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Comparing different methods of indexing commercial health care prices.

ABSTRACT: OBJECTIVE:To compare different methods of indexing health care service prices for the commercially insured population across geographic markets. DATA SOURCES:Health Care Cost Institute commercial claims data from 2012 to 2016. STUDY DESIGN:We compare price indices computed using methods with differing levels of computational intensity: weighted-average versus regression-based methods. We separately compute indices of the prices paid for set of common inpatient and set of common outpatient services in different markets across the United States using each type of method. We subsequently examined the variation of and correlations between the resulting index values. DATA COLLECTION/EXTRACTION METHODS:We computed health care service price indices separately using samples of inpatient and outpatient facility claims from 2012 to 2016 across 112 Core-Based Statistical Areas. Within each category of services, claims were limited to members under the age of 65 with employer-sponsored insurance. Both samples were limited to a common set of services that made up nearly 80 percent of the service use in the full sample every year. PRINCIPAL FINDINGS:We found that the methods studied produced highly correlated price indices (r > .94) with similar distributions across years for both inpatient and outpatient services. CONCLUSIONS:Our findings suggest that weighted-average methods, which are much less computationally intensive, will generate results similar to regression-based methods.

SUBMITTER: Johnson WC

PROVIDER: S-EPMC6980960 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Comparing different methods of indexing commercial health care prices.

Johnson William C WC Kennedy Kevin K

Health services research 20191125 1

<h4>Objective</h4>To compare different methods of indexing health care service prices for the commercially insured population across geographic markets.<h4>Data sources</h4>Health Care Cost Institute commercial claims data from 2012 to 2016.<h4>Study design</h4>We compare price indices computed using methods with differing levels of computational intensity: weighted-average versus regression-based methods. We separately compute indices of the prices paid for set of common inpatient and set of co ...[more]

PMID: 31763686

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Project description:ImportanceVarious studies have documented the rise in commercial insurance prices during the past 2 decades; however, estimates on the association of rising costs with health systems' financial health are lacking. This study calculated 2 measures from standardized Audited Financial Statements (AFSs)-operating margins and days of unrestricted cash on hand-to explore the associations.ObjectiveTo estimate the association between health systems' financial condition and the ratio of commercial to Medicare relative prices.Design, setting, and participantsThis cross-sectional analysis combined standardized 2018 AFSs from a large sample of US health systems with publicly available relative price data to assess the association between their financial outcomes and commercial-to-Medicare relative inpatient prices. The 2018 AFSs were collected and standardized from a convenience sample of multihospital health systems and single hospitals that were included in round 4 of the RAND Hospital Price Transparency Study. Cross-sectional, multivariate regression models were estimated, controlling for payer mix and other system characteristics, and models were weighted by health systems' 2018 adjusted admissions. The analyses were conducted July 2021 through November 2022.ExposuresThe commercial-to-Medicare relative price for inpatient services (2018-2020 pooled average), which represents the average amount paid by commercial plans as a percentage of what Medicare would have paid to the same health system for the same services.Main outcomes and measuresOperating margins and days cash on hand, which capture complementary aspects of financial performance (profitability and liquidity).ResultsThe study sample included 156 health systems in the US, representing diverse geography, size, and ownership type. Mean (SD) days cash on hand were 180.1 (113.3) and operating margins were 3.3% (3.6%) in 2018. Overall, a 1-unit increase in the commercial-to-Medicare relative price ratio was associated with a 21.3% (95% CI, 21.3% to 21.4%; P < .001) increase in days cash on hand and a 2.7 (95% CI, 2.7 to 2.7; P < .001) percentage point increase in average operating margins. Higher Medicaid payer mix share was associated with fewer days cash on hand (-3.3%; 95% CI, -3.3% to -3.3%; P < .001) and lower operating margins (-0.081; 95% CI, -0.082 to -0.081; P < .001).Conclusions and relevanceThis cross-sectional study of health system financial data found that higher commercial-to-Medicare relative prices and a lower Medicaid payer share were associated with higher profits and more days cash on hand. These findings provide evidence against the claim that relatively higher commercial prices are primarily used to offset losses from public payers rather than to increase profits and liquidity.

Project description:Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally similar proteins, thus finding structurally similar proteins accurately and efficiently from a large set of proteins is crucial. A protein structure can be represented as a vector by 3D-Zernike Descriptor (3DZD) which compactly represents the surface shape of the protein tertiary structure. This simplified representation accelerates the searching process. However, computing the similarity of two protein structures is still computationally expensive, thus it is hard to efficiently process many simultaneous requests of structurally similar protein search. This paper proposes indexing techniques which substantially reduce the search time to find structurally similar proteins. In particular, we first exploit two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. After that, we extend the techniques to further improve the search speed for protein structures. The extended indexing techniques build and utilize an reduced index constructed from the first few attributes of 3DZDs of protein structures. To retrieve top-k similar structures, top-10 × k similar structures are first found using the reduced index, and top-k structures are selected among them. We also modify the indexing techniques to support θ-based nearest neighbor search, which returns data points less than θ to the query point. The results show that both iDistance and iKernel significantly enhance the searching speed. In top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In θ-based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.

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Comparing different methods of indexing commercial health care prices.

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Comparing different methods of indexing commercial health care prices.

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