What Is Market Power in Health Care Peer Reviewed
Wellness Serv Res Manag Epidemiol. 2019 Jan-Dec; 6: 2333392819886414.
Practice All Hospital Systems Have Market place Power? Association Between Infirmary System Types and Cardiac Surgery Prices
Sung W. Choi
oneHealth Administration, School of Public Affairs, The Pennsylvania Land University, Harrisburg, PA, USA
Avi Dor
2 Health Policy and Direction, Milken Constitute School of Public Health, The George Washington University, Washington, DC, The states
Received 2017 Mar 11; Revised 2019 Sep 27; Accepted 2019 Sep 27.
Abstract
Objective:
This study explores the price implications of hospital systems by analyzing the association of system characteristics with selected cardiac surgery pricing.
Data Source:
Using a large private insurance claim database, the authors identified xi 282 coronary artery bypass graft (CABG) cases and 49 866 percutaneous coronary intervention (PCI) cases from 2002 to 2007.
Study Design:
We conducted a retrospective observational written report using generalized linear models.
Main Findings:
We found that the CABG and PCI prices in centralized health and medico insurance systems were significantly lower than the prices in stand up-alone hospitals by iv.iv% and 6.four%, respectively. In addition, the CABG and PCI prices in contained health systems were significantly lower than in stand-alone hospitals, by 15.4% and 14.5%, respectively.
Conclusion:
The electric current antitrust guidelines tend to focus on the market share of merging parties and pay less attending to the characteristics of merging parties. The results of this study suggest that antitrust analysis could exist more effective by considering characteristics of hospital systems.
Keywords: health economics, medical cost, efficiency, managed care, disease management
Introduction
The wellness-intendance sector in the United States has been in a process of transformation for more iii decades.1-iii Wellness-care providers, including hospitals, physician groups, and insurance plans, have created complex interorganizational relationships within infirmary systems.4-seven Hospital systems, which are interorganizational consolidations arising from common ownership among hospitals, have become increasingly prevalent in the United States. In 2017, 3198 US-based community hospitals, accounting for 66% of all US-based community hospitals, were members of a hospital system.8
Among the many possible reasons, there are 2 chief rationales behind the proliferation of hospital systems in the United States.9 I reason is a procompetitive efficiency gain from infirmary organisation membership. Organization membership can enhance efficiency in health-intendance delivery by enabling participants to pool expensive medical devices to attain economies of scale or telescopic in health-intendance commitment and other administrative activities. The American Hospital Clan (AHA) has urged hospitals to interact with one another to improve efficiency and constrain costs.10
The other reason is the enhanced market power by hospital arrangement membership. Hospital systems tin can harm contest by leveraging their marketplace power to enhance the price of care. Karen Ignagni, the president of America's Health Insurance Plans, said "the rhetoric is all nigh efficiency, simply the reality is all about higher prices."11 Hence, the overall competitive effect of hospital arrangement membership is an empirical question.
Literature Review
Wellness-intendance expenditures in the U.s. have steadily increased for decades, and in most cases, the growth rate was faster than that of the consumer price alphabetize.12-15 Hospital consolidation in the 1990s was suggested as a commuter of hospital prices, and those consolidation activities were responsible for a growth of at least 5% in hospital prices.16
Many empirical studies of hospital systems accept investigated the toll implications of infirmary organization membership. They found that infirmary systems have been able to use their enhanced marketplace power over insurers and increase prices more speedily than stand up-lone hospitals.viii,17-21
Near studies that investigated the cost effects of infirmary arrangement membership used average or list prices as toll measures due to data limitations. Neither boilerplate nor listing prices are accurate measures of bodily prices. Listing prices practise not capture payment discounts from insurers to hospitals. When total payments are divided by the total quantity of care to obtain an average value, the varying mixes of services within hospitals cannot be captured by the average payment.22 The measurement errors and information loss due to the use of listing or average prices can bias estimates of hospital arrangement payments.
Transaction prices are the actual payments from insurers to providers in the grade of reimbursements for health-care services provided.23 Transaction price is different from list or average price considering the transaction price refers to a specific admission at the patient level rather than all admissions at the hospital level. Several empirical studies have been able to use a unique information fix that includes non only individual and clinical information only also actual payment data from insurers.17,22-26
Transaction pricing studies for medical procedures found mixed results regarding the clan betwixt hospital organisation membership and surgery price. Some transaction pricing studies have demonstrated that hospital system membership is significantly associated with hospital prices.17,23 In other studies, hospital system membership was constitute to be insignificantly associated with infirmary prices.26
The mixed results for the cost effects of hospital organisation membership tin exist explained by the operationalization of the arrangement variable. The majority of articles used an indicator variable for hospital organization membership.xvi,23,26 Other articles take focused more than on local market characteristics, such as HMO penetration and marketplace concentration, than on system membership.22,24,25
Categorizing the massive restructuring of organizational arrangements amongst health-care providers since 1980s is not an easy task.27 Policy makers in antitrust and regulatory agencies have struggled to draw the responsibilities within those organizational arrangements.28 Although several instance studies have focused on evolving wellness-care organizations, Bazzoli et al'southward taxonomy can be regarded as the but empirical classification of hospital systems.29 This taxonomy contributed to the existing literature past generating empirically testable hypotheses for speedily evolving wellness-care provider consolidations.
Bazzoli et al's taxonomy categorized unlike groups of hospital systems that share a comparable caste of differentiation (note i), integration (annotation 2), and centralization (notation 3).29 Differentiation is defined as "selecting the type and scope of services to offer" amid member hospitals.29 Organizations with a high level of differentiation provide distinguished health-care services amidst member organizations, whereas less differentiated organizations provide more compatible services among fellow member organizations. Integration is defined every bit "the ability to pull the pieces together in gild to maximize the value of the services provided."29 Integration among participating organizations can exist achieved through either direct ownership or contractual relationships.30,31 Although common ownerships can lower transaction costs and achieve economies of scale, contractual relationships can flexibly answer to changes in local markets. Centralization is defined as the "degree of wellness-care services provided at the system level."29 Centralized organizations provide near of their services at the system level, whereas decentralized organizations provide most services at the individual infirmary level. Bazzoli et al chose infirmary services, physician arrangements, and insurance products as key dimensions to be evaluated based on the criteria of differentiation, integration, and centralization.29
Bazzoli et al identified 4 types of health systems using cluster analysis, categorizing different groups for systems that share a comparable degree of differentiation, integration, and centralization.29 The first cluster is the centralized health and physician insurance system (CHPIHS). These wellness systems distinguish themselves from other hospitals through their high-level centralization in infirmary services, doc arrangements, and insurance products. These wellness systems are moderately differentiated in hospital services and physician arrangements. Most centralized hospital systems are closely located with member hospitals in urban areas. The CHPIHS is more likely to have strong efficiency proceeds through centralization and differentiation and have potent market ability through small geographic dispersion, compared to stand-lone hospitals.
The second cluster is the moderately centralized health system (MCHS). These hospital systems show a moderate caste of centralization; thus, infirmary services, physician arrangements, and insurance products are adult and delivered in both systems and individual hospital settings. Physician arrangements and insurance products in these systems are moderately differentiated among member hospitals, while infirmary services are highly differentiated. Compared to the centralized health systems, these systems have a smaller number of member hospitals and more widespread geographic locations. The MCHS is more than probable to have moderate efficiency gain through centralization and differentiation and have moderate market power due to big geographic dispersion, compared to stand up-alone hospitals.
The third cluster is the decentralized wellness system (DHS). These health systems are widely decentralized in physician arrangements and insurance products but only moderately centralized in infirmary services. Physician arrangements and insurance products are developed and provided at the individual hospital level. Furthermore, these health systems are extensively differentiated in hospital services, doc arrangements, and insurance products. A few referrals are made amidst member hospitals. The number of DHS is not large, but the number of a member hospitals is large for each DHS. These DHS member hospitals are located over broad geographic locations. The DHS is more than probable to have strong efficiency proceeds through centralization and differentiation and have potent market power through integration, compared to stand-alone hospitals. The DHS is more likely to have strong efficiency proceeds through differentiation and have moderate market power due to large dispersion among members, compared to stand-alone hospitals.
