Erin Trish, PhD

 To quantify the proportion of health plan spending on services for which surprise billing is common—provided by radiologists, anesthesiologists, pathologists, emergency physicians, emergency ground ambulances, and emergency outpatient facilities—and estimate the potential impact of proposed policies to address surprise billing on health insurance premiums.

 Analysis of 2017 commercial claims data from the Health Care Cost Institute, comprising 568.5 million claims from 44.8 million covered lives in 3 large US insurers: UnitedHealthcare, Aetna, and Humana.

 We calculate the share of total health plan claims spending attributable to ancillary and emergency services. Next, we estimate the premium impact of proposed federal policies to address surprise billing, which, by removing provider leverage stemming from the ability to surprise-bill, could reduce in- and out-of-network payments for these services, in turn affecting premiums. Specifically, we model the premium impact of reducing payment for these services (1) by 15% and (2) to 150% of traditional Medicare payment rates.

More than 10% of health plan spending is attributable to ancillary and emergency services that commonly surprise-bill. Reducing payment for these services by 15% would reduce premiums by 1.6% ($67 per member per year), and reducing average payment to 150% of traditional Medicare rates—the high end of payments to other specialists—would reduce premiums by 5.1% ($212 per member per year). These savings would reduce aggregate premiums for the nation’s commercially insured population by approximately $12 billion and $38 billion, respectively.

 Addressing surprise billing could substantially affect commercial insurance premiums.

Proposed surprise-billing legislation would not only protect patients from unexpected out-of-pocket expenses, but also likely affect negotiated payments for the ancillary and emergency services that generate most surprise bills.

More than 10% of commercial health care spending is attributable to services for which surprise billing is common: those provided by radiologists, anesthesiologists, pathologists, emergency physicians, emergency ground ambulances, and emergency outpatient facilities.

Eliminating provider leverage stemming from the ability to surprise-bill could reduce commercial insurance premiums by as much as 5.1%, or $212 per member per year. This could reduce aggregate premiums by approximately $38 billion for the nation’s commercially insured population.

Surprise medical bills, which can occur in emergencies or when patients at in-network facilities are treated by out-of-network professionals, have drawn increasing public and policy attention. Although the main focus has been protecting patients from receiving these unfair—and potentially large—surprise bills, policies addressing surprise billing may affect health insurance premiums more broadly.

Most surprise bills are generated by emergency and ancillary providers, such as radiologists, anesthesiologists, pathologists, emergency physicians, emergency ground ambulances, and emergency outpatient facilities.

 Unlike most medical services, for which patients have an opportunity to seek in-network providers, patients generally are not able to choose these emergency and ancillary providers. As a result, these providers can often remain out of network without significantly reducing their patient volume. Not only can this lead to patients receiving surprise bills, but evidence suggests that the ability to surprise-bill creates leverage that enables these providers to obtain higher in-network payments.

 For example, in a letter to Congress, the CEO of TeamHealth, a large emergency physician staffing company, plainly acknowledged that “balance billing…is a contract leveraging tool.”

The expense of these high in-network payments is passed on to consumers and taxpayers through the cost of health insurance premiums. Therefore, although the surprise bills themselves are burdensome to individual patients, the higher in-network payments resulting from this leverage have a broader impact on total health care spending for consumers.

A federal policy eliminating surprise bills would influence in-network payments for this subset of providers and, in turn, influence health insurance premiums. To assess the impact of a federal surprise billing law on premiums, it is critical to quantify the share of total health plan medical claims spending attributable to the subset of services most likely affected, for which only partial estimates are available in the existing literature (to our knowledge).

In this study, we evaluate the proportion of total medical health plan spending on ancillary and emergency services with the highest prevalence of surprise billing. This includes services performed by emergency medicine professionals, radiologists, anesthesiologists, and pathologists (ERAP), as well as emergency outpatient facilities and emergency ground ambulance services. Then, we estimate the impact that potential changes in payments for these services would have on health insurance premiums.

We analyze 2017 commercial claims data from the Health Care Cost Institute (HCCI), comprising 568.5 million claims from 44.8 million covered lives (equating to 36 million member-years) in 3 of the 5 largest US insurers: UnitedHealthcare, Aetna, and Humana.

 More than 90% of observed claims were in network, although there is variation across these provider types (

]). We compute total plan-paid spending on professional, facility, and pharmaceutical services per member per year. We then calculate plan-paid spending attributable to ERAP professional services, identified using 2 methods: (1) HCCI provider categories and (2) Current Procedural Terminology (CPT) codes generally billed by these specialties.

 We also calculate the share of total plan-paid spending per member per year on outpatient emergency facility claims and emergency ground ambulance services identified by CPT codes, as recent evidence suggests that these services would be affected by surprise-billing legislation.

 Then, we estimate the impact of 2 potential changes in payments for ERAP, outpatient emergency facility, and emergency ground ambulance services on insurance premiums. We assume a baseline medical loss ratio of 90% in these estimations to align with the average ratio among large group plans, and that nonmedical spending is unchanged by policies.

First, we analyze the impact of a 15% reduction in average payments for these services, in line with the Congressional Budget Office–estimated effects of surprise-billing legislation proposed by the Senate Health, Education, Labor, and Pensions Committee.

 This proposed legislation would prohibit surprise billing and require minimum out-of-network payments equal to a health plan’s median in-network payments for similar services in the same geographic region.

