THE PRICE AIN'T RIGHT? HOSPITAL PRICES AND HEALTH SPENDING ON THE PRIVATELY INSURED.
ABSTRACT: We use insurance claims data covering 28% of individuals with employer-sponsored health insurance in the United States to study the variation in health spending on the privately insured, examine the structure of insurer-hospital contracts, and analyze the variation in hospital prices across the nation. Health spending per privately insured beneficiary differs by a factor of three across geographic areas and has a very low correlation with Medicare spending. For the privately insured, half of the spending variation is driven by price variation across regions, and half is driven by quantity variation. Prices vary substantially across regions, across hospitals within regions, and even within hospitals. For example, even for a nearly homogeneous service such as lower-limb magnetic resonance imaging, about a fifth of the total case-level price variation occurs within a hospital in the cross section. Hospital market structure is strongly associated with price levels and contract structure. Prices at monopoly hospitals are 12% higher than those in markets with four or more rivals. Monopoly hospitals also have contracts that load more risk on insurers (e.g., they have more cases with prices set as a share of their charges). In concentrated insurer markets the opposite occurs-hospitals have lower prices and bear more financial risk. Examining the 366 mergers and acquisitions that occurred between 2007 and 2011, we find that prices increased by over 6% when the merging hospitals were geographically close (e.g., 5 miles or less apart), but not when the hospitals were geographically distant (e.g., over 25 miles apart).
Project description:OBJECTIVE: To examine the relationship between insurance market structure and health care prices, utilization, and spending. DATA SOURCES: Claims for 37.6 million privately insured employees and their dependents from the Truven Health Market Scan Database in 2009. Measures of insurer market structure derived from Health Leaders Inter study data. METHODS: Regression models are used to estimate the association between insurance market concentration and health care spending, utilization, and price, adjusting for differences in patient characteristics and other market-level traits. RESULTS: Insurance market concentration is inversely related to prices and spending, but positively related to utilization. Our results imply that, after adjusting for input price differences, a market with two equal size insurers is associated with 3.9 percent lower medical care spending per capita (p = .002) and 5.0 percent lower prices for health care services relative to one with three equal size insurers (p < .001). CONCLUSION: Greater fragmentation in the insurance market might lead to higher prices and higher spending for care, suggesting some of the gains from insurer competition may be absorbed by higher prices for health care. Greater attention to prices and utilization in the provider market may need to accompany procompetitive insurance market strategies.
Project description:To measure the contribution of market-level prices, utilization, and health risk to medical spending variation among the Blue Cross Blue Shield of Texas (BCBSTX) privately insured population and the Texas Medicare population.Claims data for all BCBSTX members and publicly available CMS data for Texas in 2011.We used observational data and decomposed overall and service-specific spending into health status and health status adjusted utilization and input prices and input prices adjusted for the BCBSTX and Medicare populations.Variation in overall BCBSTX spending across HRRs appeared driven by price variation, whereas utilization variation factored more prominently in Medicare. The contribution of price to spending variation differed by service category. Price drove inpatient spending variation, while utilization drove outpatient and professional spending variation in BCBSTX. The context in which negotiations occur may help explain the patterns across services.The conventional wisdom that Medicare does a better job of controlling prices and private plans do a better job of controlling volume is an oversimplification. BCBSTX does a good job of controlling outpatient and professional prices, but not at controlling inpatient prices. Strategies to manage the variation in spending may need to differ substantially depending on the service and payer.
Project description:OBJECTIVES:To assess whether there is a difference between the net prices of medical products used by Dutch hospitals and, if there is, how this difference can be explained. DESIGN:Cross-sectional self-administered electronic survey. SETTING:We surveyed the prices paid for 17 commonly used medical products, such as pacemakers, gloves and stents in 38 Dutch hospitals (including general, specialised and academic hospitals) in 2017. Hospitals voluntarily and anonymously provided these data and received a personalised free benchmark tool in return. This tool provides information about the variance in prices of the medical products they buy. PARTICIPANTS:38 out of 79 hospitals entered and completed the study. PRIMARY AND SECONDARY OUTCOME MEASURES:Actual price paid excluding Value Added Tax (VAT) per item, the order size per year, total spending for an assortment group and total spending for all products purchased from a specific supplier were measured. RESULTS:We found large price variations for the medical products surveyed (average coefficient of variation of 71%). In general, these differences were hard to explain (average R2 of 26%). Only purchasing volume (for 8 out of 17 products) was significantly associated with the net price paid by a hospital. Total spending for an assortment group (in euros with a specific supplier) and total spending (for all products in euros with a specific supplier) were not related to the net price paid. CONCLUSIONS:We conclude that only purchasing volume is associated with lower prices paid. Total spending for an assortment group and total spending for all products purchased from a specific supplier are not. These results are in stark contrast to expectations based on economic theory. Other sources of differences in bargaining power might explain these findings. Further research might involve comparing prices across countries.