The fourth cluster is the independent health arrangement (IHS), whose members take a low level of differentiation and centralization in infirmary services, physician arrangements, and insurance products. Nigh no referral is fabricated amid the member hospitals, and many of these hospital systems are positioned in suburban areas with a small number of fellow member hospitals. The IHS is more likely to accept moderate efficiency gain through centralization and differentiation and have low market power due to small-scale number of member hospitals, compared to stand-alone hospitals.
The contributions of this study to the body of health policy literature are every bit follows: First, this study is an empirical application of a comprehensive taxonomy of hospital systems to procedure-level pricing studies. Bazzoli et al developed an empirically analyzable comprehensive taxonomy of hospital systems based on different patterns of infirmary services, dr. arrangements, and insurance products.29 This comprehensive taxonomy of infirmary systems has been extensively tested in empirical investigations of infirmary systems.32-38 Previous medical procedure pricing studies adopted an indicator variable for infirmary arrangement membership, resulting in the loss of information across unlike characteristics of hospital systems.
Second, this study analyzes the transaction prices of cardiac surgeries rather than the list or average prices. Private insurers in the United States pay providers based on negotiated payment rates determined via provider–insurer bargaining.39 Hospitals negotiate payment rates for each procedure with each insurer once a year, leading to substantial variation in reimbursement rates across insurers.40 The transaction prices that the hospital actually receives are determined past negotiations betwixt hospitals and insurers. Hence, transaction price is a better measure of the true price of individual health-care service, and this report explores individual insurers' cardiac process pricing to analyze its clan with hospital organisation membership.
Finally, this study aims to contribute to ongoing policy discussions related to antitrust guidelines in the health-intendance market place. The Department of Justice (DOJ) and Federal Trade Committee (FTC) take been keeping an heart on infirmary systems and potential market place ability that could distort market competition. From 1989 to 1993, 8 hospital mergers out of more than than 200 were challenged past the DOJ and FTC due to the potential anticompetitive furnishings of those mergers.41 According to the horizontal merger guidelines of the DOJ and FTC, mergers that raise pregnant antitrust concern are determined by the current level and the change in the market concentration.42
The current antitrust guidelines tend to focus on the market share of the merging parties and pay less attention to the characteristics of the merging parties. Market place power is divers equally "the ability to profitably maintain prices higher up competitive levels for a significant catamenia of time."42 This study aims to analyze whether hospital systems with certain characteristics have significantly higher prices than other hospitals. In this way, we want to improve the electric current horizontal merger guidelines by determining whether market concentration is the only source of market power.
Method
Data and Study Sample
The chief data source for this study was MarketScan commercial claims and encounters data. MarketScan is one of the largest multisource private health insurance databases in the United States. Information technology covers individual demographics, financial measures, and clinical information from health plans and hospitals. At the individual level, MarketScan collects details for 23 one thousand thousand employees and their family members from approximately 100 large private employers and insurance plans.43 This study analyzed MarketScan commercial claims and encounter data from 2002 to 2007.
The AHA Annual Survey Database was another information source for this study. The AHA Annual Survey Database is a nationally representative hospital database in the United States that collects provider information, including organizational structure, inpatient and outpatient utilization, expenses, physician arrangements, purchasing affiliations, and geographic indicators. This almanac survey includes data on "6500 hospitals, 400-plus wellness-intendance systems, networks and alliances, 700 wellness-care organizations and associations, 700 governmental agencies, and 3000 other accredited providers."44
This report analyzed coronary artery featherbed graft surgery (CABG) and percutaneous coronary intervention (PCI) admissions because each procedure satisfies the following weather: (1) The selected surgery is associated with well-defined surgical procedures in patient-level databases; (2) the selected surgery is among the most common procedures in the United States; (3) the selected surgeries are non discretionary procedures; and (4) the selected surgery is non substitutable for outpatient care.
The study sample included all hospital admissions undergoing a CABG or PCI procedure between January 1, 2002, and December 31, 2007, from the MarketScan commercial merits and run across data (Figure ane). Among all cases with CABG (northward = 53 392), we excluded the following: (1) cases that were 3% outliers in terms of price; (2) cases for which the principal procedure was not CABG; and (three) cases for which the AHA identifier was missing. The final sample size was xi 282 CABG admissions for 2002 to 2007. Post-obit the same exclusion criteria, the written report identified 49 866 PCI admissions. Figure one shows the final PCI sample and exclusion criteria in particular.
The coronary artery featherbed grafting (CABG) and percutaneous coronary intervention (PCI) samples and exclusion criteria.
Table 1 shows the diagnosis and procedure codes for selected cardiac surgeries. All cardiac surgeries were defined in a fashion to maximize comparability among procedures as much as possible. In addition, all procedures were segregated past their severity and complexity.
Table 1.
Procedural Codes for Cardiac Procedures.
| ICD-9 Diagnosis Codes | CPT Procedure Codes | |
|---|---|---|
| Coronary artery featherbed graft | Aortocoronary bypass of coronary artery (36.11, 36.12, 36.13, 36.14), other aortocoronary bypass (36.ten) | Vein only (33510, 33511, 33512, 33513, 33514, 33516), Venous and arterial graft (33517, 33518, 33519, 33521, 33522, 33523), arterial graft (33533, 33534, 33535, 33536) |
| Percutaneous coronary intervention | Balloon (00.xl, 00.41, 36.04), stent procedure (36.06), drug-eluting (36.07) | Balloon (92982, 92984, 92975), stent procedure (92980, 92981), drug-eluting (G0290, G0291) |
Variable Definitions
The dependent variable of this study was the transaction price of cardiac surgeries. This study analyzed the transaction price, that is, the actual payment from insurers to providers during the hospitalization stay. The independent variables of this study were divided into 3 groups: The first group of independent variables was patient characteristics.
The MarketScan commercial claim and encounter data include the age and sex of enrollees; the ethnicity information of enrollees is not reported in the data. Detailed procedural codes were used to control for patients' disease severity. The Elixhauser index was adopted as the comorbidity index. Comorbidity is defined equally "A clinical condition that exists before a patient'due south admission to the hospital, is not related to the principal reason for the hospitalization and is likely to be a significant factor influencing mortality and resource use in the hospital."45 In this article, comorbidity conditions were operationalized as 4 categorical variables that reflect the number of comorbidities (0, 1, 2, and iii+).
The MarketScan data comprise varying types of wellness insurance plans, including basic/major medical plans, comprehensive plans, sectional provider organization plans, health maintenance organizations (HMOs) plans, noncapitated indicate of service plans (POS), preferred provider organizations (PPOs) plans, capitated or partially capitated POS plans, and consumer-driven wellness plans (CDHP). Post-obit Dor et al, this written report categorized these plans into 4 groups: fee-based plans, POS plans, HMO plans, and CDHP plans.
The second group of independent variables was hospital attributes. Amidst the hospital attributes from the AHA Annual Survey, this report controlled for hospital ownership, pedagogy condition, organization membership, and system clusters. The AHA Annual Survey identifies 4 types of hospital ownership: (1) regime, nonfederal; (2) nongovernment, not for profit; (3) investor owned, for profit; and (4) government, federal. This study defined the hospital ownership types as private not for profit, individual for profit, and public. Teaching hospitals were divers as affiliated member hospitals of the Quango of Teaching Hospitals and Health Systems. Every bit previously described, clusters of system membership are defined every bit CHPIHS, MCHS, DHS, and IHS. The status of infirmary network membership was also included as an independent variable. The reference group for the hospital system variable was stand-lone hospitals, which do non vest to any hospital organization. The reference group for the hospital network variable was non-network hospitals.
The 3rd group of independent variables was the local market condition. The HMO penetration is divers equally the number of HMO enrollees divided by the total population in the market. The Herfindahl-Hirschman Alphabetize (HHI), which is the sum of squared market share of all the hospitals in the market, was used as a infirmary market concentration measure.46 Hospital referral region by the Dartmouth Atlas of Health Intendance was used every bit the geographic market place definition to summate market competition.
