Introduction: In 2011, some 3800 Rural Health Clinics (RHCs) delivered primary care in underserved rural areas throughout the USA. To date, little research has been conducted to identify the variability in RHC performance. In an effort to address the knowledge gaps, a national, longitudinal study was conducted of a panel of 3565 RHCs. The goals of the study were to determine: (1) the relationship between two aspects of performance: efficiency and effectiveness; and (2) the factors that influence variation in RHC performance.
Methods: A non-experimental study of RHC performance was conducted using 2 years of secondary data from multiple sources. A study panel of RHCs was formed. This panel was composed of all RHCs continuously in operation during the period 2006-2007. The study panel was divided into two subsets - one for the provider-based clinics; another for the independent clinics. The individual RHC was the unit of analysis throughout the study. Descriptive statistics were calculated for each subset. Bivariate analyses was conducted of the relationships between the clinic characteristics and the performance outcome measures, as well as the interrelationships between various clinic characteristics using χ², t-tests, Cramer's V, Pearson correlation, and Spearman correlation statistics. Next, using covariance structure analysis, the interrelationships were examined among the context (community or demographic factors), design (organizational structure and other mediating factors), and performance (efficiency and effectiveness) of RHCs. Three hypotheses were tested: (1) the effectiveness of RHCs is positively influenced by efficiency; (2) there is a reciprocal relationship between RHC efficiency and effectiveness; and (3) large RHCs are more efficient than small RHCs.
Results: To test the hypotheses that effectiveness of RHCs is positively influenced by efficiency and that there is a reciprocal relationship between efficiency and effectiveness, two covariance structure models were developed and revised: one for independent and one for provider-based RHCs. However, the revised models were not supported by the data. To test the hypothesis that large RHCs are more efficient than small ones, two additional efficiency-based structural equation models were constructed (one for independent RHCs and another for provider-based RHCs). Both of these models were supported by the data (independent model: χ² = 13.8, df = 8, p = 0.088, relative χ² = 1.723, adjusted goodness of fit index [AGFI] = .981, root mean square error of approximation [RMSEA] = .034; provider-based model: χ² = 19.011, df = 8, p = 0.015, relative χ² = 2.376, AGFI = .978, RMSEA = .043).
Conclusion: This study examined the relationship between efficiency and effectiveness of RHCs. In addition, it identified several factors that influence the variation in RHC performance. The study has implications for optimizing RHC performance, providing quality services to rural populations, and enhancing the value of RHC data. The present is a critical time in the history of RHCs as they transition to meet the goals and expectations of the US health system reform. Additional research is needed to quantify and trend RHCs' contribution to the rural health delivery system in order to optimize their service to rural populations.