Adjustment for patient characteristics in satisfaction surveys

Abstract

The ideal quality indicator measures a specific aspect of the quality of health care and nothing else. Unfortunately, this is often not the case, particularly for outcome indicators, which often reflect a variety of patient characteristics that are not under the provider’s control. Take patient satisfaction surveys: we all know of grumpy patients who complain even when they receive the best care, and of patients who are thankful and uncomplaining even in the worst conditions. Comparisons of mean satisfaction scores between health care providers who do not serve the same profile of patient population may be biased. Unadjusted results of satisfaction surveys are often mistrusted by providers, particularly those who fare poorly in comparison with others, and are therefore not used to improve care. A common solution is to perform statistical adjustment of satisfaction scores for any available patient characteristics, such as age, sex, education, and health status [1–3]. For example, in this issue of the Journal, Lin et al. [4] adjust their comparison of patient satisfaction in solo and group practices for patient age, sex, education, and type of illness. The underlying model is that patient satisfaction is determined by two separate sets of causes: the health care provider, and patient characteristics, which act as confounders (Figure 1). Statistical adjustment removes the influence of these confounders, and produces a presumably purer and more trustworthy measure of patient satisfaction. Figure 1 Simple model for case-mix adjustment of satisfaction scores. Satisfaction is influenced by two separate groups …