Analytic Methods
Private-level cost regressions were performed separately for the CABG and PCI samples. The price equation for each procedure is given by the equation:
where i, h, j, and t alphabetize the individual, infirmary, local market place, and year, respectively. The vector Xit refers to the patient characteristics, including age, sex, disease severity, comorbidity, and health insurance plan type.
Dissimilar sets of patient severity measures were adopted across procedures. The number of vessels treated, including single CABG with 1 vessel, 2 vessels, 3 vessels, and four or more vessels, was the measure of patient severity for the cases with CABG. Those who underwent multiple CABGs, for example, 2-vessel CABG and iv-vessel CABG performed in a unmarried episode of care, were the reference grouping for the patients with CABG.
3 unlike PCI types accounted for 49 866 PCI admissions: (1) balloon procedures; (two) drug-eluting procedures; and (3) stent procedures. Patient severity for the balloon and drug-eluting procedures was measured by the number of vessels treated, while patient severity for the stent procedure was measured by the number of stents used. Some other severity mensurate was included for those who underwent thrombolytic infusion surgery, which is the injection of a claret clot–bursting drug. Too, the reference group for the PCI admissions was patients who underwent multiple PCIs in a single episode of care. Three Elixhauser variables for comorbidities (1, 2, 3+) were included, and the no comorbidity (0) group constituted the reference category. Three programme types (HMOs, POS, and CDHP) were included; the reference category was fee-based plans.
The vector Chiliadh t refers to infirmary h's attributes, including individual not for turn a profit, private for profit, teaching status, network membership, and system cluster (CHPIHS, MSHS, DHS, and IHS). The reference categories for ownership, didactics condition, and arrangement membership were public hospitals, nonteaching hospitals, and stand up-alone hospitals, respectively. The vector Zjt refers to local marketplace conditions, including HMO penetration rate and the HHI. The variable refers to the twelvemonth effect, and refers to the random issue.
The transaction price variable had a right-skewed distribution, which is a typical characteristic of health-care result variables. This tail problem violates the assumptions of linear regression and can crusade biased estimates of the standard errors. One standard method of robust estimation is log transformation to smooth skewed distributions in statistical interpretation.47 When the dependent variable is log-transformed, the results should exist back-transformed into the original scale for meaningful interpretation of the results. Several retransformation methods were available for this process.48,49 However, it is complicated to retransform when heteroskedasticity is present.49,50
As an alternative method, generalized linear models (GLMs) were developed and tested among economists.51,52 The GLM method solves the retransformation effect by using the link function instead of transforming the dependent variable itself.52 In addition, the presence of heteroskedasticity can be controlled by a variance structure in the model.53 This study used the GLM method to obtain robust standard errors in the price equations with log-link and Gaussian distribution.
Results
Descriptive Statistics
The CABG sample consisted of 11 282 patients whose principal process was CABG (Table two). The average CABG price for all sample hospital admissions was Usa$36 712. The average CABG vary across system blazon from US$33 441 of IHS to US$37 203 of DHS. In the CABG sample, CHPIHS were more probable to exist private not for profit, teaching, network affiliated, and large in terms of size and market share than stand-lone hospital (SAH). In addition, CHPIHS were located in more than full-bodied market, higher HMO penetration rate, and larger metropolitan area than SAH. The PCI sample included 49 866 patients whose primary reason for hospitalization was PCI. The boilerplate unadjusted PCI toll for this sample was U.s.a.$22 485 (Table 3). The average PCI also vary across arrangement type from US$xix 885 of IHS to United states$23 696 of DHS. In the PCI sample, CHPIHS were more likely to exist non for profit, education, network affiliated, and big in terms of size and market share. In addition, CHPIHS were located in more concentrated market, higher HMO penetration rate, and had college portion of emergency PCI delivery than SAH. Market place concentration was measured using system HHI, which was 0.194 for the CABG sample and 0.198 for the PCI sample. The average HMO penetration rate was 22.nine% for CABG sample and 21.5% for PCI sample. The rates are lower than the results of existing studies, which means the markets covered in this report were less concentrated than existing studies.54,55
Table 2.
Descriptive Statistics for the CABG by Organization Blazon.
| CHPIHS | MCHS | DHS | IHS | SAH | Total | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Hateful | SD | Hateful | SD | Mean | SD | Hateful | SD | Mean | SD | Mean | SD | |
| Dependent variable | ||||||||||||
| Hospital cost (United states of america$) | 36 802.6 | 385.v | 36 035.six | 366.5 | 372 03.7 | 417.9 | 334 41.2 | 826.4 | 37 057.3 | 316.eight | 36 711.ix | 180.viii |
| Patient demographics | ||||||||||||
| Historic period | 56.118 | 0.137 | 55.976 | 0.123 | 56.033 | 0.131 | 56.543 | 0.303 | 56.067 | 0.096 | 56.062 | 0.058 |
| Female (reference) | 0.208 | 0.009 | 0.182 | 0.008 | 0.199 | 0.008 | 0.122 | 0.019 | 0.204 | 0.006 | 0.196 | 0.004 |