Second, we estimate the effects of a policy that would have the effect of reducing payment rates for ERAP professionals and emergency service providers to 150% of Medicare’s reimbursement rates for the same service. We choose this level because it is roughly the upper bound of average markups for specialists not commonly involved in surprise billing.

 In contrast, multiple studies estimate average in-network payments at roughly 300% of Medicare for emergency physicians, 200% of Medicare for radiologists, and 350% of Medicare for both anesthesiologists and pathologists.

 Medicare payments are intended to reflect the relative cost of providing care, and excess commercial markups observed among ERAP professionals and emergency service providers may be driven by their ability to surprise-bill. Therefore, we expect that a policy such as network matching

—by eliminating provider leverage stemming from the ability to surprise-bill—could result in negotiated payments with markups over Medicare more similar to these other types of specialists. Although it is impossible to predict the precise magnitude of prices resulting from these policies, we estimate the premium impact of reducing average payments for these services to 150% of Medicare payments.

In this analysis, we do not compute payments as a fraction of Medicare’s payment rates for each service in our data but, rather, use estimates from the literature described previously. For example, we assume that such a policy would reduce spending on radiology services by one-fourth, from 200% to 150% of Medicare’s rates. For emergency ground ambulance services, we estimate that commercial plans currently pay approximately 170% of Medicare’s rates (

). We assume that emergency facility payments would face the same reduction as emergency physician payments.

We find that ERAP professional services account for 7.0% to 8.6% of total plan spending in 2017, depending on the definition (

). Outpatient emergency facility claims account for an additional 3.1% of plan spending, and emergency ground ambulance services comprise 0.3% of plan spending. Average annual plan spending per member-year is $4161 among plans in our data set, with approximately $290 to $357 spent on ERAP professional services, $127 spent on emergency facilities, and $13 spent on emergency ground ambulances.

We estimate that a 15% reduction in payments to ERAP professionals corresponds to a 0.9% to 1.2% (approximately $37-$50 per member-year) reduction in annual premiums per enrollee (

). If average in-network payments for ERAP professionals declined to 150% of Medicare rates, premiums would decline by 3.1% to 3.6% (approximately $129-$150 per member-year), all else staying equal.

Including emergency facilities and emergency ground ambulance services, the estimated premium reduction would increase to 1.4% to 1.6% (approximately $58-$67 per member-year) under the 15% payment reduction scenario and 4.5% to 5.1% (approximately $187-$212 per member-year) if payments declined to 150% of Medicare. Applying these premium reductions to the US commercially insured population (approximately 177 million individuals),

 we estimate that 1.6% and 5.1% reductions in insurance premiums would yield a total savings of $12 billion and $38 billion, respectively.

The financial burden of surprise bills for individual patients has captured public attention, but this study illustrates that provider leverage derived from the ability to surprise-bill has broader effects on health care spending—resulting in commercial health insurance premiums as much as 5% higher than they otherwise would be in the absence of this market failure. Our estimates are roughly in line with others in the literature.

This growing body of research indicates that policies to address surprise billing would address an important market failure that has enabled emergency and ancillary services providers to command high payment rates. Removing the ability to surprise-bill patients reduces leverage for this subset of providers in their payment negotiations with health plans. Several federal proposals employ payment standards or arbitration processes that additionally require insurers to make a minimum payment to out-of-network providers. Other policy approaches would instead address surprise medical bills by imposing “network matching” requirements, whereby emergency, ancillary, and similar professional services delivered through in-network facilities could no longer be billed on an out-of-network basis; in turn, providers of such services would have to negotiate payment with the facility and/or health plans that the facility accepts, with the ability to surprise-bill now eliminated.

The net premium impact of any proposal would depend on the regulatory approach and generosity of any required minimum payments from health plans to out-of-network providers. If a policy were to base payment standards on something that is generally higher than payment rates today, such as provider charges, it could actually increase health insurance premiums.

These findings may not be generalizable beyond the 3 insurers we study, and we do not describe variation in effects across local markets. We may misattribute services to ERAP professionals, although consistent findings using 2 approaches suggest robustness. We analyze only 2 potential policy scenarios within a wider range of approaches.

Although economic theory lends some intuition to the effect that different policies might have, the precise effects of different approaches on provider prices are highly uncertain and we do not account for potential secondary effects such as provider consolidation. There is also some uncertainty surrounding status quo commercial payment levels. Additional specialties may be affected by surprise-billing legislation, which would further magnify premium impacts. For example, legislation may affect inpatient facility spending incurred by patients admitted through the emergency department, but we conservatively exclude such spending from our calculations.

Providers affected by surprise-billing regulation—ERAPs, emergency outpatient facilities, and emergency ground ambulances—comprise more than 10% of total plan spending. Policies addressing surprise billing can meaningfully influence commercial insurance premiums. A well-designed policy to address this market failure could therefore reduce consumer health care spending.


 Leonard D. Schaeffer Center for Health Policy and Economics at University of Southern California (ELD, BL, ET), Los Angeles, CA; USC-Brookings Schaeffer Initiative for Health Policy (ELD, LA, ET), Los Angeles, CA, and Washington, DC; Brookings Institution (LA, ET), Washington, DC; University of Southern California School of Pharmacy (ET), Los Angeles, CA.

 This work was funded by Arnold Ventures. The funding source had no involvement in the collection, analysis, and interpretation of data; in the writing of the manuscript; or in the decision to submit the article for publication.

