Dataset Information

Comparing Gold-standard Copayment and Coinsurance Values From Claims Processing Engines to Values Derived From Behavioral Health Claims Databases.

ABSTRACT:

Background

While researchers use patient expenditures in claims data to estimate insurance benefit features, little evidence exists to indicate whether the resulting measures are accurate.

Objective

To develop and test an algorithm for deriving copayment and coinsurance values from behavioral health claims data.

Subjects

Employer-sponsored insurance plans from 2011 to 2013 for a national managed behavioral health organization (MBHO).

Measures

Twelve benefit features, distinguishing between carve-in and carve-out, in-network and out-of-network, inpatient and outpatient, and copayment and coinsurance, were created. Measures drew from claims (claims-derived measures), and benefit feature data from a claims processing engine database (true measures).

Study design

We calculate sensitivity and specificity of the claims-derived measures' ability to accurately determine if a benefit feature was required and for plan-years requiring the benefit feature, the accuracy of the claims-derived measures. Accuracy rates using the minimum, 25th, 50th, 75th, and maximum claims value for a plan-year were compared.

Principal findings

Sensitivity (82% or higher for all but 3 benefit features) and specificity (95% or higher for all but 2 benefit features) were relatively high. Accuracy rates were highest using the 75th or maximum claims value, depending on the benefit feature, and ranged from 69% to 99% for all benefit features except for out-of-network inpatient coinsurance.

Conclusions

For most plan-years, claims-derived measures correctly identify required specialty mental health copayments and coinsurance, although the claims-derived measures' accuracy varies across benefit design features. This information should be considered when creating claims-derived benefit features to use for policy analysis.

SUBMITTER: Friedman SA

PROVIDER: S-EPMC8917070 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparing Gold-standard Copayment and Coinsurance Values From Claims Processing Engines to Values Derived From Behavioral Health Claims Databases.

Friedman Sarah A SA Xu Haiyong H Azocar Francisca F Ettner Susan L SL

Medical care 20220401 4

<h4>Background</h4>While researchers use patient expenditures in claims data to estimate insurance benefit features, little evidence exists to indicate whether the resulting measures are accurate.<h4>Objective</h4>To develop and test an algorithm for deriving copayment and coinsurance values from behavioral health claims data.<h4>Subjects</h4>Employer-sponsored insurance plans from 2011 to 2013 for a national managed behavioral health organization (MBHO).<h4>Measures</h4>Twelve benefit features, ...[more]

PMID: 35213427

Similar Datasets

Project description:BackgroundMany problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers.ResultsWe compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/.ConclusionsThe examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.

Project description:ImportanceMedicare has historically imposed higher beneficiary coinsurance for behavioral health services than for medical and surgical care but gradually introduced parity between 2009 and 2014. Although Medicare insures many people with serious mental illness (SMI), there is limited information on the impact of coinsurance parity in this population.ObjectiveTo examine the association between coinsurance parity and outpatient behavioral health care use among low-income beneficiaries with SMI.Design, setting, and participantsThis cohort study used Medicare claims data for a 50% national sample of lower-income Medicare beneficiaries from January 1, 2007, to December 31, 2016. The study sample included patients with SMI (schizophrenia, bipolar disorder, or major depressive disorder). Data analysis was performed from August 1, 2018, to July 15, 2020.ExposuresReduction in behavioral health care coinsurance from 50% to 20% between January 1, 2009, and January 1, 2014.Main outcomes and measuresTotal annual spending for outpatient behavioral health care visits and the percentage of beneficiaries with an annual outpatient behavioral health care visit overall, with a prescriber, and with a psychiatrist. A difference-in-difference approach was used to compare outcomes before and after the reduction in coinsurance for beneficiaries with and without cost-sharing decreases. Linear regression models with beneficiary fixed effects that adjusted for time-changing beneficiary- and area-level covariates were used to examine changes in outcomes.ResultsThe study included 793 275 beneficiaries with SMI in 2008; 518 893 (65.4%) were younger than 65 years (mean [SD] age, 57.6 [16.1] years), 511 265 (64.4%) were female, and 552 056 (69.6%) were White. In 2008, the adjusted percentage of beneficiaries with an outpatient behavioral health care visit was 40.7% (95% CI, 40.4%-41.0%) among those eligible for the cost-sharing reduction and 44.9% (95% CI, 44.9%-45.0%) among those with free care. The mean adjusted out-of-pocket costs for outpatient behavioral health care visits decreased from $132 (95% CI, $129-$136) in 2008 to $64 (95% CI, $61-$66) in 2016 among those with reductions in cost-sharing. The adjusted percentage of beneficiaries with behavioral health care visits increased to 42.2% (95% CI, 41.9%-42.5%) in the group with a reduction in coinsurance and to 47.2% (95% CI, 47.0%-47.3%) in the group with free care. The cost-sharing reduction was not positively associated with visits (eg, relative change of -0.76 percentage points [95% CI, -1.12 to -0.40 percentage points] in the percentage of beneficiaries with outpatient behavioral health care visits in 2016 vs 2008).Conclusions and relevanceThis cohort study found that beneficiary costs for outpatient behavioral health care decreased between 2009 and 2014. There was no association between cost-sharing reductions and changes in behavioral health care visits. Low levels of use in this high-need population suggest the need for other policy efforts to address additional barriers to behavioral health care.

Dataset Information

Comparing Gold-standard Copayment and Coinsurance Values From Claims Processing Engines to Values Derived From Behavioral Health Claims Databases.

Background

Objective

Subjects

Measures

Study design

Principal findings

Conclusions

Publications

Comparing Gold-standard Copayment and Coinsurance Values From Claims Processing Engines to Values Derived From Behavioral Health Claims Databases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets