William L. Schpero, MPH

The accountable care organization (ACO) model is currently being pursued by private insurers, as well as federal and state governments. Little is known, however, about the prevalence of private payer ACO contracts and the characteristics of contract structures or how these compare with public ACO contracts.

Cross-sectional analysis of the National Survey of Accountable Care Organizations (n = 173) on ACO contracts with public and private payers and private payer contract characteristics.

Most ACOs had only 1 ACO contract (57%). About half of ACOs had a contract with a private payer. The single most common private payer ACO contract was an upside-only shared savings model (41%), although the majority of private contracts included some form of downside risk (56%). A large majority of contracts made shared savings contingent upon quality performance (79%), and some included bonus payments for quality performance (39%). Most private payer contracts included upfront payments, such as care management payments (56%) or capital investment (17%). Organizations with private ACO contracts were larger and more advanced than ACOs with only public payer contracts.

While there are fewer ACOs with commercial contracts than public contracts, commercial contracts are more likely to include both downside risk and upfront payments.

This study drew from a national survey of accountable care organizations (ACOs) to examine the prevalence of private payer ACO contracts and the characteristics of contract structures, as well as to determine how these compare with public ACO contracts.

		The majority of ACOs had only 1 ACO contract, and about half of ACOs had a contract with a private payer.

		Private payer ACO contracts were most often shared-savings models, and the majority of private contracts included downside risk.

		Organizations with private ACO contracts were larger and more advanced than ACOs with only public payer contracts.

Private and public insurers continue to pursue implementation of the accountable care organization (ACO) model, a value-based payment and delivery model in which a group of providers is held accountable for the cost and quality of care for an assigned patient population. ACOs are present in 55% of local healthcare markets,

 and recent estimates suggest that more than 14% of patients receive their care from a provider that is part of an ACO.

 State Medicaid agencies have also developed ACO models,

 as have a number of private insurers, including Aetna, Blue Cross Blue Shield associations, Cigna, and United HealthCare.

 Despite the number of provider organizations implementing the ACO model and the growing understanding of Medicare ACO programs, little is known about what types of ACO contracts are being pursued with commercial payers and what types of provider organizations hold these contracts or the details of these contracts.

Understanding the nature of ACO contracts is critical to even beginning to understand the potential effect of the ACO model on healthcare costs and quality. Critics of the ACO model have suggested that most ACOs will have difficulty implementing the infrastructure they need to effectively manage downside risk.

 This raises questions about the characteristics of provider organizations that choose to sign an ACO contract involving financial risk. In addition, some have argued that ACO contracts, despite their ostensible emphasis on incentivizing quality, will in reality mirror managed care of the 1990s and potentially encourage physicians to skimp on necessary care to produce cost savings.

 Done correctly, ACO contract incentive structures could lead to desired outcomes of reduced costs and increased quality. But done poorly, incentive structures may lead to unanticipated consequences, such as inciting “upcoding,” to game risk adjustment schemes

 or encouraging physicians to attract low-risk patients, as some argue has happened in Medicare managed care.

 Before researchers and policy makers can assess these issues, a clear understanding of ACO contracts and their incentive structures is key.

Past attempts to characterize ACO contracts have focused on the arrangements of a singular insurer,

a single compound of care (such as behavioral health),

or the experiences of small convenience samples.

 In this study, we use new data from the National Survey of ACOs

 to systematically examine the prevalence of various types of ACO contracts (eg, Medicare, Medicaid, and commercial), the components of commercial payer contracts, and the capabilities of the organizations that are pursuing commercial payer contracts. Our data provide the first comprehensive, systematic picture of the commercial ACO contract landscape. Our results illuminate how commercial ACO contracts compare with public ACO contracts, as well as what types of provider organizations are pursuing each type of contract.

We define an ACO contract as a contract that holds a group of providers collectively responsible for both the total cost of care and the quality of care for a defined patient population. Notably, this definition excludes several types of contracts that some may classify under the broad umbrella of “accountable care”: pay-for-performance that does not include responsibility for total cost of care, and capitation without a major quality component. Additionally, this definition excludes organizations that are selfidentified ACOs without a corresponding ACO contract.

ACO contracts may vary significantly in their design, including the level of financial risk assumed by the provider organization (ie, are providers eligible only for gains or are they responsible for any losses incurred?) and how the cost and quality of care are measured and rewarded. 

 provides illustrative examples of some major public and commercial payer ACO contracts. ACO contracts are generally based on one of 3 payment arrangements: shared savings, global budgets, and capitated payments. Under shared savings models, providers are reimbursed via fee-for-service, and at the end of the year split with the payer any savings generated (as measured against prespecified benchmarks). Providers are often eligible to keep a larger proportion of these savings if they also agree to share in any losses or costs above prespecified benchmarks. The Medicare Shared Savings Program is a version of this type of contract. Under a global budget model, providers have a predetermined global budget for their assigned patient population that is reconciled at the end of the year. If costs are below the global budget, the ACO retains these savings; if costs are above the global budget, the ACO is responsible to pay back some or all of these losses. Under a capitated model, providers are paid in advance a set amount per assigned ACO patient and often bear full financial risk; they are responsible for any costs they incur above their capitated payment but retain any savings if their costs are below the capitated payment.