 Dr Trish is a consultant to Cedars Sinai Medical Center and for a capital management firm, both outside the specific issue of this paper; has performed expert witness work for the Blue Cross Blue Shield Association and Premera, outside the specific issue of this paper; and has received speaking fees from MultiPlan. The remaining 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 (ELD, BL, LA, ET); acquisition of data (ET); analysis and interpretation of data (ELD, BL, LA, ET); drafting of the manuscript (ELD, BL); critical revision of the manuscript for important intellectual content (ELD, LA, ET); and obtaining funding (LA, ET).

 Erin L. Duffy, PhD, MPH, Leonard D. Schaeffer Center for Health Policy and Economics at University of Southern California, 650 Childs Way, Los Angeles, CA 90089. Email: eld_805@usc.edu.

1. Garmon C, Chartock B. One in five inpatient emergency department cases may lead to surprise bills. 

. 2017;36(1):177-181. doi:10.1377/hlthaff.2016.0970

2. Melnick G, Fonkych K. An empirical analysis of hospital ED pricing power. 

. 2020;26(3):105-110. doi:10.37765/ajmc.2020.41929

3. Adler L, Fiedler M, Ginsburg PB, et al. State approaches to mitigating surprise out-of-network billing. Brookings. February 2019. Accessed August 12, 2019. https://www.brookings.edu/wp-content/uploads/2019/02/Adler_et-al_State-Approaches-to-Mitigating-Surprise-Billing-2019.pdf

4. Cooper Z, Morton FS, Shekita N. Surprise! out-of-network billing for emergency care in the United States. National Bureau of Economic Research working paper 23623. July 2017. Updated January 2019. Accessed August 12, 2019. https://www.nber.org/papers/w23623

5. Duffy EL. Influence of out-of-network payment standards on insurer-provider bargaining: California’s experience. 

6. Murphy L. Re: bi-partisan workgroup’s request for data and information on surprise medical billing. March 13, 2019. Accessed May 1, 2019. https://www.documentcloud.org/documents/6568825-TeamHealth-Letter.html

7. Cooper Z, Nguyen H, Shekita N, Morton FS. Out-of-network billing and negotiated payments for hospital-based physicians. 

. 2020;39(1):24-32. doi:10.1377/hlthaff.2019.00507

8. Commercial claims data, 2014-2017. Health Care Cost Institute. Accessed September 3, 2019. https://www.healthcostinstitute.org/data

9. Current procedure codes. American Academy of Professional Coders. Accessed September 3, 2019. https://www.aapc.com/resources/medical-coding/cpt.aspx

10. Medical loss ratio. CMS. Accessed September 3, 2019. https://www.cms.gov/apps/mlr/mlr-search.aspx

11. S 1895, Lower Health Care Costs Act. Congressional Budget Office. July 16, 2019. Accessed August 12, 2019. https://www.cbo.gov/publication/55457

12. Pelech D. An analysis of private-sector prices for physicians’ services. Congressional Budget Office. January 2018. Accessed October 2, 2019. https://www.cbo.gov/system/files/115th-congress-2017-2018/workingpaper/53441-workingpaper.pdf

13. Biener AI, Selden TM. Public and private payments for physician office visits. 

. 2017;36(12):2160-2164. doi:10.1377/hlthaff.2017.0749

Report to the Congress: Medicare Payment Policy

. Medicare Payment Advisory Commission; 2017. Accessed September 3, 2019. http://medpac.gov/docs/default-source/reports/mar17_entirereport.pdf

15. Stead SW, Merrick SK. ASA survey results for commercial fees paid for anesthesia services—2018. 

16. Song Z. The pricing of care under Medicare for All: implications and policy choices. 

. 2019;322(5):395-397. doi:10.1001/jama.2019.8245

17. Adler L, Fiedler M, Ippolito B. Network matching: an attractive solution to surprise billing. 

. May 23, 2019. Accessed June 28, 2019. https://www.healthaffairs.org/do/10.1377/hblog20190523.737937/full/

18. Health insurance coverage of the total population, 2017. Kaiser Family Foundation. Accessed September 3, 2019. https://www.kff.org/other/state-indicator/total-population/?dataView=1&currentTimeframe=1&selectedDistributions=employer--non-group&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D

Articles

In addition to protecting patients from receiving unexpected bills, policies to address surprise billing could reduce health insurance premiums by 1% to 5%.

Policies to Address Surprise Billing Can Affect Health Insurance Premiums 

 To describe the number and availability of individual market plans sold by provider-owned insurers and compare differences in premiums between traditional and provider-owned insurers.

 Using the Robert Wood Johnson Foundation’s HIX Compare data, we identified insurers selling Affordable Care Act (ACA)—compliant policies in the individual market and identified those insurers owned by health systems by using information on their websites. We determined the number of insurers selling policies in each market and the size of the population living in areas where provider-owned insurers sold plans in 2016 and 2017. We used least squares regression to compare premiums between traditional and provider-owned insurers within markets, and we adjusted standard errors for clustering at the market and insurer level.

There were 149 insurers that sold ACA-compliant plans in 2017, of which 51 were provider owned. Provider-owned insurers operated in 208 of the 503 exchange markets. We estimate that about 62% of US residents (more than 170 million people) live in a market in which a provider-owned insurer sells plans. Premiums did not differ significantly between traditional and provider-owned plans in 2017.