Project description:States are introducing regulations to slow health care spending growth, but which of these successfully reduce spending growth remains unclear. We studied Rhode Island's 2010 affordability standards, which imposed price controls-particularly inflation caps and diagnosis-based payments-on contracts between commercial insurers and hospitals and clinics and required commercial insurers to increase their spending on primary care and care coordination services. Using a difference-in-differences design, we compared spending among 38,001 commercially insured adults in Rhode Island to that among 38,001 matched adults in other states in the period 2007-16. Relative to quarterly fee-for-service (FFS) spending among the control group, quarterly FFS spending among the Rhode Island group decreased by $76 per enrollee after implementation of the policy, or a decline of 8.1 percent from 2009 spending. Quarterly non-FFS primary care coordination spending increased by $21 per enrollee. Total spending growth decreased, driven by lower prices concordant with the adoption of price controls. Quality measures were unaffected or improved. The Rhode Island experience indicates that states may be able to slow total commercial health care spending growth through price controls while maintaining quality.
Project description:BACKGROUND:Wide variations exist in price and quality for health-care services, but the link between price and quality remains uncertain. OBJECTIVE:This paper used claims data from a large commercially insured population to assess the association between both procedure- and provider-level prices and complication rates for three common outpatient surgical services. DESIGN:This is a retrospective cohort study. SETTING:The study used medical claims data from commercial health plans between 2009 and 2013 for three outpatient surgical services-joint arthroscopy, cataract surgery, and colonoscopy. MAIN MEASURES:For each procedure, price was assessed as the sum of patient, employer, and insurer spending. Complications were identified using existing algorithms specific to each service. Multivariate regressions were used to risk-adjust prices and complication rates. Provider-level price and complication rates were compared by calculating standardized differences that compared provider risk-adjusted price and complication rates with other providers within the same geographic market. The association between provider-level risk-adjusted price and complication rates was estimated using a linear regression. KEY RESULTS:Across the three services, there was an inverse association between both procedure- and provider-level prices and complication rates. For joint arthroscopy, cataract surgery, and colonoscopy, a one standard deviation increase in procedure-level price was associated with 1.06 (95% CI 1.05-1.08), 1.14 (95% CI 1.11-1.16), and 1.07 (95% CI 1.06-1.07) odds increases in the rate of procedural complications, respectively. A one standard deviation increase in risk-adjusted provider price was associated with 0.09 (95% CI 0.07 to 0.11), 0.02 (95% CI 0.003 to 0.05), and 0.32 (95% CI 0.29 to 0.34) standard deviation increases in the rate of provider risk-adjusted complication rates, respectively. LIMITATIONS:Results may be due to unobserved factors. Only three surgical services were examined, and the results may not generalize to other services and procedures. Quality measurements did not include patient satisfaction or experience measures. CONCLUSIONS:For three common outpatient surgical services, procedure- and provider-level prices are associated with modest increased rates of complication rates.
Project description:OBJECTIVE:To examine the effects of hospital and insurer markets concentration on transaction prices for inpatient hospital services. DATA SOURCES:Measures of hospital and insurer markets concentration derived from American Hospital Association and HealthLeaders-InterStudy data are linked to 2005-2008 inpatient administrative data from Truven Health MarketScan Databases. STUDY DESIGN:Uses a reduced-form price equation, controlling for cost and demand shifters and accounting for possible endogeneity of market concentration using instrumental variables (IV) technique. PRINCIPAL FINDINGS:The findings suggest that greater hospital concentration raises prices, whereas greater insurer concentration depresses prices. A hypothetical merger between two of five equally sized hospitals is estimated to increase hospital prices by about 9 percent (p < .001). A similar merger of insurers would depress prices by about 15.3 percent (p < .001). Over the 2003-2008 periods, the estimates imply that hospital consolidation likely raised prices by about 2.6 percent, while insurer consolidation depressed prices by about 10.8 percent. Additional analysis using longer panel data and applying hospital fixed effects confirms the impact of hospital concentration on prices. CONCLUSION:The findings provide support for strong antitrust enforcement to curb rising hospital service prices and health care costs.
