Use of surgical-site infection rates to rank hospital performance across several types of surgery.

BACKGROUND: Comparing and ranking hospitals based on health outcomes is becoming increasingly popular, although case-mix differences between hospitals and random variation are known to distort interpretation. The aim of this study was to explore whether surgical-site infection (SSI) rates are suitable for comparing hospitals, taking into account case-mix differences and random variation. METHODS: Data from the national surveillance network in the Netherlands, on the eight most frequently registered types of surgery for the year 2009, were used to calculate SSI rates. The variation in SSI rate between hospitals was estimated with multivariable fixed- and random-effects logistic regression models to account for random variation and case mix. 'Rankability' (as the reliability of ranking) of the SSI rates was calculated by relating within-hospital variation to between-hospital variation. RESULTS: Thirty-four hospitals reported on 13 629 patients, with overall SSI rates per surgical procedure varying between 0 and 15·1 per cent. Statistically significant differences in SSI rate between hospitals were found for colonic resection, caesarean section and for all operations combined. Rankability was 80 per cent for colonic resection but 0 per cent for caesarean section. Rankability was 8 per cent in all operations combined, as the differences in SSI rates were explained mainly by case mix. CONCLUSION: When comparing SSI rates in all operations, differences between hospitals were explained by case mix. For individual types of surgery, case mix varied less between hospitals, and differences were explained largely by random variation. Although SSI rates may be used for monitoring quality improvement within hospitals, they should not be used for ranking hospitals.

Displaying random variation in comparing hospital performance.

INTRODUCTION: The role of transparency in quality of care is becoming ever more important. Various indicators are used to assess hospital performance. Judging hospitals using rank order takes no account of disturbing factors such as random variation and case-mix differences. The purpose of this article is to compare displays for the influence of random variation on the apparent differences in the quality of care between the Dutch hospitals. METHOD: The authors analysed the official 2005 data of all 97 hospitals on the following performance indicators: pressure ulcer, cerebro-vascular accident and acute myocardial infarction. The authors calculated CIs of the point estimate and the simulated CIs of the ranks with bootstrap sampling, and visualised the influence of random variation with three modern graphical techniques: forest plot, funnel plot and rank plot. RESULTS: Statistically significant differences between hospitals were found for nearly all performance indicators (p<0.001). However, the CIs in the forest plot revealed that only a small number of hospitals performed significantly better or worse. The funnel plot provides a representation of differences between hospitals compared with a target value and allows for the uncertainty of these differences. The rank plot showed that ranking hospitals was very uncertain. CONCLUSION: Despite statistically significant differences between hospitals, random variation is a crucial factor that must be taken into account when judging individual hospitals. The funnel plot provides easily interpretable information on hospital performance, including the influence of random variation.

Nasal provocation and passive anterior rhinomanometry (PAR).

A rapid and convenient method of nasal provocation (aerosol provocation combined with passive anterior rhinomanometry) is described. A comparative study between skin tests, bronchial provocations and nasal provocations highlights the usefulness of this method.