Med Decis Making - Performance profiling in primary care: does the choice of statistical model matter?

Tópicos

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Resumo

CKGROUND: Profiling is increasingly being used to generate input for improvement efforts in health care. For these efforts to be successful, profiles must reflect true provider performance, requiring an appropriate statistical model. Sophisticated models are available to account for the specific features of performance data, but they may be difficult to use and explain to providers.OBJECTIVE: To assess the influence of the statistical model on the performance profiles of primary care providers. Data Source. Administrative data (2006?2008) on 2.8 million members of a Dutch health insurer who were registered with 1 of 4396 general practitioners.METHODS: Profiles are constructed for 6 quality measures and 5 resource use measures, controlling for differences in case mix. Models include ordinary least squares, generalized linear models, and multilevel models. Separately for each model, providers are ranked on z scores and classified as outlier if belonging to the 10% with the worst or best performance. The impact of the model is evaluated using the weighted kappa for rankings overall, percentage agreement on outlier designation, and changes in rankings over time.RESULTS: Agreement among models was relatively high overall (kappa typically .0.85). Agreement on outlier designation was more variable and often below 80%, especially for high outliers. Rankings were more similar for processes than for outcomes and expenses. Agreement among annual rankings per model was low for all models.CONCLUSIONS: Differences among models were relatively small, but the choice of statistical model did affect the rankings. In addition, most measures appear to be driven largely by chance, regardless of the model that is used. Profilers should pay careful attention to the choice of both the statistical model and the performance measures.

Resumo Limpo

ckground profil increas use generat input improv effort health care effort success profil must reflect true provid perform requir appropri statist model sophist model avail account specif featur perform data may difficult use explain providersobject assess influenc statist model perform profil primari care provid data sourc administr data million member dutch health insur regist general practitionersmethod profil construct qualiti measur resourc use measur control differ case mix model includ ordinari least squar general linear model multilevel model separ model provid rank z score classifi outlier belong worst best perform impact model evalu use weight kappa rank overal percentag agreement outlier design chang rank timeresult agreement among model relat high overal kappa typic agreement outlier design variabl often especi high outlier rank similar process outcom expens agreement among annual rank per model low modelsconclus differ among model relat small choic statist model affect rank addit measur appear driven larg chanc regardless model use profil pay care attent choic statist model perform measur

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