 Provider-owned insurers play a prominent role in the individual insurance market. Although health systems that sell insurance have incentives to reduce costs, provider-owned insurers and traditional insurers have similar premiums.

By combining the provision and financing of insurance, provider-owned insurers have the potential to reduce costs. We describe the role of provider-owned insurers in the individual insurance market.

There were 149 insurers that sold Affordable Care Act—compliant plans in 2017, of which 51 were provider owned.

Provider-owned insurers operated in 208 of the 503 exchange markets.

About 62% of US residents (more than 170 million people) live in a market in which a provider-owned insurer sells plans.

Premiums did not differ between traditional and provider-owned plans in 2017.

Health policy experts have touted the benefits of integration between insurers and providers for over 40 years,

 but the model has been slow to catch on. Commercial insurers and Blue Cross Blue Shield plans have dominated the health insurance market. The federal government has tried to promote provider—insurer integration via accountable care organizations (ACOs). Although ACOs face limited downside risk, policy makers clearly hope to nudge the healthcare system to be more like Kaiser Permanente,

 a fully integrated delivery system that is often held up as a model of efficient, high-quality healthcare delivery.

In this paper, we describe the number and geographic reach of provider-owned insurers in the individual insurance market, and we compare premiums between provider-owned and traditional insurers. The individual market is of special concern for policy makers, given the exits of some prominent insurers from the exchange markets and subsequent reports of rising premiums. Previous analyses have focused on the role of provider-owned insurers in the Medicare Advantage market and the insurance market generally. Twenty percent of Medicare Advantage enrollees were in plans operated by provider-owned insurers in 2016, and 11 of 19 new Medicare Advantage insurers were provider owned.

 In 2013, there were 107 providers selling insurance that covered 18 million enrollees, including Medicaid managed care and Medicare Advantage enrollees.

Historically, it has been difficult to evaluate the claim that Kaiser Permanente

 and other provider-owned insurers have lower costs. Insurance premiums for employer-based insurance policies are not publicly available, and the lack of standardization of benefits makes it difficult to compare premiums even when they are available. Insurance claims data are an important source of information about healthcare spending generally, but either integrated systems do not release claims data or the data do not have encounter-level transaction prices.

The RAND Health Insurance Experiment remains the most rigorous comparison of costs between provider-owned and traditional insurers. Subjects were randomized to different tiers of fee-for-service health plans or Group Healthcare Cooperative of Puget Sound, a health maintenance organization. Costs were 28% lower in the Group Healthcare arm.

 found that provider-owned insurers had higher premiums in the Medicare Advantage market in 2009. However, premiums in the Medicare Advantage market are highly regulated. If a plan submits a bid below the benchmark, the plan receives a rebate that it must use to increase benefits. Half of the plans in the sample used by Frakt et al had $0 premiums.

The degree of standardization in individual policies brought about by the Affordable Care Act (ACA) offers a novel opportunity to compare premiums between provider-owned and traditional insurers. Premiums are publicly available, insurance products are standardized, and premiums depend on consumer characteristics to a limited degree and in ways that are observable. For plans sold on the exchanges, robust risk adjustment weakens the close correspondence between insurers’ profits and enrollee characteristics that exists in most other insurance markets.

 using 2016 data on premiums for plans sold on the exchanges, found that the minimum Silver plan premium in a market was $12 lower if 1 of the insurers participating in the market was provider owned. Their analysis had 1 observation per market.

 used 2016 data for plans sold on the exchanges in 30 states to compare premiums between provider-owned insurers and 5 categories of traditional insurers. They found that premiums for Blue Cross Blue Shield plans were similar to those for provider-owned insurers, but premiums for national commercial plans were $260 higher. Our study differs from theirs in a number of respects. We (1) use data covering all 50 states and Washington, DC; (2) compare premiums between provider-owned insurers and all other insurers, which we believe is a more interesting, policy-relevant comparison; (3) include market, versus state, fixed effects; (4) adjust standard errors for clustering of plans by market and by insurer; and (5) use 2017 data.

We used the Robert Wood Johnson Foundation’s 2017 HIX Compare data set (the February 2, 2018, version), compiled from insurers’ mandatory submissions to the CMS Health Insurance Oversight System. The data cover every plan sold on the exchanges, including plans sold on federally facilitated, state partnership, federally supported, and state-based Marketplaces, as well as off-exchange, ACA-qualified plans. The data cover every plan’s characteristics but do not include information on the number and characteristics of enrollees. Observations are at the plan-market level. Insurers sell multiple plans across and within markets.

We classified an insurer as provider owned if the insurer was owned by or shared a common owner with a hospital system or multispecialty physician clinic. (We hypothesized that insurers that owned only primary care clinics would not have sufficient leverage or control over the delivery of healthcare to substantially reduce costs.)

We identified provider-owned insurers by examining the About Us section of each insurer’s website. We looked for statements indicating that (1) the insurer was owned by a health system or (2) the insurer owned and operated hospitals and clinics. We then examined network directories to determine if the network included hospitals or physician clinics affiliated with the insurer. A research assistant classified each insurer, and then one of the authors reviewed each classification, using the same methods, to determine its accuracy. 

) contains a list of these insurers and their classifications.

