A Meta-summarization of Qualitative Findings About Health Systems Performance Evaluation Models: Conceptual Problems and Comparability Limitations

Abstract

Due to the replacement of the issue of performance measurement in health policies worldwide this study identifies and analyzes the models for evaluating health systems performance. For this purpose, a systematic review of the literature on the topic “health systems performance evaluation” is done, making it compatible with a qualitative meta-synthesis of the type “meta-summarization.” It works with all databases related to the theme (PubMed, Scopus, EMBASE, PubAdm and Lilacs/Scielo). Portuguese, English, and Spanish are elected as language limit. Of the total number of articles (n = 32), 23 articles (71.8%) do not have a definition on “performance.” In those who have a definition, “performance” could be summed up in 6 central ideas. Among the most frequent subsidiary concepts that makes up the performance idea are the concepts of “efficiency” (11.9%), “quality” (9.5%) and “effectiveness” (7.1%). Six models were found in this review: “dashboard,” “balanced scorecard,” “open system model,” “PCATool,” “analyze dimension and performance indicators” and “standardized checklist and interview.” The “dashboard” was the most frequent performance evaluation model, found in 35.7% of studies. Only 25% of the reviewed studies presented the performance evaluation model applied specifically to health systems. Far from being configured as management tools useful to comprehension of health systems, these performance evaluation models have shortcomings that compromise their systemic evaluative power. This reinforces the inversion of reality in the relationship between quality and performance. Also, the performance evaluation models used try to adapt to the object, however in most them with relevant analytical problems compromising the specificities in depth of the health system under analysis. This generates inaccuracies and replaces the question about its use and their limitations to compare health systems.