Search

OBJECTIVE: This study examined the rates and correlates of out-of-network outpatient mental health specialty care. RESEARCH DESIGN: Using administrative data from a large insurer, we examine the frequency of out-of-network utilization, analyze demographic and clinical characteristics of individuals receiving out-of-network care, and examine the types of service provided out-of-network. RESULTS: Out-of-network outpatient mental health care was received by 15.4% of adults who used outpatient mental health services, with 11.8% of adult outpatient mental health users receiving only out-of-network care and 3.6% receiving both in-network and out-of-network care. Out-of-network users received significantly more outpatient mental health care than individuals receiving only in-network mental health care. Rates of out-of-network psychotherapy services were substantially greater than for other commonly provided mental health services. CONCLUSION: A significant number of patients covered under this insurer received their outpatient mental health care out-of-network. This is most pronounced for individuals receiving psychotherapy. Further information is needed to improve our understanding of who seeks care from out-of-network providers and why as well as the effect of such care on clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Race/ethnicity information is vital for measuring disparities across groups, and self-report is the gold standard. Many surveys assign simplified race/ethnicity based on responses to separate questions about Hispanic ethnicity and race and instruct respondents to "check all that apply." When multiple races are endorsed, standard classification methods either create a single heterogenous multiracial group, or attempt to impute the single choice that would have been selected had only one choice been allowed., Objectives: To compare 3 options for classifying race/ethnicity: (a) hierarchical, classifying Hispanics as such regardless of racial identification, and grouping together all non-Hispanic multiracial individuals; (b) a newly proposed additive model, retaining all original endorsements plus a multiracial indicator; (c) an all-combinations approach, separately categorizing every observed combination of endorsements., Research Design: Cross-sectional comparison of racial/ethnic distributions of patient experience scores; using weighted linear regression, we model patient experience by race/ethnicity using 3 classification systems., Subjects: In total, 259,763 Medicare beneficiaries age 65+ who responded to the 2017 Medicare Consumer Assessments of Healthcare Providers and Systems Survey and reported race/ethnicity., Measures: Self-reported race/ethnicity, 4 patient experience measures., Results: Additive and hierarchical models produce similar classifications for non-Hispanic single-race respondents, but differ for Hispanic and multiracial respondents. Relative to the gold standard of the all-combinations model, the additive model better captures ratings of health care experiences and response tendencies that differ by race/ethnicity than does the hierarchical model. Differences between models are smaller with more specific measures., Conclusions: Additive models of race/ethnicity may afford more useful measures of disparities in health care and other domains. Our results have particular relevance for populations with a higher prevalence of multiracial identification.

Objective: Illustrate an accessible method of bridging data from earlier surveys to the CAHPS Hospital Survey to support hospitals' internal quality improvement efforts., Data Sources/study Setting: Survey of patients with more than 300,000 annual hospitalizations in a large urban hospital. STUDY DESIGN AND DATA: Six pairs of parallel items from the CAHPS and Picker Hospital Surveys were administered to the same 734 patients. We assessed item comparability and applied bridging adjustments to convert old items to predicted scores on the new CAHPS items., Principle Findings: Differences in wording, response options, and cut points for "problem scores" yielded large differences in problem score rates between the Picker and CAHPS Hospital Surveys, requiring bridging formulas. Tetrachoric correlations for 5 of 6 pairs indicated high correspondence (r = 0.71-0.97, P < 0.001) in the underlying constructs assessed by the 2 surveys, validating the use of bridging. Bridged scores contain less information per observation than directly measured new scores, but with sufficient sample sizes they can be used to detect trends across the transition., Conclusions: Hospitals can use the methodology described here to trend their scores from a previous survey to the CAHPS Hospital Survey with sufficient precision to support ongoing quality improvement efforts. Hospitals should administer an instrument containing pairs of old and new items to enough patients to yield at least 625 completes to measure bridging parameters precisely. Where correspondence is high, old items can and should be replaced by CAHPS items. Important old items with weaker associations with new items may be retained.