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Cross-country comparisons of costs and quality between hospitals are often made at the macro level. The goal of this study was to explore methods to compare micro-level data from hospitals in different health care systems. To do so, we developed a multi-level framework in combination with a propensity score matching technique using similarly structured data for patients receiving treatment for acute myocardial infarction in German and US Veterans Health Administration hospitals. Our case study shows important differences in results between multi-level regressions based on matched and unmatched samples. We conclude that propensity score matching techniques are an appropriate way to deal with the usual baseline imbalances across the samples from different countries. Multi-level models are recommendable to consider the clustered structure of the data when patient-level data from different hospitals and health care systems are compared. The results provide an important justification for exploring new ways in performing health system comparisons.
Schreyögg et al. (Mon,) studied this question.