| Male | 0.792 | 0.009 | 0.818 | 0.008 | 0.801 | 0.008 | 0.878 | 0.019 | 0.796 | 0.006 | 0.804 | 0.004 |
| Elixhauser 0 (reference) | 0.393 | 0.011 | 0.346 | 0.009 | 0.378 | 0.010 | 0.479 | 0.028 | 0.409 | 0.008 | 0.388 | 0.005 |
| Elixhauser i | 0.323 | 0.010 | 0.320 | 0.009 | 0.306 | 0.009 | 0.305 | 0.026 | 0.294 | 0.007 | 0.308 | 0.004 |
| Elixhauser two | 0.131 | 0.007 | 0.175 | 0.008 | 0.161 | 0.008 | 0.100 | 0.017 | 0.150 | 0.006 | 0.153 | 0.003 |
| Elixhauser 3+ | 0.154 | 0.008 | 0.158 | 0.007 | 0.156 | 0.007 | 0.116 | 0.018 | 0.147 | 0.006 | 0.152 | 0.003 |
| Multiple CABGs (reference) | 0.642 | 0.011 | 0.621 | 0.010 | 0.659 | 0.010 | 0.746 | 0.025 | 0.654 | 0.007 | 0.648 | 0.004 |
| Single CABG—1 vessel | 0.173 | 0.008 | 0.168 | 0.007 | 0.137 | 0.007 | 0.119 | 0.018 | 0.149 | 0.006 | 0.154 | 0.003 |
| Single CABG—2 vessels | 0.116 | 0.007 | 0.103 | 0.006 | 0.079 | 0.006 | 0.045 | 0.012 | 0.090 | 0.005 | 0.094 | 0.003 |
| Unmarried CABG—3 vessels | 0.033 | 0.004 | 0.070 | 0.005 | 0.079 | 0.006 | 0.039 | 0.011 | 0.060 | 0.004 | 0.061 | 0.002 |
| Unmarried CABG—4 vessels + | 0.035 | 0.004 | 0.039 | 0.004 | 0.046 | 0.004 | 0.051 | 0.013 | 0.047 | 0.003 | 0.043 | 0.002 |
| Emergency | 0.297 | 0.010 | 0.250 | 0.009 | 0.224 | 0.009 | 0.350 | 0.027 | 0.218 | 0.007 | 0.245 | 0.004 |
| Nonemergency (reference) | 0.703 | 0.010 | 0.750 | 0.009 | 0.776 | 0.009 | 0.650 | 0.027 | 0.782 | 0.007 | 0.755 | 0.004 |
| Hospital characteristics | ||||||||||||
| Private not-for-turn a profit | 0.933 | 0.006 | 0.885 | 0.006 | 0.673 | 0.010 | 0.717 | 0.026 | 0.719 | 0.007 | 0.785 | 0.004 |
| Private for-profit | 0.000 | 0.000 | 0.009 | 0.002 | 0.314 | 0.010 | 0.090 | 0.016 | 0.057 | 0.004 | 0.090 | 0.003 |
| Public (reference) | 0.067 | 0.006 | 0.106 | 0.006 | 0.013 | 0.002 | 0.193 | 0.022 | 0.224 | 0.007 | 0.124 | 0.003 |
| Didactics | 0.535 | 0.011 | 0.242 | 0.008 | 0.214 | 0.008 | 0.183 | 0.022 | 0.236 | 0.007 | 0.285 | 0.004 |
| Nonteaching (reference) | 0.465 | 0.011 | 0.758 | 0.008 | 0.786 | 0.008 | 0.817 | 0.022 | 0.764 | 0.007 | 0.715 | 0.004 |
| Network | 0.627 | 0.011 | 0.426 | 0.010 | 0.637 | 0.010 | 0.331 | 0.027 | 0.452 | 0.008 | 0.513 | 0.005 |
| Not-network (reference) | 0.373 | 0.011 | 0.574 | 0.010 | 0.363 | 0.010 | 0.669 | 0.669 | 0.548 | 0.008 | 0.487 | 0.005 |
| Organization size | four.601 | 0.054 | three.582 | 0.051 | 3.950 | 0.054 | iii.026 | 0.081 | 1.000 | 0.000 | 2.905 | 0.024 |
| Market Share | 0.313 | 0.005 | 0.258 | 0.004 | 0.246 | 0.003 | 0.261 | 0.013 | 0.198 | 0.003 | 0.244 | 0.002 |
| Insurance types | ||||||||||||
| Plan: Fee based (reference) | 0.737 | 0.010 | 0.727 | 0.009 | 0.811 | 0.008 | 0.807 | 0.022 | 0.799 | 0.006 | 0.774 | 0.004 |
| Plan: HMO | 0.115 | 0.007 | 0.124 | 0.007 | 0.069 | 0.005 | 0.090 | 0.016 | 0.073 | 0.004 | 0.092 | 0.003 |
| Plan: POS | 0.142 | 0.008 | 0.140 | 0.007 | 0.116 | 0.007 | 0.100 | 0.017 | 0.123 | 0.005 | 0.128 | 0.003 |
| Plan: CDHP | 0.005 | 0.002 | 0.008 | 0.002 | 0.004 | 0.001 | 0.003 | 0.003 | 0.004 | 0.001 | 0.005 | 0.001 |
| Marketplace structure | ||||||||||||
| Organization HHI | 0.216 | 0.004 | 0.192 | 0.003 | 0.196 | 0.002 | 0.220 | 0.008 | 0.182 | 0.002 | 0.194 | 0.001 |
| HMO penetration | 0.278 | 0.003 | 0.224 | 0.003 | 0.256 | 0.003 | 0.137 | 0.007 | 0.199 | 0.002 | 0.229 | 0.002 |
| Rural surface area (reference) | 0.077 | 0.006 | 0.033 | 0.004 | 0.009 | 0.002 | 0.109 | 0.018 | 0.078 | 0.004 | 0.054 | 0.002 |
| Small-scale metro area | 0.254 | 0.010 | 0.436 | 0.010 | 0.376 | 0.010 | 0.556 | 0.028 | 0.535 | 0.008 | 0.429 | 0.005 |
| Large metro area | 0.669 | 0.010 | 0.532 | 0.010 | 0.615 | 0.010 | 0.334 | 0.027 | 0.386 | 0.008 | 0.516 | 0.005 |
| N | 2037 | 2543 | 2358 | 311 | 4033 | xi 282 | ||||||
Table iii.
Descriptive Statistics for the PCI by System Type.
| CHPIHS | MCHS | DHS | IHS | SAH | Total | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | SD | Hateful | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | |
| Dependent variable | ||||||||||||
| Hospital cost (Us$) | 23 276.three | 189.0 | 23 695.ix | 115.8 | 22 883.4 | 153.viii | 19 885.four | 339.1 | 21 950.3 | 76.v | 22 485.8 | 56.1 |
| Patient demographics | ||||||||||||
| Age | 54.540 | 0.111 | 54.093 | 0.072 | 54.313 | 0.081 | 53.020 | 0.232 | 54.155 | 0.042 | 54.178 | 0.031 |
| Female (reference) | 0.202 | 0.006 | 0.234 | 0.004 | 0.213 | 0.005 | 0.214 | 0.013 | 0.234 | 0.003 | 0.228 | 0.002 |
| Male | 0.798 | 0.006 | 0.766 | 0.004 | 0.787 | 0.005 | 0.786 | 0.013 | 0.766 | 0.003 | 0.772 | 0.002 |
| Elixhauser 0 (reference) | 0.491 | 0.008 | 0.422 | 0.005 | 0.463 | 0.006 | 0.426 | 0.016 | 0.470 | 0.003 | 0.461 | 0.002 |
| Elixhauser 1 | 0.283 | 0.007 | 0.316 | 0.005 | 0.322 | 0.005 | 0.293 | 0.015 | 0.315 | 0.003 | 0.313 | 0.002 |
| Elixhauser 2 | 0.144 | 0.005 | 0.167 | 0.004 | 0.119 | 0.004 | 0.178 | 0.012 | 0.142 | 0.002 | 0.144 | 0.002 |
| Elixhauser iii+ | 0.082 | 0.004 | 0.094 | 0.003 | 0.096 | 0.003 | 0.104 | 0.010 | 0.073 | 0.002 | 0.082 | 0.001 |
| Multiple PCIs (reference) | 0.128 | 0.005 | 0.109 | 0.003 | 0.145 | 0.004 | 0.066 | 0.008 | 0.113 | 0.002 | 0.117 | 0.001 |
| Single PCI—ane stent | 0.832 | 0.006 | 0.854 | 0.004 | 0.811 | 0.005 | 0.880 | 0.010 | 0.831 | 0.002 | 0.833 | 0.002 |
| Single PCI—multiple stents | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.001 | 0.001 | 0.000 | 0.001 | 0.000 |
| Single PCI—1 vessel | 0.039 | 0.003 | 0.034 | 0.002 | 0.044 | 0.002 | 0.053 | 0.007 | 0.054 | 0.001 | 0.047 | 0.001 |
| Single PCI—multiple vessels | 0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.000 | 0.001 | 0.000 |