ACO contracts may reward quality through 3 major mechanisms. First, shared savings contracts can make all or a portion of shared savings contingent upon quality performance. In the Medicare Shared Savings Program, for example, providers can only share in savings if they meet quality benchmarks. Second, contracts may provide bonuses for quality performance. For example, the Alternative Quality Contract model paid providers bonuses above their global budget for achieving quality benchmarks. Finally, contracts may withhold a portion of payment and return it—contingent upon quality performance For example, under the Hennepin Health ACO arrangement, a portion of the capitated payment is withheld and returned to the providers only if the providers meet quality benchmarks.

We used data from the National Survey of Accountable Care Organizations (NSACO) for this study. We designed the survey to capture key characteristics and capabilities of ACOs, along with the perspectives and outlook of their leaders. The Web and telephone survey included questions on ACO contracts, leadership, structure, organizational capabilities, and local context, as well as questions designed to understand respondents’ views on the potential of the ACO model (

). These questions were designed based on frameworks for evaluating ACOs,

other surveys, and interviews with ACOs. The survey was completed by executive- or director-level individuals at identified ACOs.

For inclusion in our study sample, we attempted to identify all ACOs that had been established by August 2012. Medicare and state Medicaid ACO program participant lists were publicly available. ACOs involving private insurers were identified from multiple sources, including Web searches, press releases, membership lists of ACO collaboratives, and previous surveys that had identified ACOs. In total, we identified 292 organizations potentially eligible for our survey. A set of screening questions at the beginning of the survey determined if a respondent had an ACO contract and was therefore eligible for the survey. Our survey was completed by 173 eligible ACOs between October 2012 and May 2013, with a response rate of 70%. The eAppendix includes more details on the survey design, population, and response rate.

The NSACO included a number of questions specific to ACO contracts and contract structures. Respondents were asked to indicate whether they were participating in Medicare and Medicaid ACO programs and to list up to 4 ACO contracts with private insurers. We only included contracts with private insurers if the provider organization had a signed letter of agreement in place or a signed contract.

Respondents were asked several questions on the structure of their largest contract with a private insurer (defined by the number of attributed patients), including payment and risk arrangements and total cost-of-care calculations. In our analysis, we describe the distribution of contract types and the structure of those contracts. We also compare the characteristics and capabilities of organizations with differing contract types (public vs private only, risk-bearing vs not risk-bearing).

Just over half of the respondents (51%) had an ACO contract with a private insurer (

), two-thirds had an ACO contract with Medicare either through the Pioneer Program or the Medicare Shared Savings Program, and a quarter had a Medicaid ACO contract. Most organizations had only one ACO contract (57%). Blue Shield Blue Cross associations represented the most common private payer (

Respondents were asked to describe attributes of their largest ACO contract with a private insurer (

). Private payer ACO contracts were most often shared savings models (69%). Most private payer contracts included downside risk (56%), either through capitation, global budgets, or shared savings models that included shared losses. In comparison, only 7% of Medicare Shared Savings Program contracts included downside risk. All Pioneer ACO contracts, by design, include some level of downside risk during the contract period.

Respondents reported that under private payer ACO contracts, the ACO received a mean of 56% of shared savings generated. In comparison, providers with upside risk only in the MSSP receive 50% of savings generated, those under MSSP with downside risk receive 60% of savings, and Pioneer ACOs receive 80% of savings. A large majority of private contracts made shared savings contingent upon quality performance (79%), a finding similar to the Medicare programs. Some ACO contracts included bonus payments for quality performance (39%), either in addition to contingent shared savings or as quality payments for those under capitation or global budgets. Most private payer contracts included upfront payments, such as care management payments (56%) or capital investment (17%).

We next examined what services are included in the total cost of care for which providers are held responsible under private payer ACO contracts. Nearly all contracts held ACOs responsible for costs associated with emergency department care, outpatient care, inpatient care, labs and x-rays, advanced imaging, and professional services (98% to 100%). Fewer included pharmacy (78%); durable medical equipment (78%); mental health or substance abuse services (60%); and vision, hearing, and speech services (50%). Medicare ACO contracts, by comparison, do not include pharmacy, vision, or hearing in the total cost of care calculation, but do include mental health and durable medical equipment.

The mean length of a private payer contract was 3.2 years, with a range of 1 year to 9 years. Contract size varied across Medicare, Medicaid, and private payers (not shown in table). Medicare Shared Savings Program participants are required to have a minimum of 5000 attributed beneficiaries, while Pioneer Program participants must have 15,000 in most cases. However, a number of private payer ACO contracts (21%) cover fewer than 5000 beneficiaries. We compared contract features and size across specific private payers for ACO contracts. We found across the board that there was common variation on contract characteristics among payers. For example, there were similar proportions of risk-bearing contracts, upfront payments, and services included in the total cost of care across the private payers shown in Table 2.

We conducted some additional analyses on private payer contracts not presented in our tables. First, we examined common structures of private payer ACO contracts. The most common structure involved a shared savings model with no downside risk, under which receipt of shared savings was contingent on meeting prespecified quality metrics (n = 20). These structures mirror the financial model of the Medicare Shared Savings Program.

Additionally, respondent organizations were asked whether they believed the amount of risk they would be assuming in their largest commercial ACO contract would be sufficient to influence their behavior to try to achieve targeted cost and quality goals. More than 60% indicated they “agreed” or “strongly agreed” that the risk they assumed in their first performance year would be sufficient. Interestingly, the proportion agreeing or strongly agreeing did not differ significantly between those without downside risk (64%) and those whose contracts included downside risk (62%).

Finally, we examined characteristics of organizations with private payer ACO contracts compared to those with only a public payer contract (Medicare or Medicaid) to understand systematically what kinds of provider organizations were taking on private payer ACO contracts (Table 4). Organizations with private ACO contracts were more advanced than those with only public ACO contracts: they were significantly more likely to be an integrated delivery system (69% vs 36%) or include a hospital (72% vs 53%). ACOs with private contracts also had somewhat higher care management capabilities (38% vs 28%) and levels of meaningful use among primary care physicians (43% vs 37%), although neither of these differences were statistically significant. In addition, organizations with private ACO contracts had greater numbers of participating providers: ACOs with private contracts had significantly more full-time-equivalent primary care physicians (219 vs 138) and specialists (342 vs 137). Notably, organizations with private ACO contracts were more likely to have experience with pay-for-performance initiatives than organizations with only public ACO contracts, although they had similar levels of experience with other health reform efforts, including patient-centered medical homes, public reporting, and risk-based contracts.

Similar results were observed when comparing the capabilities of ACOs that indicated they bear financial risk as part of their ACO contracts compared with those that do not. Along with more experience with pay-for-performance initiatives (97% vs 85%), risk-bearing ACOs also had significantly more experience with various other healthcare reform efforts, including public reporting (92% vs 80%) and risk-based contracts (83% vs 55%).

This work provides some of the first empirical evidence on the nature of private payer ACO contracts, the characteristics of ACOs with private payer contracts, and how those organizations compare with other ACOs. ACO contracts are relatively new, with Medicare initiating pilots in 2005 and broad programs in 2012, and private payers piloting contracts beginning in 2009.

Among private contracts, only the Alternative Quality Contract (a Blue Cross Blue Shield program in Massachusetts) has been described in detail publicly. Our results show that private payer ACO contracts are varied in nature with different types of incentive programs in place. The single most common type of commercial contract is upside shared savings only (41%) with no risk for overspending; although when one combines all types of risk bearing (shared savings with downside risk, global budget, and capitation), the majority of commercial-payer ACOs are under contracts that include downside risk. Most contracts reward quality by basing financial rewards (ie, shared savings) on quality. Additionally, unlike Medicare ACO contracts, most commercial-payer ACO contracts include upfront payments, typically in the form of care coordination or care management payments.

In addition to shedding light on the nature of private payer ACO contracts, this work examines the types of ACOs that currently have private payer contracts and how they compare with ACOs with only public payer contracts. Public ACO programs, in particular Medicare, are the largest force in the ACO movement, with 80% holding an ACO contract with Medicare or Medicaid, compared with only 50% holding an ACO contract with a private payer. Medicare contracts may be most common for a variety of reasons: our results show ACOs with commercial contracts may have more care management and information technology capabilities than ACOs with only Medicare and/or Medicaid ACO contracts, suggesting that less advanced organizations may first pursue public ACO contracts. Medicare contracts may also be perceived as more straightforward. Rather than involving a negotiation with a commercial payer, joining the Medicare Shared Savings Program involves submitting an application. The program’s prespecified quality measures and financial benchmarking may be more manageable for provider organizations to understand and do not require them to negotiate on complex or unfamiliar terms. In addition, anecdotal evidence suggests that provider organizations may view the anti-trust protections under Medicare ACOs as an important benefit. Finally, it is possible that organizations more tentative in their approach to payment reform are using the Medicare Shared Savings Program to gain experience without being responsible for downside risk.

Our analysis faces some limitations. First, our survey population includes only those ACOs with private contracts for which some information was publicly available, as described in the methods. Organizations that have private payer ACO contracts that are confidential or have no public information (and the ACO has no public payer ACO contracts) were not included in our survey population. Therefore, we had no way to determine the true population of commercial ACOs. Second, the private contracts described in this analysis represent the largest commercial ACO contract per organization and our analysis of contract characteristics was limited to 1 commercial contract per organization. If larger ACO contracts are systematically different from smaller contracts, then our results on the nature of commercial payer ACO contracts may be biased (eg, if smaller contracts tend to less often include downside risk). Finally, our analysis is limited to ACOs that were established as of August 2012. The field of ACOs is rapidly developing, and continuing research on ACO contracts is necessary to understand how contracts are developing and changing.

Medicare has offered 2 programs that have attracted a large number of ACOs in the first 2 years. With 32 organizations starting in the Pioneer Program and 220 entering in the Medicare Shared Savings Program in 2012 and 2013, Medicare is leading the field in the move away from traditional fee-for-service payment. In fact, the Pioneer program requires that greater than 50% of the ACO’s patients must be covered by risk-based contracts in the first few years, necessitating changes in commercial contracts. While more organizations are testing the water with a Medicare Shared Savings Program contract with upside shared savings only, commercial contracts are more likely to include downside risk, which some have hypothesized will produce larger reductions in cost growth. However, we do not know, for example, if risk bearing will be more important than the proportion of patients an organization has under ACO contracts.

Medicare has been relatively flexible in terms of the types of providers it accepts into the Shared Savings Program, and has allowed organizations to join that have only upside potential. These program characteristics may influence participating providers. It may be that commercial ACO contracts will gain momentum in the next few years as payers and providers gain experience with these contracts. Researchers should continue to track the evolution of ACO contracts and eventually measure the relationship between contract characteristics and performance.

As performance information becomes available, it will be important to understand nuances in contract design and association with differences in cost and quality. Learning across Medicare, Medicaid, and commercial contracts can allow each to learn from one another and iteratively work toward the best possible contracts. This information will enable public and private payers to learn which types of contracts and incentives work best in different contexts and how to improve ACO contracts.

 From The Dartmouth Institute for Health Policy & Clinical Practice (VAL, CHC, WLS, ESF), Department of Community and Family Medicine (ESF), Department of Medicine (ESF), Geisel School of Medicine at Dartmouth, Hanover, NH; Division of Health Policy and Management, School of Public Health and Haas School of Business (SMS), Berkeley, CA; Department of Health Policy and Management, Yale School of Public Health, New Haven, CT (WLS).

 This work was supported by a grant from The Commonwealth Fund (#20130084).

Dr Lewis received 2 grants on accountable care organization (ACO) evaluation and spoke at the New England Society for Vascular Surgery and Michigan Hospital Association on ACOs. Drs Colla, Shortell, and Fisher and Mr Schpero do not report any relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (VAL, CHC, SMS, ESF); acquisition of data (VAL, CHC, ESF); analysis and interpretation of data (VAL, CHC, WLS, SMS); drafting of the manuscript (VAL, CHC, WLS, ESF); critical revision of the manuscript for important intellectual content (CHC, WLS, SMS, ESF); statistical analysis (VAL, CHC, WLS); provision of study materials or patients; obtaining funding (VAL, CHC, SMS, ESF); administrative, technical, or logistic support (CHC, WLS); and supervision (CHC).

Valerie A. Lewis, PhD, 35 Centerra Parkway, Lebanon, NH, 03766. E-mail: 

1. Lewis VA, Colla CH, Carluzzo KL, Kler SE, Fisher ES. Accountable care organizations in the United States: market and demographic factors associated with formation. 

2. Muhlestein DB, Croshaw AA, Merrill TP. Risk bearing and use of fee-for-service billing among accountable care organizations. 

3. Accountable care organizations now serve 14% of Americans. Oliver Wyman; 2013.

4. Auerbach DI, Liu H, Hussey PS, Lau C, Mehrotra A. Accountable care organization formation is associated with integrated systems but not high medical spending. 

5. McGinnis T, Small D. Accountable care organizations in Medicaid: emerging practices to guide program design. Hamilton, NJ: Center for Health Care Strategies; 2012.

6. Larson BK, Van Citters AD, Kreindler SA, et al. Insights from transformations under way at four Brookings-Dartmouth accountable care organization pilot sites. 

Articles

The authors find 51% of accountable care organizations have private payer contracts, which are more likely than public contracts to include downside risk and upfront payments.

ACO Contracting With Private and Public Payers: A Baseline Comparative Analysis

To explore the feasibility of using a distributed data model for ongoing reporting of local healthcare spending, specifically to investigate the contribution of utilization and pricing to geographic variation and trends in reimbursements for commercially insured beneficiaries younger than 65 years.

Commercial claims were obtained for beneficiaries in 5 states for the years 2008 to 2010 using a distributed data model. Claims were aggregated to the hospital service area (HSA) level and healthcare utilization was quantified using a novel, National Quality Forum—endorsed measure that is independent of price and allows for the calculation of resource use across all services in standardized units. We examined trends in utilization, prices, and reimbursements over time. To examine geographic variation, we mapped resource use by HSA in the 3 states from which we had data from multiple insurers. We calculated the correlation between commercial and Medicare reimbursements and utilization. Medicare claims were obtained from the Dartmouth Atlas.

We found that much of the recent growth in reimbursements for the commercially insured from 2008 to 2010 was due to increases in prices, particularly for outpatient services. As in the Medicare population, resource use by this population varied by HSA. While overall resource use patterns in the commercially insured did not mirror those among Medicare beneficiaries, we observed a strong correlation in inpatient hospital use.