We classified observations at the insurer rather than plan or product level. For example, UnitedHealthcare is a traditional insurer but had a recently closed subsidiary, Harken Health, that operated its own physician clinics. We classified both UnitedHealthcare and Harken Health as traditional insurers because only a small share of the United plans were sold under the Harken brand and because United may have used the profits from Harken to cross-subsidize its other products or vice versa.

Corporate names are used inconsistently in the HIX Compare data (for example, some plans sold by Harvard Pilgrim are associated with “Harvard Pilgrim”; others, with “Harvard Pilgrim Health Care”). We therefore recoded the carrier variable in the HIX Compare data so that all plans sold by an insurer had a common code. This step was necessary to correctly adjust standard errors for clustering of plans by insurer. We grouped all of the Blue Cross Blue Shield plans owned by Anthem, Inc, together. Plans sold by non-Anthem Blue Cross Blue Shield insurers were assigned distinct codes.

We assessed the presence and number of provider-owned and traditional insurers in each market (as defined by exchange rating areas). We merged the HIX Compare data with the Health Resources and Services Administration’s Area Resource File to determine the characteristics of the markets in which provider-owned insurers operate. We measured the association between market characteristics and the presence of at least 1 provider-owned insurer in the market using logistic regression.

We estimated differences in premiums between plans sold by provider-owned and traditional insurers using a correlated random-effects model.

 We restricted attention to each insurer’s lowest-cost Silver plan for an individual aged 50 years in each market. There is 1 observation per insurer per market.

Similar to a fixed-effects model, the correlated random-effects model estimates within-market differences in premiums. However, similar to a mixed-effects model, it also permits adjustment of standard errors for clustering at the market and insurer levels. Plans are nested in markets and in insurers, but neither markets nor insurers are nested in each other (they are “crossed” effects). A traditional fixed-effects model cannot accommodate the complex error structure. The model is of the following form:

 indicates whether the insurer is provider owned. Controlling for the market-level average of the share of provider-owned insurers, 

, ensures that the coefficient on the provider-owned insurer indicator, 

, is equivalent to an estimate from a fixed-effects model. The variables 

 represent insurer and market random effects, respectively.

Availability of Provider-Owned Insurance Plans

There were 149 insurers that sold ACA-compliant plans in 2017, of which 51 were provider owned based on our criteria. Provider-owned insurers operated in 208 of the 503 markets. 

 displays the number of provider-owned insurers in each market. Provider-owned insurers operate in geographically diverse areas in the United States, including the upper Midwest, the coastal West, and the mid-Atlantic. Five providers (CHRISTUS Health, Covenant Health, Memorial Hermann, Prominence Health, and Baylor Scott & White Health) sell insurance in Texas, a state not normally associated with delivery system innovation.

Between 2016 and 2017, 4 provider-owned insurers exited the ACA-compliant market entirely, 5 entered (for a net gain of 1), and 46 participated throughout both years. In the same time frame, 9 traditional insurers exited, 3 entered (for a net loss of 6), and 95 participated throughout both years. The mean number of insurers (of both types) per rating area in the ACA-compliant market decreased from 6.1 in 2016 to 4.2 in 2017. In most markets in which provider-owned insurers sold plans, there were usually just 1 or 2 provider-owned insurers per market.

 displays the change in the availability of plans sold by provider-owned and traditional insurers by population. The x-axis represents the number of carriers per market of each type operating in 2016, and the y-axis represents the number per market operating in 2017. For example, the large dot at the x = 1, y = 1 position in the “Provider-Owned Insurers” panel indicates that there are more than 50 million individuals who live in markets where exactly 1 provider-owned insurer sold plans in 2016 and 2017.

Large regions of the United States have no provider-owned insurers, but many states are sparsely populated. About 62% of US residents (more than 170 million people) live in a rating area in which a provider-owned insurer sells plans.

Six percent of the population live in areas in which the number of provider-owned insurers increased between 2016 and 2017 (represented by the lighter circles in Figure 2). Eleven percent of the population live in areas in which the number of provider-owned insurers decreased (the darker circles in Figure 2), and 51% live in areas in which the number of insurers stayed the same (the medium circles in Figure 2). The remaining 32% of the population live in areas in which no provider-owned plan was available in either 2016 or 2017.

One percent of the US population live in areas in which the number of traditional insurers increased, 56% live in areas in which the number of insurers decreased, and 43% live in areas in which the number of insurers stayed the same.

 summarizes market characteristics and shows the association between market characteristics and the presence of a provider-owned insurer in the 503 rating areas. Only per capita income was significantly associated with the presence of a provider-owned insurer in a market. An increase in income of 1 SD ($8700) is associated with a 14-percentage-point increase in the likelihood that there is a provider-owned insurer in a market.

 presents summary statistics at the insurer-market level for each insurer’s least expensive Silver plan in the markets in which they offer at least 1 plan. Provider-owned insurers sell 30% of the plans in the sample. Seventy-eight percent of plans were classified as health maintenance organizations or exclusive provider organizations. Thirty-five percent of all plans in the sample were available in the off-exchange market only.