| Single PCI—thrombolytic infusion | 0.000 | 0.000 | 0.002 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.000 | 0.001 | 0.000 |
| Emergency | 0.642 | 0.007 | 0.611 | 0.005 | 0.561 | 0.006 | 0.569 | 0.016 | 0.522 | 0.003 | 0.555 | 0.002 |
| Nonemergency (reference) | 0.358 | 0.007 | 0.389 | 0.005 | 0.439 | 0.006 | 0.431 | 0.016 | 0.478 | 0.003 | 0.445 | 0.002 |
| Hospital characteristics | ||||||||||||
| Individual not-for-profit | 0.955 | 0.003 | 0.814 | 0.004 | 0.606 | 0.006 | 0.680 | 0.015 | 0.721 | 0.003 | 0.741 | 0.002 |
| Private for-profit | 0.000 | 0.000 | 0.031 | 0.002 | 0.371 | 0.006 | 0.179 | 0.012 | 0.053 | 0.001 | 0.094 | 0.001 |
| Public (reference) | 0.045 | 0.003 | 0.155 | 0.004 | 0.022 | 0.002 | 0.141 | 0.011 | 0.226 | 0.002 | 0.165 | 0.002 |
| Teaching | 0.434 | 0.008 | 0.179 | 0.004 | 0.081 | 0.003 | 0.091 | 0.009 | 0.206 | 0.002 | 0.200 | 0.002 |
| Nonteaching (reference) | 0.566 | 0.008 | 0.821 | 0.004 | 0.919 | 0.003 | 0.909 | 0.009 | 0.794 | 0.002 | 0.800 | 0.002 |
| Network | 0.525 | 0.008 | 0.360 | 0.005 | 0.527 | 0.006 | 0.347 | 0.015 | 0.414 | 0.003 | 0.429 | 0.002 |
| Non-network (reference) | 0.475 | 0.008 | 0.640 | 0.005 | 0.473 | 0.006 | 0.653 | 0.015 | 0.586 | 0.003 | 0.571 | 0.002 |
| System size | 3.324 | 0.034 | 2.224 | 0.018 | 2.285 | 0.019 | ii.138 | 0.041 | ane.000 | 0.000 | ane.638 | 0.006 |
| Market share | 0.314 | 0.003 | 0.241 | 0.002 | 0.221 | 0.002 | 0.188 | 0.006 | 0.192 | 0.001 | 0.216 | 0.001 |
| Insurance types | ||||||||||||
| Plan: Fee-based (reference) | 0.777 | 0.006 | 0.802 | 0.004 | 0.809 | 0.005 | 0.830 | 0.012 | 0.777 | 0.002 | 0.787 | 0.002 |
| Plan: HMO | 0.139 | 0.005 | 0.096 | 0.003 | 0.095 | 0.003 | 0.077 | 0.009 | 0.097 | 0.002 | 0.100 | 0.001 |
| Programme: POS | 0.076 | 0.004 | 0.099 | 0.003 | 0.092 | 0.003 | 0.093 | 0.009 | 0.119 | 0.002 | 0.107 | 0.001 |
| Plan: CDHP | 0.008 | 0.001 | 0.003 | 0.001 | 0.005 | 0.001 | 0.000 | 0.000 | 0.007 | 0.000 | 0.006 | 0.000 |
| Marketplace structure | ||||||||||||
| System HHI | 0.231 | 0.003 | 0.206 | 0.001 | 0.208 | 0.001 | 0.195 | 0.003 | 0.188 | 0.001 | 0.198 | 0.001 |
| HMO penetration | 0.242 | 0.002 | 0.211 | 0.001 | 0.237 | 0.002 | 0.189 | 0.004 | 0.207 | 0.001 | 0.215 | 0.001 |
| Rural area (reference) | 0.044 | 0.003 | 0.015 | 0.001 | 0.053 | 0.003 | 0.089 | 0.009 | 0.075 | 0.002 | 0.059 | 0.001 |
| Small metro area | 0.490 | 0.008 | 0.580 | 0.005 | 0.413 | 0.006 | 0.480 | 0.016 | 0.500 | 0.003 | 0.501 | 0.002 |
| Big metro expanse | 0.466 | 0.008 | 0.405 | 0.005 | 0.534 | 0.006 | 0.431 | 0.016 | 0.424 | 0.003 | 0.441 | 0.002 |
| Due north | 4251 | 9228 | 7443 | 963 | 27 981 | 49 866 | ||||||
For both groups, the average age of the patients was mid-50s (CABG: 56.06%; PCI: 54.18%). The majority were male (CABG: eighty.iv%; PCI: 77.ii%). As measured past the Elixhauser index, 38.eight% of the patients with CABG did not accept any comorbid condition. The proportions of patients with CABG who had one, two, and 3 or more than weather condition were 30.8%, 15.3%, and xv.2%, respectively. Among the patients with PCI, 46.ane% did not have whatever comorbid condition. The proportions of patients with PCI who had i, 2, and 3 or more comorbid atmospheric condition were 31.3%, fourteen.iv%, and 8.2%, respectively.
Multiple measures were adopted to control for severity amid the patients with CABG and PCI. Approximately 15.4% of all patients with CABG underwent i-vessel CABG, and four.3% underwent CABG of 4 or more vessels. More than half (64.8%) had multiple CABG procedures during one episode of intendance. The proportion of CABG procedures performed in emergency settings was 24.v%.
Among the cases with PCI, 83.3% underwent single PCI with 1 stent, and 0.1% underwent single PCI with multiple stents. The proportions of single PCI with ane-vessel and multiple-vessel PCIs were 4.7% and 0.1%, respectively. In addition, 0.ane% of patients with PCI underwent thrombolytic infusion PCI and 11.7% underwent multiple PCI procedures during one episode of care. More than half (55.5%) of the patients received PCIs in emergency settings.
Regarding hospital characteristics, the 2 samples had comparable features. Privately endemic not-for-profit hospitals accounted for 78.5% of CABG admissions and 74.1% of PCI admissions, while educational activity hospitals accounted for 28.5% of CABG cases and 20.0% of cases with PCI. Network fellow member hospitals performed 51.3% of CABG cases and 42.ix% of PCI cases, while 64.4% of CABG cases and 43.9% of PCI cases were performed at organization member hospitals. The proportions of CABGs performed at CHPIHS, MCHS, DHS, and IHS were eighteen.1%, 22.5%, 20.nine%, and 2.8%, respectively. The proportions of PCIs performed at CHPIHS, MCHS, DHS, and IHS were 8.5%, 18.v%, 14.9%, and 1.9%, respectively.
The majority of patients with CABG and PCI were enrolled in fee-based health insurance plans (77.4% and 78.seven%, respectively). The proportions of the HMO, POS, and CDHP/HDHP enrollees amid the patients with CABG were 9.2%, 12.8%, and 0.five%, respectively. The HMO, POS, and CDHP plans accounted for 10.0%, 10.7%, and 0.6% of patients with PCI, respectively.
Pricing Regressions
Tabular array 4 shows the results of pricing regressions for the 11 282 cases with CABG and 49 866 cases with PCI. For both samples, an indicator variable for hospital system status was adopted in model i, while hospital arrangement cluster variables were adopted in model 2. In general, the 2 models showed comparable coefficients and significance.
Tabular array 4.
The Generalized Linear Model Results for the CABG and PCI Admissions.