This research demonstrates the feasibility and value of public reporting of standardized area-level utilization and price data using a distributed data model to understand variation and trends in reimbursements.

This study explored the use of a distributed data model and a novel method for calculating utilization to track local trends in spending for commercially insured beneficiaries.

Recent growth in spending for commercially insured beneficiaries was due principally to increases in prices, rather than increases in utilization.

Commercial utilization and spending varied across local areas and was not highly correlated with Medicare utilization and spending.

A distributed model may allow for nationwide reporting of spending and utilization to track the local effects of healthcare reform efforts.

Timely, local data are important to policy-makers, providers, patients, payers, and employers working to slow the growth of healthcare spending, which is a major focus of federal, state, and local healthcare reform initiatives. Community-based multistakeholder coalitions have formed across the country in an effort to influence their local healthcare markets and reduce costs. More than 40 percent of people in the United States live in a community with a multistakeholder coalition aimed at improving health and healthcare, including collaboratives focused on improving the exchange of health information, accelerating engagement by key local opinion leaders and stakeholders, or promoting quality improvement.

 All of these entities, however, lack the local data needed to determine if their efforts are making a difference.        

The factors contributing to rising healthcare spending differ across communities and depend on local context; understanding the drivers of local spending growth is complicated by the variety of inputs. Provider culture and supply, various market segments (outpatient, inpatient, long-term care), payer mix, regulation, and the competitiveness of hospital and physician markets all affect pricing, utilization, and ultimately, the total cost of care.

Research has shown that the relative contribution of these factors varies across markets and that drivers of commercial spending are not necessarily the same as drivers of Medicare spending. Chernew et al found that commercial spending was not correlated with Medicare spending across hospital referral regions.

 Examining the commercial and Medicare populations for El Paso and McAllen, Texas, Franzini et al found there was 86% greater per capita Medicare spending in McAllen than El Paso, but 7 percent less commercial spending.

 A recent Institute of Medicine report found that regional variation in spending in the commercial insurance market is due in large part to differences in price markups by providers. Differences in utilization, however, still explained over 30% of the regional variation in spending.

These diverse results indicate that the factors that contribute to rising healthcare spending are multifactorial and highlight the need for more comprehensive and detailed analysis of commercial data. The 2013 report issued by the Institute of Medicine, Variation in Health Care Spending: Target Decision Making, Not Geography, was unprecedented in that it combined both Medicare and commercial data from a variety of proprietary sources for analysis. The report underscored the importance of available data on both commercial and Medicare utilization and prices at a local level. These data could be useful to engage local stakeholders (such as employers), identify areas of potential waste, point regulators to localities where monopolistic pricing may be occurring, and evaluate the impact of local and national reform initiatives.

There is substantial uncertainty about the best approach to make local data publicly available. Feasible options might include a single federal database,

 combining state all-payer data (currently available in Colorado, Kansas, Maine, Maryland, Massachusetts, Minnesota, New Hampshire, Tennessee, Utah, and Vermont),6 using private data aggregators, or relying on a distributed data model.

 The Health Care Cost Institute has combined data from 4 large commercial insurers and has published broad reports on trends in employer-sponsored insurance, but has not produced any data at a local level.

 There are some community-level efforts under way to track healthcare spending and utilization locally, most notably in California, but they are relatively rare.

 In spite of the Institute of Medicine’s call for making more and better data available on both Medicare and commercial populations, it is not clear how this might be done.

In this paper we explore the feasibility and value of 2 potential approaches for making commercial spending and utilization data available at a local level. First, we work with an all-payer data set for New Hampshire, Maine, and Vermont, aggregated by Onpoint Health Data, a private, nonprofit organization. Second, we use a distributed data model to aggregate data from a single payer in 2 states. Our methods include a standardized approach to measuring utilization, and our partners submitted utilization and spending reports that were stripped of protected health information. These reports allowed us to aggregate data at a hospital service area (HSA) level and adjust for demographic characteristics. The findings demonstrate the feasibility of each of these approaches and the resulting data highlight the potential utility of tracking local healthcare spending.

  To test the distributed data model, we used data from 3 distinct sources. We obtained data on utilization and reimbursements for commercial beneficiaries in Maine, New Hampshire, and Vermont from an all-payer data set (2008-2010), along with data on beneficiaries of Blue Cross Blue Shield of Michigan (2009-2010) and Texas (2008- 2010) plans. The latter 2 states were chosen because of existing research relationships with relevant data providers. Each of the participating analytic teams applied standard software to measure utilization and submitted reports detailing summary information for beneficiaries in each age and gender category for each HSA. HSAs were defined in previous work by identifying the zip codes where the highest proportion of Medicare beneficiaries received their care from a single hospital.

 Data were then aggregated by the study authors and compared with corresponding HSA data from the Medicare program.

Each participating organization applied a standardized approach for defining the commercial population to be included in the analysis. Beneficiaries were required to be enrolled for a minimum of 9 months during the year, unless the member was born during the calendar year or was older than 65 years by the end of the year.

