). Also, it is possible that the indicators' inclusion criteria are not accurate enough to exclude cases from the indicators which are explained by special circumstances.…”

“…This naive rule has been traditionally used for classification decisions in Germany (20), which is why we compare its results to that of the decision rules described above.…”

Facility profiling under competing risks using multivariate prognostic scores: Application to kidneytransplant centers

“…Many methods exist for facility profiling based on survival data in the absence of competing risks. [1][2][3] Depending on the application at hand, the competing risks aspect of the data may be defensibly ignored. However, in many applied settings, much information is squandered by not considering the causes separately.…”