| CABG | PCI | |||||||
|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 1 | Model two | |||||
| Parameter Judge | Standard Mistake | Parameter Judge | Standard Mistake | Parameter Estimate | Standard Error | Parameter Estimate | Standard Error | |
| Patient demographics | ||||||||
| Age × 10 | −0.009 | 0.008 | −0.009 | 0.008 | −0.026a | 0.003 | −0.027a | 0.003 |
| Male | −0.006 | 0.012 | −0.008 | 0.012 | 0.026a | 0.006 | 0.027a | 0.006 |
| Elixhauser 1 | 0.032a | 0.012 | 0.034a | 0.012 | 0.082a | 0.006 | 0.079a | 0.006 |
| Elixhauser two | 0.133a | 0.014 | 0.135a | 0.014 | 0.115a | 0.007 | 0.114a | 0.007 |
| Elixhauser iii+ | 0.146a | 0.015 | 0.148a | 0.015 | 0.171a | 0.009 | 0.171a | 0.009 |
| Single vessels one | −0.093a | 0.014 | −0.095a | 0.014 | ||||
| Single vessels two | 0.023 | 0.016 | 0.024 | 0.016 | ||||
| Single vessels iii | 0.054a | 0.019 | 0.055a | 0.019 | ||||
| Single vessels 4+ | 0.016 | 0.024 | 0.018 | 0.024 | ||||
| One stent | −0.147a | 0.008 | −0.146a | 0.008 | ||||
| Multiple stents | 0.008 | 0.109 | 0.010 | 0.109 | ||||
| One vessel | −0.384a | 0.015 | −0.379a | 0.015 | ||||
| Multiple vessels | −ane.093a | 0.175 | −one.070a | 0.182 | ||||
| Thrombolytic infusion | 0.325a | 0.089 | 0.279a | 0.086 | ||||
| Emergency | 0.184a | 0.011 | 0.181a | 0.011 | 0.236a | 0.005 | 0.236a | 0.005 |
| Hospital characteristics | ||||||||
| Private not-for-turn a profit | −0.088a | 0.017 | −0.089a | 0.017 | 0.036a | 0.008 | 0.034a | 0.008 |
| Private for-profit | 0.014 | 0.025 | 0.017 | 0.027 | 0.143a | 0.011 | 0.166a | 0.012 |
| Teaching | −0.020b | 0.011 | −0.018 | 0.012 | −0.012b | 0.007 | −0.016c | 0.007 |
| Network member | 0.064a | 0.011 | 0.064a | 0.011 | 0.050a | 0.005 | 0.056a | 0.006 |
| System size | −0.005c | 0.002 | −0.003 | 0.002 | −0.014a | 0.002 | −0.012a | 0.002 |
| Market share | 0.667a | 0.066 | 0.645a | 0.066 | 0.529a | 0.031 | 0.520a | 0.031 |
| System fellow member | −0.016 | 0.021 | 0.007 | 0.008 | ||||
| System × market share | −0.220a | 0.054 | −0.012 | 0.024 | ||||
| CHPIHS | −0.044c | 0.018 | −0.064a | 0.013 | ||||
| MCHS | −0.021 | 0.016 | 0.052a | 0.008 | ||||
| DHS | −0.001 | 0.019 | −0.057a | 0.011 | ||||
| IHS | −0.154a | 0.045 | −0.145a | 0.020 | ||||
| CHPIHS × market share | −0.061 | 0.049 | 0.167a | 0.034 | ||||
| MCHS × market place share | −0.272a | 0.080 | −0.125a | 0.027 | ||||
| DHS × market share | −0.418a | 0.075 | 0.112c | 0.046 | ||||
| IHS × market share | 0.025 | 0.093 | −0.035 | 0.132 | ||||
| Insurance type | ||||||||
| Plan: HMO | −0.084a | 0.017 | −0.079a | 0.018 | −0.156a | 0.009 | −0.158a | 0.009 |
| Plan: POS | −0.003 | 0.014 | −0.005 | 0.014 | −0.073a | 0.008 | −0.075a | 0.008 |
| Programme: CDHP/HDHP | 0.250a | 0.063 | 0.252a | 0.063 | −0.046 | 0.032 | −0.053b | 0.032 |
| Market structure | ||||||||
| HMO penetration × 10 | 0.007b | 0.004 | 0.006 | 0.004 | −0.015a | 0.002 | −0.012a | 0.002 |
| HHI arrangement × x | −0.684a | 0.082 | −0.682a | 0.083 | −0.488a | 0.040 | −0.503a | 0.040 |
| Small metro area | 0.011 | 0.022 | 0.016 | 0.022 | −0.107a | 0.011 | −0.116a | 0.011 |
| Large metro surface area | −0.001 | 0.024 | 0.007 | 0.024 | −0.169a | 0.012 | −0.177a | 0.012 |
| Twelvemonth dummies | ||||||||
| 2003 | 0.074a | 0.019 | 0.073a | 0.019 | 0.100a | 0.014 | 0.098a | 0.014 |
| 2004 | 0.088a | 0.016 | 0.086a | 0.016 | 0.201a | 0.012 | 0.203a | 0.012 |
| 2005 | 0.158a | 0.020 | 0.154a | 0.018 | 0.240a | 0.012 | 0.240a | 0.012 |
| 2006 | 0.219a | 0.021 | 0.217a | 0.020 | 0.272a | 0.012 | 0.279a | 0.012 |
| 2007 | 0.222a | 0.025 | 0.214a | 0.024 | 0.298a | 0.013 | 0.296a | 0.012 |
| Total | 11 282 | 49 866 | ||||||
Comorbid conditions were significantly associated with the CABG and PCI prices. Having 1 comorbid condition was associated with CABG price increases of 3.ii% (model 1) and 3.iv% (model two). Having 3 or more comorbidities was associated with a fourteen.8% (model two) increment in the CABG price. Similarly, having 1 comorbid condition was associated with PCI price increases of 8.ii% (model ane) and seven.9% (model 2), respectively. Having 3 or more than comorbid conditions was associated with a 17.1% (model 1 and ii) increase in the PCI price.
We too institute that CABG and PCI prices were significantly associated with the patient severity measures. The price of single CABG with 1 vessel was lower than the multiple CABG price (reference grouping) by 9.3% (model 1) and ix.five% (model ii). The price of unmarried CABG with 3 vessels was college than that of the reference group past 5.four% (model i) and 5.5% (model 2), respectively. The cost of a single PCI with 1 stent was lower than the multiple PCI price (reference grouping) past 14.7% (model 1) and 14.vi% (model two), respectively. The cost of a unmarried PCI with 1 vessel was lower than that of the reference group by 38.4% (model i) and 37.9% (model 2), respectively. The CABG and PCI prices in an emergency setting were higher than nonemergency CABG and PCI prices by 18.one% (model 2) and 23.6% (model 1 and 2), respectively.
The CABG and PCI prices were significantly associated with the characteristics of the infirmary at which the procedures were performed. Private not-for-profit hospitals had lower CABG prices than public hospitals by 11.6% (model one) and 12% (model 2), respectively. Individual for-profit hospitals had higher PCI prices than public hospitals by 8.8% (model i) and eight.9% (model ii), respectively. Network member hospitals had significantly higher CABG and PCI prices than non-network hospitals past 6.4% (model 1) and five.0% (model 1), respectively.
Hospital arrangement status variables were insignificantly associated with both CABG and PCI prices. System fellow member hospitals' CABG and PCI prices were not significantly different from that of stand-lone hospitals.
However, the association betwixt system cluster variables and the cardiac procedure prices showed more meaningful results. The CHPIHS had significantly lower CABG and PCI prices than stand-lonely hospitals by iv.4% and half dozen.four%, respectively. Moreover, IHS besides had significantly lower CABG and PCI prices than stand-lone hospitals by 15.iv% and 14.five%, respectively. MCHSs had a college PCI price than stand-alone hospitals, while DHS had a lower PCI toll than stand up-alone hospitals. The results advise that not all arrangement fellow member hospitals had higher CABG and PCI prices than stand-lonely hospitals and there existed structural differences in CABG and PCI prices beyond system cluster.
The marketplace share of hospital organization also significantly associated with CABG and PCI prices. When market share increased past 10%, the CABG prices increased by 6.vii% (model i) and 6.v% (model 2), respectively. In the same situation, the PCI prices increased past v.three% (model ane) and 5.ii% (model 2), respectively. System size measured by the number of fellow member hospitals was significantly associated with CABG and PCI prices, simply the issue size was relatively small-scale. The interaction term betwixt system cluster and market share showed mixed effects on CABG and PCI prices. When the market place share increased by 10%, the PCI prices for CHPIHS increased by 1.67%. For DHS, 10% increment in market place share was associated with iv.2% subtract in CABG prices.
The type of health insurance program was likewise significantly associated with the CABG and PCI prices. The CABG price for HMO enrollees was significantly lower than that of fee-based plan enrollees (reference group) by 8.4% (model 1) and 7.9% (model two). The CABG cost for patients with CDHP plans was 25.2% college than that of the reference grouping (models 2). The PCI price for HMO enrollees was too lower than that of the reference group by 15.vi% (model i) and 15.viii% (model ii). The PCI price for patients with a POS plan was higher than that of the reference group by 7.3% (model one) and 7.5% (model 2).
Marketplace concentration and HMO penetration rate were significantly associated with the PCI price. When the system HHI increased by 0.1, the CABG price decreased by half dozen.8% (models 1 and 2) and PCI price decreased by five.0% (model 2). When the HMO penetration increased by x%, the PCI price decreased by 1.5% (model ane) and i.2% (model two), respectively.