Provider-owned insurers sold 1396 Silver plans in total, of which 156 (10%) were available off-exchange only. Traditional insurers that operated in the same markets as provider-owned plans sold 3868 plans in those markets, of which 502 (14%) were available in the off-exchange market only. On average, provider-owned insurers sell on-exchange plans in 80% of the markets in which they operate. Traditional insurers sell on-exchange plans in 90% of the markets in which they operate (considering only markets in which provider-owned insurers sell plans, to facilitate comparison).

 shows estimates of differences in premiums between provider-owned and traditional insurers’ plans from 6 mixed-effects logistic regression models estimated on the sample described in Table 2 (n = 970). Full estimates are presented in 

. The first model includes a random effect for market but not insurer. The second, our preferred specification, adds a random effect for insurer (insurers sell multiple plans across markets). The model indicates that premiums for plans sold by provider-owned insurers are lower, on average, but not statistically different from premiums for plans sold by traditional insurers.

The third model includes plans sold by multistate insurers (n = 533), and the fourth includes plans sold by all insurers other than Kaiser Permanente (n = 923). The fifth and sixth add controls for plan type and the cost-sharing parameters described in Table 2. There are 190 variables in the HIX Compare data that describe different plans’ cost-sharing requirements. We did not control for these in the baseline model because benefit levels in the exchange are tightly regulated. Silver plans must have an actuarial value of at least 70%. A plan that requires lower cost sharing along one dimension will have higher cost-sharing in another. We nevertheless estimated a model in which we control for several especially salient cost-sharing parameters, such as whether the plan has a deductible for in-network care, the size of the in-network deductible, and the maximum out-of-pocket payment for in-network care.

The results are consistent across models: Point estimates are negative (indicating that provider-owned plans have lower premiums), and the CIs are wide. Results are similar when we re-estimate the model on plans in different metal tiers (Bronze and Gold), using premiums for different policy holder types (individuals aged 27 years and couples both aged 30 years), and on plans sold in the exchange market only.

When we re-estimate the second model (our preferred specification with market- and insurer-level random effects) using 2016 data, the coefficient on provider-owned plans is —15 (95% CI, –39 to 9). Premiums for provider-owned plans were lower but not statistically different from premiums for plans sold by traditional insurers. If we omit carrier-level random effects, the coefficient is –18 (95% CI, –28 to –8).

Provider-owned insurers play an important but underappreciated role in the individual insurance market. About one-third of the insurers who sell plans in the ACA-qualified individual market are provider owned. Two-thirds of the population live in areas where at least 1 provider-owned insurer sells plans. Provider-owned insurers are located on the West Coast and in the upper Midwest, some Western states, and Texas.

We find that premiums are similar between provider-owned and traditional insurers. The exchange market has a number of features, including standardization of actuarial values and benefits and market-level risk adjustment, that facilitate direct comparisons of premiums between insurers. Although risk adjustment is imperfect, risk adjustment provisions of the ACA entail substantial transfers to and between plans. Risk adjustment significantly diminishes selection-driven differences in plans’ profits.

 As a result, plans ought to set premiums based on the average level of risk in the market rather than the level of risk among likely enrollees. To the extent that there is residual selection, the direction of the bias is unclear. Healthier enrollees are probably more willing to trade off lower premiums for stricter limits on provider choice in provider-owned plans, but participants in the associated health system form a natural customer base for provider-owned insurers.

 we use data covering the entire United States, our data are recent (from 2017), and we adjust standard errors for clustering by insurer. Adjusting standard errors for clustering has a large effect on the size of CIs.

Provider-owned insurers sell individual policies in areas that cover 62% of the US population and have premiums similar to policies of traditional insurers.

Provider-Owned Insurers in the Individual Market

 To evaluate the impact of enrollment in a consumer-directed health plan (CDHP) on out-of-pocket (OOP) spending and on the financial burden associated with healthcare utilization.

 Using commercial claims data from 2011 through 2013, we estimated difference-in-differences models that compared changes in outcomes for individuals who switched to CDHPs (CDHP group) with outcome changes for individuals who remained in traditional plans (traditional plan group).

 We estimated the impact of CDHP enrollment on OOP spending at the point of care and on having high financial burden, defined as whether an enrollee spent 3% or more of household income on OOP spending. Additionally, we assessed these outcomes for 2 subgroups: those with lower household income and those with chronic conditions.

 Within the first year of CDHP enrollment, CDHP enrollees experienced a mean marginal increase in OOP spending of $285 (41% increase; 95% CI, $271-$299; 

<.001) relative to traditional plan enrollees. The lower-income and chronic conditions subgroups experienced mean marginal increases in OOP costs of $306 (44% increase; 95% CI, $257-$353; 

<.001) and $387 (56% increase; 95% CI, $339-$435; 

<.001), respectively. The probability of an enrollee having excessive financial burden increased by 4.3 percentage points (95% CI, 4.0-4.6; 

<.001) for the full CDHP sample. These effects were about 3 times larger for the lower-income subgroup (12.3 percentage points; 95% CI, 10.7-13.8; 

<.001) and 2 times larger for the chronic conditions subgroup (8.0 percentage points; 95% CI, 6.9-9.1; 

 CDHP enrollment led to a significant increase in financial burden associated with healthcare utilization, especially for those with lower incomes and those with chronic conditions.

Consumer-directed health plan (CDHP) enrollment increases the financial burden associated with healthcare utilization, especially for those with lower incomes, those with chronic conditions, and those at the higher end of the healthcare spending distribution. 

Due to the unpredictable nature of healthcare utilization, it is unclear in advance who will end up with high healthcare utilization and significant financial burden in a given year. 

More effort is needed to make individuals aware of the potentially significant out-of-pocket spending and financial burden that could be incurred after enrollment in a CDHP.