  Participating health plans and data providers used software developed by HealthPartners, a nonprofit healthcare organization and health plan, to measure a utilization amount associated with each insurance claim. The HealthPartners algorithm quantifies utilization using Total Care Relative Resource Values (TCRRVs), which are the basis for the National Quality Forum (NQF)-endorsed Total Resource Use measure.

 TCRRVs, which are expressed in dollars, measure the utilization and intensity of the services delivered to manage a patient’s healthcare needs. We chose to use the HealthPartners measure because it is independent of price and allows for the weighting and calculation of resource use across all medical services in standardized units. We did not want to obscure the effect of regional price differences on overall reimbursement amounts. In addition, we chose to use the measure because it makes resource use amounts equivalent for services offered across multiple settings. 

TCRRVs are unique in that they are relative within and across components of care (inpatient, outpatient, professional, and pharmacy), which allows for the isolation of resource use not only by component, but also on a total, per capita basis. The TCRRVs are made relative within the components of care using the CMS weighting system, including Medicare Severity Diagnosis Related Groups for inpatient care, Ambulatory Payment Classifications for outpatient care, and Relative Value Units for professional office care, while the TCRRVs for pharmaceutical expenditures are made relative by using the median average wholesale price per day for each National Drug Code. TCRRVs are calculated by multiplying the CMS weight by national average paid amount and the relevant service count. While the Health Care Cost Institute also uses CMS weights to measure the intensity of care, its methodology does not involve reporting (1) a resource use amount based on both utilization and intensity, or (2) a measure of total resource use across service lines.

The TCRRV algorithm was applied at the claim level by each data contributor and aggregated at the HSA level for transmission to study authors. (TCRRV weights are updated annually to correspond to updates in the CMS weight files. We used the 2011 TCRRV national weights.) Resource use was capped at $100,000 for each beneficiary in a given year and all components of care were reduced proportionally for both reimbursements and resource use. Very large resource use amounts greatly skew the mean in populations where the majority of beneficiaries have very low utilization and often represent extreme, unavoidable events. Capping reduced average resource use in 2008-2010 by an average of 11% across all HSAs (9.3% and 13.6% for HSAs in the lowest and highest quintile of resource use, respectively).

Both reimbursement and resource use (ie, utilization) amounts for each component of care (inpatient, outpatient, professional, and pharmacy) were transmitted to the study authors, as were denominator data (age in groups, gender, HSA of residence, number of people, and number of person-years). The HealthPartners software performs quality control checks at the claim level by comparing calculated resource use with the reimbursement amount. If the calculated resource use for a particular claim was outside of normal limits, we imputed resource use using the reimbursement amount multiplied by the ratio between resource use and reimbursement amount for all normal claims from that state, year, and component of care. The combination of numerator and denominator data allowed for adjustment and rate calculations. The transmitted data also included the prevalence of prescription drug and mental health carve-outs for each HSA. The Blue Cross Blue Shield of Michigan data did not include HSAspecific information on carve-out levels, but did include the overall state values by year. We used this information and exploratory models from the other states to impute prescription drug reimbursement and utilization values for Michigan HSAs. The imputation assumed a constant carve-out rate across the state each year.

Age, gender, and carve-out adjustment was performed using linear regression across the 5 states. Weighted averages for each HSA were calculated using the adjusted values for each year and component of care. State averages were created by weighting by the number of beneficiaries in each HSA. HSA average relative prices were defined as the ratio of resource use to the reimbursement amount. We normalized relative prices to 1 across all 5 states. Only HSAs with a sum of more than 1000 commercial beneficiaries over the study period were included. Analysis was also limited to those HSAs that were either in 1 of the 5 target states or overlapped the border of 1 of the 5 target states. These 2 exclusion factors reduced the total number of people per calendar year in the analyses by less than 0.1%. 

To examine geographic variation, we mapped resource use for each HSA in Maine, New Hampshire, and Vermont, reflecting procedures and services offered across all 4 components of care. We divided the HSAs into quintiles to display differences in resource use. Maps were not generated for Michigan or Texas, because data were from a single insurer in each state.

To further validate our methodology by attempting to replicate previous findings by examining the association between Medicare and commercial reimbursements and utilization. Age-, sex-, and race-adjusted reimburse- ment and utilization data at the HSA level for the feefor- service Medicare program were downloaded from the Dartmouth Atlas website.

 Utilization data were adjusted for prices across areas and settings.

 Using these data, we examined the association between calculated resource use and reimbursements for the commercially insured and Medicare (aged ≥65 years) populations by calculating a correlation coefficient and plotting the relationship, weighting the regression and the size of the data point by the sum of the number of commercial and Medicare beneficiaries in the HSA.