Discussion
This study examined the association betwixt hospital system membership and cardiac surgery prices. The results of this written report bear witness that CHPIHS and IHS had significantly lower CABG and PCI prices than stand-alone hospitals after controlling for patient characteristics, infirmary attributes, and market place conditions. Our results confirm the existing studies that constitute efficiency gains amidst arrangement member hospitals.56,57
The DOJ and FTC guidelines for horizontal mergers and acquisitions explain the antitrust analysis process that the agencies utilise to evaluate the anticompetitive effects of infirmary arrangements. The Antitrust Agencies regard mergers and acquisitions under the post-obit weather condition every bit potentially anticompetitive: First, pregnant antitrust concern arises for mergers and acquisitions that effect in HHI increases of more than 100 points in either a moderately concentrated market place (HHI between 1500 and 2500) or a highly concentrated market (HHI to a higher place 2500). Second, the Antitrust Agencies regard mergers and acquisitions resulting in HHI increases of more than 200 points equally generating or reinforcing market power.58
According to the Horizontal Merger Guidelines, the definition of market power is "the ability to profitably maintain prices to a higher place competitive levels for a significant period of time."42 The Antitrust Agencies determine potentially anticompetitive mergers and acquisitions based on the level of market concentration and the alter in market concentration.58 Nonetheless, there are many other sources of marketplace power, including product quality, proximity to consumers, information, and so on.59 This study shows evidence that certain types of infirmary systems (CHPIHS and IHS) have significantly lower prices of care than stand-alone hospitals. The results suggest that the Antitrust Agencies tin can ameliorate the efficiency of antitrust analyses by focusing on moderately centralized hospital organisation.
This study also found a meaning and positive association between hospital network membership and cardiac surgery prices (Table 3). The CABG cost at network member hospitals was college than that of stand-alone hospitals past 6.4% (models 1 and ii). In add-on, the PCI price at network member hospitals was higher than that of stand up-alone hospitals past 5.0% (model 1) and v.6% (model ii).
The results are consistent with existing studies that emphasize that direct ownership in infirmary systems can pb to better coordination of purpose, strategy, and action than contractual relationships in health networks.32 In addition, the transaction cost framework predicts that straight ownership in infirmary systems tin can evade transaction costs, including monitoring and coordination, that tin occur in contractual relationships among network member hospitals.60,61
Express generalizability is a limitation of this report. The MarketScan data exercise non provide a representative sample of the U.s. population. The results of this report can be generalized at most to Americans covered past employer-sponsored insurance. The MarketScan commercial claims and encounters data (2002-2007) were merged with the AHA Annual Survey Database using AHA identification numbers (AHA IDs). A considerable number of infirmary admissions in the MarketScan information did not include AHA IDs, so the study sample included simply those admissions with AHA IDs.
This study did not clarify the association between hospital network clusters and cardiac surgery prices. The original taxonomy by Bazzoli et al (1999) included dissimilar types of health network affiliations, such equally centralized health networks, moderately centralized health networks, decentralized health networks, and independent health networks. Still, the network type variables were not included in the AHA Annual Survey Database for 2002 to 2007. The results of this essay cannot be generalized to different clusters of infirmary networks.
Acknowledgments
Avi Dor admit support from the Bureau for Healthcare Quality and Research (AHRQ; Grant No. ROI HS02361 0-01).
Author Biographies
Sung W. Choi is an assistant professor of Wellness Administration at the School of Public Affairs, Penn Land Harrisburg.
Avi Dor is a professor in the Section of Health Policy and Management at the Milken Institute School of Public Health, George Washington University (GW), and Managing director of its Wellness Economics and Health Policy PhD Programs.
Notes
i.Organizations with a high level of differentiation provide distinctive health-care services among member organizations, whereas less differentiated organizations provide more uniform services among member organizations.
2.Integration among participating organizations can be achieved through either straight buying or contractual relationships. Although common ownerships can lower transaction costs and achieve economies of scale, contractual relationships tin can flexibly respond to changes in local markets.
3.Centralized organizations provide about of their services at the system level, whereas decentralized organizations provide virtually services at the individual hospital level.
Footnotes
Announcement of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this commodity.
Funding: The author(s) disclosed receipt of the post-obit fiscal support for the enquiry, authorship, and/or publication of this article: Support past the Agency for Healthcare Quality and Research (AHRQ; Grant No. ROI HS02361 0-01).
ORCID iD: Sung W. Choi, PhD 
https://orcid.org/0000-0001-9990-762X
Avi Dor, PhD https://orcid.org/0000-0003-4475-4333
References
1. Bazzoli GJ, Dynan L, Burns LR, Yap C. Two decades of organizational change in wellness care: what have we learned? Med Care Res Rev. 2004;61(3):247–331. [PubMed] [Google Scholar]
2. Gaynor Yard, Haas-Wilson D. Change, consolidation, and competition in wellness care markets. J Econ Perspec. 1999;thirteen(1):141–164. [PubMed] [Google Scholar]
three. Lesser CS, Ginsburg Pb. Update on the nation'due south health care organisation: 1997-1999. Health Aff. 2000;19(6):206–216. [PubMed] [Google Scholar]
4. Bazzoli GJ, Manheim LM, Waters TM. U.S. hospital industry restructuring and the infirmary safety net. Research. 2003;40(i):6–24. [PubMed] [Google Scholar]
5. Luke RD, Ozcan YA, Olden PC. Local markets and systems: hospital consolidations in metropolitan areas. Wellness Serv Res. 1995;30(four):555–575. [PMC free article] [PubMed] [Google Scholar]
six. Reardon J, Reardon L. The restructuring of the hospital services manufacture. J Econo Issu. 1995;29(four):1063–1081. [Google Scholar]
7. Shortell SM, Bazzoli GJ, Dubbs NL, Kralovec P. Classifying wellness networks and systems: managerial and policy implications. Health Intendance Manage Rev. 2000;25(4):9–17. [PubMed] [Google Scholar]
9. Burgess JF, Carey K, Young GJ. The effect of network arrangements on infirmary pricing beliefs. J Wellness Econ. 2005;24(2):391–405. [PubMed] [Google Scholar]
10. American Hospital Association. Hospital Collaboration: The Need for an Appropriate Antitrust Policy. Chicago, IL: Function of General Counsel, American Hospital Clan; 1992. [Google Scholar]
xi. Creswell J, Abelson R. New laws and rise costs create a surge of supersizing hospitals: business/financial desk. New York Times. 2013. [Google Scholar]
12. Strunk BC, Ginsburg Lead. Tracking health care costs: trends plough down in 2003: although health care costs grew more slowly than in 2002, their growth even so outpaced growth in the overall U.S. economy in 2003. Wellness Aff. 2004;23(suppl 1):W4–354-362. [Google Scholar]
13. Ginsburg Atomic number 82, Strunk BC, Broker MI, Cookson JP. Tracking health care costs: continued stability but at high rates in 2005. Health Aff. 2006;25(vi):w486–w495. [PubMed] [Google Scholar]
14. Lemieux J, Mulligan T. Trends in inpatient infirmary prices, 2008 to 2010. Am J Manag Care. 2013;19(three):e106. [PubMed] [Google Scholar]
15. Melnick GA, Fonkych K. Hospital prices increase in California, specially among hospitals in the largest multi-hospital systems. Inquiry. 2016;53:4695801665155. [PMC gratuitous article] [PubMed] [Google Scholar]
16. Vogt Westward, Town R. How has Hospital Consolidation Afflicted the Price and Quality of Hospital Intendance? Princeton, NJ: The Robert Wood Johnson Foundation; 2006. [PubMed] [Google Scholar]