Consumer-directed health plans (CDHPs) are high-deductible health plans (HDHPs) coupled with a health savings account (HSA) or a health reimbursement arrangement (HRA). The proportion of covered workers with employer-sponsored insurance enrolled in a CDHP increased from 4% in 2006 to 29% in 2016.

In 2016, 56% of covered workers were employed by a firm that offered a CDHP and, among the 41% of covered workers who were offered only 1 type of plan, nearly one-third were offered only a CDHP.

CDHPs are designed to make patients more cost-conscious and to encourage value-based decision making. Although previous work has consistently demonstrated that CDHPs reduce healthcare spending and utilization,

 they may also increase financial burden when patients utilize healthcare. Previous research has shown that CDHP enrollees are more likely to have difficulty accessing necessary healthcare and to incur high medical bills and medical debt,

 particularly among lower-income individuals and those with chronic conditions.

However, many existing studies are based on findings from cross-sectional surveys and thus might be subject to potential selection bias. We therefore investigated the impact of CDHP enrollment on the financial burden of healthcare utilization at the point of service, using longitudinal private insurance claims data from January 1, 2011, to December 31, 2013. We estimated the effects of CDHP enrollment on out-of-pocket (OOP) costs both at the mean and across the distribution of healthcare spending. Additionally, we analyzed the effect of CDHP enrollment on the probability of an enrollee having excessive financial burden from OOP spending. Finally, given evidence that lower-income individuals and the chronically ill may be more likely to forgo or delay care due to cost, coupled with the fact that the RAND Health Insurance Experiment suggests that high cost sharing may lead to impaired health outcomes among the poor and sick,

 we examined the effects of CDHP enrollment on financial burden for these vulnerable populations. 

We used claims data from the OptumInsight (a subsidiary of UnitedHealth Group) Clinformatics Data Mart from 2011 to 2013 to analyze a 25% random sample of the insurer’s commercially insured subscribers and dependents who were continuously enrolled over the full 3-year period in both employer-sponsored and nongroup plans. These claims data also include measures of socioeconomic status, including categorical household income, predicted by a demographic-based analytical model using Census block group—level data where the unit population size (~600-3000) is smaller than that of a Census tract (~1200-8000) or a zip code tabulation area.

 The data also include measures of education level, predicted by the median level of education attained by individuals 25 years or older at the Census block level, and race/ethnicity code, derived from a combination of sources including public records, self-reported surveys, and a proprietary ethnic code algorithm.

We included 2 groups of enrollees. The CDHP group included enrollees who were in a traditional plan in 2011 and in a CDHP in 2012 and 2013 (switch date: January 1, 2012) and enrollees who were in a traditional plan in 2011 and 2012 and in a CDHP in 2013 (switch date: January 1, 2013); switching was split relatively evenly across the 2 years in the study sample (

]). The traditional plan control group included enrollees who were in a traditional plan throughout all 3 years. We excluded enrollees who were 65 years or older (5.7% of the sample) and enrollees with negative OOP costs (0.009% of the sample). The lower-income subgroup included enrollees with an estimated annual household income of less than $40,000 (the lowest category defined in the data). The chronic conditions subgroup was defined as enrollees with at least 1 chronic condition in the baseline year, defined by the Charlson Comorbidity Index (CCI).

 The data were deidentified, and this study was approved by the University of Southern California University Park Institutional Review Board.

We analyzed OOP spending and a binary indicator of excessive financial burden, defined by OOP spending being greater than or equal to 3% of household income, based on prior research.

 We also used alternative thresholds (5% and 10%) to examine the sensitivity of our findings to this definition.

 In all cases, OOP spending refers only to spending incurred at the point of care and represents the sum of co-payments, deductibles, and coinsurance paid by an enrollee for all healthcare services utilized in the given year. Because estimated household income was a categorical variable in our data, we calculated the excessive financial burden indicator using the midpoint of each income interval, capped at $100,000 (the highest category defined in the data). 

test to compare baseline characteristics between the CDHP group and the traditional plan group. We then performed descriptive analyses of the trends in mean OOP spending and the percentage of enrollees having excessive financial burden before and after CDHP enrollment. We used difference-in-differences (DID) regression analysis to compare changes in the outcomes for individuals who switched to CDHPs (CDHP group) with those for individuals who remained in traditional plans (traditional plan group). Compared with a standard DID model in which the participants in the treatment group usually experience a 1-time shift, we analyzed enrollees who switched to a CDHP at 2 different time points to enable estimation of both short-term (1-year) and medium-term (2-year) effects of CDHP enrollment. In all models, the analysis was at the enrollee-year level, and the primary independent variables were the indicators for the first and second year (where applicable) of CDHP enrollment, adjusted for group fixed effects, year fixed effects, age, gender, race, education, Census division, and CCI score.