We identified 388 HSAs and a total of 16,819,237 beneficiary years for inclusion in our analysis (Table 1). The average number of commercial beneficiaries across 2008- 2010 included in the data set for each HSA ranged from 337 to 296,555. The majority of the beneficiaries in each of the 5 states were female. The age distribution was similar across the states, with about half of the population under 40 years and half aged 40 to 65 years. The only exception was Texas, where the population was slightly younger (a greater proportion in the 18-to-39-year age group). The age distributions for our study cohort were similar to those reported by the United States Census Bureau for privately insured individuals in each state.

Trends in Commercial Beneficiary Resource Use,

We examined trends in resource use and relative prices to investigate the extent to which changes in those 2 factors affected changes in reimbursements. The top portion of Figure 1 shows the year-to-year resource use trend per beneficiary by component of care and state. Profes- sional services constituted the highest proportion (42.9%) of resource use on average across the 5 states, followed by outpatient services (21.8%), inpatient services (20.0%), and pharmacy (15.1%). Annual resource use ranged from $4479 per commercial beneficiary in Vermont in 2008 to $5324 per beneficiary in Texas in 2009. The bottom portion of the figure shows calculated changes in resource use, prices, and reimbursements from year to year by component of care. Overall increases in reimbursements were greater in 2008- 2009 than 2009-2010. Reimbursements increased each year in each state, with the exception of Maine in 2010. Total resource use went down in each state between 2009 and 2010, mostly due to declines in inpatient (hospital) services. Increases in prices were larger than the decreases in resource use, however, so reimbursements still increased. The changes in reimbursements in each state can also be compared with changes in the consumer price index (CPI) overall and for medical care. The CPI decreased 0.4% between 2008 and 2009 and increased 1.6% between 2009 and 2010. The medical care CPI increased 3.2% between 2008 and 2009 and 3.4% between 2009 and 2010.15 Overall, growth in prices for commercial beneficiaries in our data set exceeded overall inflation in each state and year. In all but 2 cases, our measure of price increases also exceeded medical inflation.

Variation in Commercial Beneficiary Resource Use,

Resource use, relative prices, and reimbursement amounts varied among commercial beneficiaries across HSAs within our 5 states, and across states (Table 2). Resource use among commercial beneficiaries ranged from $3041 to $6280 across HSAs in the 5 states, which compares to a range of $6322 to $15,049 among Medicare beneficiaries. There was less variation in the commercial population (coefficient of variation = 0.10) than the Medicare population (coefficient of variation = 0.17). Within-state variation in resource use was most marked in Michigan. Relative prices ranged from 0.83 to 1.35 across HSAs in the 5 states, and reimbursements ranged from $1938 to $4626. Relative prices under 1 indicate that, for a given HSA, prices were below the average across all HSAs in the 5 states. The correlation between prices and resource use across HSAs was low (P = .03). 

Variation in resource use is further illustrated in Figure 2, which maps the ratio of resource use in each New England HSA to the weighted average of resource use across the region. Those HSAs with higher resource use relative to the regional average are darker in color. The HSAs with the highest utilization in this area were Rochester, New Hampshire; Ellsworth, Maine; and Augusta, Maine. The HSAs with the lowest utilization in this area were Rumford, Maine; Colebrook, New Hampshire; and St. Johnsbury, Vermont.

Overall resource use in the commercial population was not correlated with resource use in the Medicare population at the HSA level across these 5 states (P = .10, Figure 3), but the correlation across HSAs for inpatient resource use was higher (P = .42). A negative correlation was observed for overall commercial and Medicare reimbursements (P = —.36) and a low correlation for inpatient reimbursements (P = .10).

  This research combined data from 2 private payers and an all-payer data set to produce standardized estimates of utilization, pricing, and reimbursements at the local level across 5 states using a distributed data model. We were able to calculate HSA-level trends and variation for the commercially insured population aged <65 years in the select states using a standardized, NQF-endorsed measure of utilization. Our findings revealed marked variation in both relative prices and utilization, consistent with work by Dunn et al and Baker et al, and confirmed that commercial trends do not necessarily mirror trends in the Medicare population, as previously found by Chernew et al and the Institute of Medicine.

The goal of this research is to help communities understand local healthcare spending patterns and to begin to develop tailored solutions to slow growth in healthcare spending. This line of research differs from previous attempts to examine healthcare costs within the commercially insured population in several significant ways. First, it uses a validated, publicly available measure of utilization to calculate and standardize the magnitude of healthcare resource use across various commercially insured populations and service lines (outpatient, inpatient, professional, and pharmacy), taking into account both the frequency and intensity of care. This measure is independent of price and thus can be used to compare healthcare utilization for different health plans in different parts of the country. Second, we conducted our analysis at the level of the HSA, a relatively small unit that describes the geographic area where patients served by local hospitals reside. Finally, by using a distributed data model, this research demonstrates the feasibility of an approach that could be used either by local multistakeholder initiatives or by a national effort to report on local utilization and spending.

