Retail clinic versus office setting: do patients choose appropriate providers?

OBJECTIVE: To assess whether health plan members who used retail clinics chose that setting for minor conditions and continued to see other providers for more complex conditions. STUDY DESIGN: Retrospective analysis of claims data in a commercially insured population. METHODS: Health plan enrollment data were used to identify and describe the analysis population. Episode Treatment Groups were used to identify members with chronic conditions and to analyze reasons for retail clinic use, complexity of retail clinic visits, and care for chronic conditions in non-retail clinic settings. Logistic regression was used to study predictors of retail clinic use. RESULTS: Retail clinic users differed significantly from nonusers. The most significant predictors of retail clinic use were age, sex, and proximity to a retail clinic. Episodes of care treated in the retail clinic appeared to be less complex than similar episodes treated in other settings. Chronically ill members who used the retail clinic saw another provider for their chronic condition at rates similar to or higher than those of members who did not use the retail clinic. CONCLUSIONS: Individuals may be able to identify when conditions are minor enough to be treated in a retail clinic and serious enough to be treated by a traditional provider.

Improving the imputation of race: evaluating the benefits of stratifying by age.

Health plans and other health care institutions may use indirect methods such as geocoding and surname analysis to estimate race, ethnicity, and socioeconomic status in an effort to measure disparities in care or target specific demographics. This study investigated whether stratifying by age improved imputations of race and ethnicity made through geocoding. Self-reported race and ethnicity from Medicaid enrollment records and from a health risk assessment administered by a large employer were used to validate imputation results from both an age-stratified model and a standard model. Sensitivity, specificity, and positive predictive value were calculated. Both approaches successfully imputed race and ethnicity for whites, blacks, Asians, and Hispanics. The age-stratified approach identified more blacks than did the unstratified approach, and correctly identified more blacks and whites. The two approaches worked equally well for identifying Asians and Hispanics. Age stratification may improve the accuracy of imputation methods, and help health care organizations to better understand the demographics of the people they serve.