Project description:<h4>Background</h4>Cost-sharing in health insurance plans creates incentives for patients to shop for lower prices, but it is unknown what price information patients can obtain when scheduling office visits.<h4>Objective</h4>To determine whether new patients can obtain price information for a primary care visit and identify variation across insurance types, offices, and geographic areas.<h4>Design</h4>Simulated patient methodology in which trained interviewers posed as non-elderly adults seeking new patient primary care appointments. Caller insurance type (employer-sponsored insurance [ESI], Marketplace, or uninsured) and plan were experimentally manipulated. Callers who were offered a visit asked for price information. Unadjusted means and regression-adjusted differences by insurance, office types, and geography were calculated.<h4>Participants</h4>Calls to a representative sample of primary care offices in ten states in 2014: Arkansas, Georgia, Iowa, Illinois, Massachusetts, Montana, New Jersey, Oregon, Pennsylvania, and Texas (N?=?7865).<h4>Main measures</h4>Callers recorded whether they were able to obtain a price. If not, they recorded whether they were referred to other sources for price information.<h4>Key results</h4>Overall, 61.8% of callers with ESI were able to obtain a price, versus 89.2% of uninsured and 47.3% of Marketplace callers (P?<?0.001 for differences). Price information was also more readily available in small offices and in counties with high uninsured rates. Among callers not receiving a price, 72.1% of callers with ESI were referred to other sources (billing office or insurance company), versus 25.8% of uninsured and 50.9% of Marketplace callers (P?<?0.001). A small fraction of insured callers were told their visit would be free. If not free, mean visit prices ranged from $157 for uninsured to $165 for ESI (P?<?0.05). Prices were significantly lower at federally qualified health centers (FQHCs), smaller offices, and in counties with high uninsured and low-income rates.<h4>Conclusions</h4>Price information is often unavailable for privately insured patients seeking primary care visits at the time a visit is scheduled.
Project description:In this paper we examine spending by privately insured patients with four conditions often treated with specialty drugs: cancer, kidney disease, rheumatoid arthritis, and multiple sclerosis. Despite having employer-sponsored health insurance, these patients face substantial risk for high out-of-pocket spending. In contrast to traditional pharmaceuticals, we find that specialty drug use is largely insensitive to cost sharing, with price elasticities ranging from 0.01 to 0.21. Given the expense of many specialty drugs, care management should focus on making sure that patients who will most benefit receive them. Once such patients are identified, it makes little economic sense to limit coverage.
Project description:To slow the growth of Medicare spending, some policy makers have advocated raising the Medicare eligibility age from the current sixty-five years to sixty-seven years. For the majority of affected adults, this would delay entry into Medicare and increase the time they are covered by private insurance. Despite its policy importance, little is known about how such a change would affect national health care spending, which is the sum of health care spending for all consumers and payers-including governments. We examined how spending differed between Medicare and private insurance using longitudinal data on imaging and procedures for a national cohort of individuals who switched from private insurance to Medicare at age sixty-five. Using a regression discontinuity design, we found that spending fell by $38.56 per beneficiary per quarter-or 32.4 percent-upon entry into Medicare at age sixty-five. In contrast, we found no changes in the volume of services at age sixty-five. For the previously insured, entry into Medicare led to a large drop in spending driven by lower provider prices, which may reflect Medicare's purchasing power as a large insurer. These findings imply that increasing the Medicare eligibility age may raise national health care spending by replacing Medicare coverage with private insurance, which pays higher provider prices than Medicare does.
Project description:BACKGROUND:In October 2012, the Chinese government established maximum retail prices for specific products, including 30 antineoplastic medications. Three years later, in June 2015, the government abolished price regulation for most medications, including all antineoplastic medications. This study examined the impacts of regulation and subsequent deregulation of prices of antineoplastic medications in China. METHODS:Using hospital procurement data and an interrupted time series with comparison series design, we examined the impacts of the policy changes on relative purchase prices (Laspeyres price index) and volumes of and spending on 52 antineoplastic medications in 699 hospitals. We identified three policy periods: prior to the initial price regulation (October 2011 to September 2012); during price regulation (October 2012 to June 2015); and after price deregulation (July 2015 to June 2016). RESULTS:During government price regulation, compared with price-unregulated cancer medications (n=22, mostly newer targeted products), the relative price of price-regulated medications (n=30, mostly chemotherapeutic products) decreased significantly (?=-0.081, p<0.001). After the government price deregulation, no significant price change occurred. Neither government price regulation nor deregulation had a significant impact on average volumes of or average spending on all antineoplastic medications immediately after the policy changes or in the longer term (p>0.05). CONCLUSION:Compared with unregulated antineoplastics, the prices of regulated antineoplastic medications decreased after setting price caps and did not increase after deregulation. To control the rapid growth of oncology medication expenditures, more effective measures than price regulation through price caps for traditional chemotherapy are needed.