The main limitation of this study stems from it being pilot research: the number of payers included in our analn- ysis for each state varied significantly (61 in Maine, 37 in New Hampshire, 35 in Vermont, and 1 each in Michigan and Texas), along with the insurance products offered by those payers, which reduces comparability across states. The use of a distributed data model presents its own set of potential limitations, since no part of the team had access to claim-level data for all of the states. To address this issue, we created quality control reports to ensure that outputs were accurate and standardized across sites. 

Another limitation was our reliance on risk adjustment using solely age and gender indicators, rather than risk adjustment that included other demographic characteristics (income, education, etc) or information on medical conditions. Diagnosis-related risk adjustment has practical implications and may introduce additional biases

 we sought to demonstrate the viability of the distributed data model before compounding the complexity and cost of data aggregation by implementing a risk adjustment methodology. In future research, we plan to compare results using various risk adjustment methods. Lastly, a potential limitation to this study was our use of HSAs as the unit of analysis. HSAs were defined by Medicare utilization patterns in the 1990s and may not fully capture utilization patterns among commercial beneficiaries.

Key factors contributing to the success of our pilot included the generation of quality control reports for data providers and the data aggregator to ensure data accuracy, as well as the use of mock commercial claims to test aggregation algorithms. While it is possible to apply our methodology using other levels of analysis, we continue to believe that HSAs represent the best option as they, unlike counties, reflect actual patterns of healthcare use, and likely provide more actionable information for community- level interventions than hospital referral regions.

Routine reporting of timely data at a local level can provide actionable information to communities hoping to understand the sources of healthcare spending and growth. This information will be useful for many purposes: to engage employers in reducing healthcare costs, encourage accountability for prices and utilization among providers, encourage community-level shared savings programs (such as the Akron Accountable Care Collaborative

), and facilitate research and evaluation of regional and national reforms. In addition, as consolidation in physician and hospital markets increases, these data can support transparency among health systems and aid regulators in monitoring the effects of changes in payment incentives that might drive consolidation, such as accountable care organizations or bundled payment reforms.

This research provides a unique avenue to develop a nationwide, comprehensive commercial data set in which payers create measures of cost and utilization from their own claims using standard practices, with controls for data quality and consistency. In addition, it allows payers to avoid releasing protected health information (in which patients may be identified) or pricing data, which is commercially valuable and possibly subject to antitrust concerns. The goal is for both large and small payers to contribute to an all-inclusive national data set for public reporting. The barriers to this approach are not trivial: developing it requires the cooperation and effort of multiple competitive payers, as well as entrusting the protection of pricing information to a third party.

The authors are indebted to Sarah Kler of The Dartmouth Institute for Health Policy and Clinical Practice for assistance with mapping, as well as Greg Kotzbauer and Andrew Toler of The Dartmouth Institute, Robyn Rontal of Blue Cross Blue Shield of Michigan, and Marianne Udow-Phillips of the Center for Healthcare Research and Transformation for their insights and advice.

In addition, the authors would like to acknowledge the following entities as the original sources for the New Hampshire and Vermont data sets: the New Hampshire Department of Health and Human Services; the New Hampshire Insurance Department; the Vermont Department of Financial Regulation (formerly the Vermont Department of Banking, Insurance, Securities, and Health Care Administration); and the Green Mountain Care Board. The analyses and conclusions drawn from these data do not necessarily represent those of any agency of the state of New Hampshire or state of Vermont.

The Dartmouth Institute for Health Policy and Clinical Practice (CHC, WLS, DJG, ABM, ESF), the Department of Medicine (ESF), and the Department of Community and Family Medicine (ESF), Geisel School of Medicine at Dartmouth, Lebanon, NH; Blue Cross Blue Shield of Michigan, Detroit, (PGA); Center for Healthcare Research and Transformation, Ann Arbor, MI (NB); Onpoint Health Data, Portland, ME (KF, RS); Division of Management, Policy and Community Health, University of Texas School of Public Health, Houston (LF, RP); and HealthPartners, Inc, Bloomington, MN (GK, SK). Funding Source: This work was funded by grant 69088 from the Robert Wood Johnson Foundation.

The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (CHC, DJG, ABM, GK, SK, ESF); acquisition of data (CHC, DJG, ABM, PGA, NB, KF, LF, GK, SK, RS, ESF); analysis and interpretation of data (CHC, WLS, DJG, PGA, KF, LF, GK, SK, RP, RS); drafting of the manuscript (CHC, WLS, ABM, GK, SK, ESF); critical revision of the manuscript for important intellectual content (CHC, WLS, DJG, ABM, NB, LF, GK, SK, ESF); statistical analysis (CHC, WLS, GK, SK, RP); provision of study materials or patients (CHC); obtaining funding (CHC, ESF); administrative, technical, or logistic support (CHC, WLS, DJG, PGA, KF, GK, SK, RP, RS); and supervision (CHC, ESF).

Carrie H. Colla, PhD, 35 Centerra Pkwy, Lebanon, NH 03766. E-mail: carrie.h.colla@dartmouth.edu.

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The authors demonstrate the utility of distributed data models for reporting of local trends and variation in utilization, pricing, and spending for commercially insured beneficiaries.

William L. Schpero, MPH

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