17. Capps C, Dranove D. Hospital consolidation and negotiated PPO prices. Health Aff. 2004;23(2):175–181. [PubMed] [Google Scholar]
18. Cuellar AE, Gertler PJ. How the expansion of infirmary systems has affected consumers. Health Aff. 2005;24(one):213–219. [PubMed] [Google Scholar]
nineteen. Dranove D, Shanley One thousand. Cost reductions or reputation enhancement as motives for mergers: the logic of multihospital systems. Strategic Manage J. 1995;16(i):55–74. [Google Scholar]
20. Melnick G, Keeler Eastward. The effects of multi-hospital systems on hospital prices. J Health Econ. 2007;26(two):400–413. [PubMed] [Google Scholar]
21. Young GJ, Desai KR, Hellinger FJ. Customs control and pricing patterns of nonprofit hospitals: an antitrust assay. J Wellness Polit Policy Law. 2000;25(6):1051–1081. [PubMed] [Google Scholar]
22. Moriya AS, Vogt WB, Gaynor G. Hospital prices and market structure in the hospital and insurance industries. Wellness Econ Policy Police force. 2010;five(iv):459–479. [PubMed] [Google Scholar]
23. Brooks JM, Dor A, Wong HS. Infirmary-insurer bargaining: an empirical investigation of appendectomy pricing. J Health Econ. 1997;16(4):417–434. [PubMed] [Google Scholar]
24. Dor A, Grossman Yard, Koroukian SM. Hospital transaction prices and managed-care discounting for selected medical technologies. Am Econ Rev. 2004;94(ii):352–356. [PubMed] [Google Scholar]
25. Dor A, Koroukian SM, Grossman 1000. Managed care discounting: bear witness from the MarketScan database. Inquiry. 2004;41(2):159–169. [PubMed] [Google Scholar]
26. Dor A, Koroukian S, Xu F, Stulberg J, Delaney C, Cooper G. Pricing of surgeries for colon cancer: patient severity and market factors. Cancer. 2012;118(23):5741–5748. [PMC free article] [PubMed] [Google Scholar]
27. Conrad DA, Shortell SM. Integrated health systems: promise and functioning. Front Health Serv Manage. 1996;13(i):iii–viii. [PubMed] [Google Scholar]
28. Shortell SM, Gillies RR, Anderson DA. The new earth of managed care: creating organized commitment systems. Health Aff. 1994;13(v):46–64. [PubMed] [Google Scholar]
29. Bazzoli GJ, Shortell SM, Dubbs Northward, Chan C, Kralovec P. A taxonomy of wellness networks and systems: bringing order out of chaos. Wellness Serv Res. 1999;33(6):1683–1717. [PMC free article] [PubMed] [Google Scholar]
30. Goldsmith JC. The illusive logic of integration. Healthc Forum J. 1994;37(v):26. [PubMed] [Google Scholar]
31. Robinson JC, Casalino LP. Vertical integration and organizational networks in wellness care. Health Aff. 1996;xv(ane):7–22. [PubMed] [Google Scholar]
32. Bazzoli GJ, Chan B, Shortell SM, D'Aunno T. The financial performance of hospitals belonging to health networks and systems. Inquiry. 2000;37(3):234–252. [PubMed] [Google Scholar]
33. Bazzoli GJ, Shortell SM, Dubbs NL. Rejoinder to taxonomy of health networks and systems: a reassessment. Health Serv Res. 2006;41(3p1):629–639. [PMC free article] [PubMed] [Google Scholar]
34. Carey Thou. Infirmary cost efficiency and system membership. Inquiry. 2003;40(1):25–38. [PubMed] [Google Scholar]
35. Chukmaitov As, Bazzoli GJ, Harless DW, Hurley RE, Devers KJ, Zhao M. Variations in inpatient mortality among hospitals in different system types, 1995 to 2000. Med Care. 2009;47(4):466–473. [PubMed] [Google Scholar]
36. Chukmaitov A, Harless DW, Bazzoli GJ, Carretta HJ, Siangphoe U. Commitment system characteristics and their association with quality and costs of care: implications for answerable care organizations. Health Intendance Manage Rev. 2015;40(2):92–103. [PubMed] [Google Scholar]
37. Rosko Medico, Proenca J, Zinn JS, Bazzoli GJ. Hospital inefficiency: what is the affect of membership in different types of systems? Inquiry. 2007;44(3):335–349. [PubMed] [Google Scholar]
38. Wang J, Probst JC, Stoskopf CH, Sanders JM, McTigue JF. Information disproportion and performance tilting in hospitals: a national empirical written report. Health Econ. 2011;20(12):1487–1506. [PubMed] [Google Scholar]
39. Reinhardt UE. The pricing of U.S. hospital services: anarchy behind a veil of secrecy. Health Aff. 2006;25(1):57–69. [PubMed] [Google Scholar]
40. Sorensen AT. Insurer-hospital bargaining: negotiated discounts in mail-deregulation connecticut. J Indust Econom. 2003;51(4):469–490. [Google Scholar]
41. United States. Department of Justice. Antitrust Segmentation. Federal Trade Commission. Improving Health Intendance: A Dose of Competition. Washington, DC: Department of Justice, Antitrust Segmentation; 2004. [Google Scholar]
42. U.s.a.. Federal Trade Commission, United States Department of Justice. Department of Justice and Federal Merchandise Commission Statements of Antitrust Enforcement Policy in Health Care. Washington, DC: Federal Merchandise Commission; 1997. [Google Scholar]
43. Adamson DM, Chang Southward, Hansen LG. Health Inquiry Data for the Real World: The MarketScan Databases. Bengaluru, Karnataka: Thomson Healthcare; 2008. [Google Scholar]
44. American Hospital Association. AHA Guide to the Wellness Care Field. Chicago, IL: Health Forum LLC; 2014. [Google Scholar]
45. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with authoritative data. Med Care. 1998;36(1):8–27. [PubMed] [Google Scholar]
46. Manheim LM, Bazzoli GJ, Sohn M. Local hospital competition in large metropolitan areas. J Econom Manage Strat. 1994;3(1):143–167. [Google Scholar]
47. Manning WG, Mullahy J. Estimating log models: to transform or non to transform? J Wellness Econ. 2001;20(4):461–494. [PubMed] [Google Scholar]
48. Duan N. Smearing estimate: a nonparametric retransformation method. J Ame Statist Associat. 1983;78(383):605–610. [Google Scholar]
49. Manning WG. The logged dependent variable, heteroscedasticity, and the retransformation problem. J Health Econ. 1998;17(iii):283–295. [PubMed] [Google Scholar]
l. Mullahy J. Much ado about 2: reconsidering retransformation and the two-office model in health econometrics. J Health Econ. 1998;17(3):247–281. [PubMed] [Google Scholar]
51. Wedderburn RWM. Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method. Biometrika. 1974;61(3):439–447. [Google Scholar]
52. Basu A, Rathouz PJ. Estimating marginal and incremental furnishings on health outcomes using flexible link and variance function models. Biostatistics (Oxford, England). 2005;6(i):93–109. [PubMed] [Google Scholar]
53. Crowder SV. A simple method for studying run—length distributions of exponentially weighted moving boilerplate charts. Technometrics. 1987;29(4):401–407. [Google Scholar]
54. Encinosa We, Hellinger FJ. The touch of medical errors on 90-solar day costs and outcomes: an examination of surgical patients. Health Serv Res. 2008;43(half dozen):2067–2085. [PMC free commodity] [PubMed] [Google Scholar]
55. Shen Y, Wu VY, Melnick G. Trends in hospital toll and revenue, 1994-2005: how are they related to HMO penetration, concentration, and for-profit ownership? Health Serv Res. 2010;45(1):42–61. [PMC free article] [PubMed] [Google Scholar]
56. Ozcan YA, Luke RD. A national report of the efficiency of hospitals in urban markets. Health Serv Res. 1993;27(six):719–739. [PMC gratis article] [PubMed] [Google Scholar]
57. Rosko Md, Proenca J. Bear upon of network and system utilise on hospital X-inefficiency. Health Care Manage Rev. 2005;30(one):69–79. [PubMed] [Google Scholar]
58. United States Federal Trade Commission, United States. Department of Justice. Department of Justice and Federal Trade Committee Statements of Horizontal Merger Guidelines. Washington, DC: Federal Trade Committee; 2010. [Google Scholar]
59. American Bar Clan. Section of Antitrust Law. Market Power Handbook: Contest Constabulary and Economic Foundations. Chicago, IL: ABA Section of Antitrust Law; 2005. [Google Scholar]
threescore. Mick SS. Innovations in Wellness Care Delivery: Insights for Organization Theory. San Francisco, CA: Jossey-Bass Publishers; 1990. [Google Scholar]
61. Williamson OE, Masten SE. Transaction Cost Economic science. Aldershot, England: Edward Elgar; 1995. [Google Scholar]
Manufactures from Health Services Research and Managerial Epidemiology are provided here courtesy of SAGE Publications
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851608/#:~:text=Market%20power%20is%20defined%20as,higher%20prices%20than%20other%20hospitals.
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