For each population, we first estimated the impact of CDHP enrollment on mean OOP spending. Because OOP spending was highly skewed, we used a generalized linear model (GLM) with log link function and gamma distribution. We calculated the average marginal effects (incremental effects) of first- and second-year CDHP enrollment. Standard errors were clustered at the enrollee level. We also investigated the impact of CDHP enrollment on the distribution of differences in OOP spending (relative to traditional plan enrollees) using the linear quantile DID (QDID) model,

 which outputs the treatment effects of CDHP enrollment at each decile of OOP spending. We used the QDID regression coefficients to predict traditional plan enrollees’ adjusted OOP spending distribution in 2013 and the traditional plan group’s counterfactual OOP spending distribution (ie, what the OOP spending distribution of the traditional plan group would have been had they enrolled in a CDHP). Based on the adjusted and counterfactual OOP cost distribution, we examined the change in the percentage of enrollees who would have very high OOP spending (eg, higher than $2000) due to CDHP enrollment. Finally, we analyzed the impact of CDHP enrollment on the indicator of whether an enrollee had excessive financial burden (OOP spending to household income ratio ≥3%, 5%, or 10%) in a given year using GLM with logit link function and Bernoulli distribution. Standard errors were clustered at the enrollee level. Statistical analyses were performed using SAS version 9.4 (SAS Institute Inc; Cary, North Carolina) and Stata version 14 (StataCorp; College Station, Texas). We used a significance level of 

 ≤.05 and all statistical tests were 2-sided.

There were a total of 689,542 enrollees in our sample, consisting of 36,387 enrollees in the CDHP group and 653,155 enrollees in the traditional plan group. The baseline characteristics of each group are shown in 

 test indicates that CDHP enrollees differed from traditional plan enrollees in terms of age, education, gender, health status, region, and race and ethnicity. There were no statistically significant differences in predicted annual household income between the 2 groups.

Trends in OOP Spending and Financial Burden

Both the levels and trends in our key outcome variables were similar for the CDHP and the traditional plan group during the pre-CDHP enrollment period of up to 2 years (

). Within the first year of CDHP enrollment, the percentage of CDHP enrollees having excessive financial burden rose from 9.7% (95% CI, 9.4%-10.0%) to 16.0% (95% CI, 15.7%-16.4%) for the full sample, compared with a slight increase from 9.0% (95% CI, 8.9%-9.1%) to 9.8% (95% CI, 9.7%-9.9%) among traditional plan enrollees (eAppendix Figure 2).

The impact of CDHP enrollment on financial burden was more pronounced among the lower-income and chronic conditions subgroups: The percentage of CDHP enrollees having excessive financial burden among these subgroups rose from 32.9% (95% CI, 31.4%-34.5%) to 47.7% (95% CI, 46.1%-49.4%) and from 25.3% (95% CI, 24.1%-26.5%) to 33.9% (95% CI, 32.7%-35.2%), respectively (eAppendix Figure 2). The effects of CDHP enrollment persisted for 2 years; OOP spending increased among CDHP enrollees while OOP spending for traditional plan enrollees remained stable (eAppendix Figure 1).

The effects of CDHP enrollment on mean OOP spending are shown in 

. In the first year of CDHP enrollment, the mean marginal increase in OOP spending was $285 (41% increase; 95% CI, $271-$299; 

<.001). CDHP enrollment resulted in larger marginal increases in OOP spending for the lower-income ($306; 44% increase; 95% CI, $257-$354; 

<.001) and chronic conditions ($387; 56% increase; 95% CI, $339-$435; 

<.001) subgroups. By the second year of CDHP enrollment, mean marginal OOP spending increased by $306 among the full sample (44% increase; 95% CI, $286-$325; 

<.001), $364 (53% increase; 95% CI, $300-$429; 

<.001) among the lower-income subgroup, and $428 (62% increase; 95% CI, $364-$492; 

<.001) among the chronic conditions subgroup. The larger increases in OOP spending in the second year of CDHP enrollment are consistent with prior work which has found that utilization increases in the second year of CDHP enrollment.

presents the QDID estimates of the impact of CDHP enrollment at each decile across the distribution of OOP spending. Generally, the marginal effect of CDHP enrollment increased with higher levels of OOP spending. For example, among the full sample, CDHP enrollment led to a $162 (95% CI, $150-173; 

<.001) marginal increase in OOP spending at the median in the first year compared with a $726 (95% CI, $678-$773; 

<.001) marginal increase in OOP spending at the 90th percentile. That is, the marginal impact of CDHP enrollment on OOP spending (compared with traditional plan enrollees) was greater for those at the higher end of the OOP spending distribution. For the lower-income and chronic conditions subgroups, the marginal increases at the median were $241 (95% CI, $200-$282; 

<.001), respectively, and the marginal increases at the 90th percentile were $715 (95% CI, $558-$871; 

The adjusted and counterfactual OOP spending distributions for the traditional plan group are shown in the 

. As expected, the counterfactual OOP spending distributions fall above the predicted OOP spending distribution, suggesting that traditional plan enrollees would have had higher OOP spending if they had switched to a CDHP (Figure). As a sensitivity analysis, we also separately estimated the magnitude of these counterfactual spending estimates if the traditional plan group switched to CDHP/HRAs compared with CDHP/HSAs (based on separate underlying QDID regressions for the CDHP/HRA and CDHP/HSA populations). We found that increased OOP spending after enrollment in a CDHP tended to be larger for enrollees in CDHP/HRAs versus CDHP/HSAs across nearly all of the OOP spending distributions (

). This is consistent with prior work, which has found that enrollees in CDHP/HSAs had significantly greater reductions in utilization than did enrollees in CDHP/HRAs, potentially because employees with HSAs have a stronger incentive to save money because HSA account balances are owned by the employee whereas HRA account balances are owned by the employer.

CDHP enrollment also increased the probability of having very high OOP spending. For example, the percentage of enrollees spending more than $2000 increased by nearly 10 percentage points for the full sample and for the lower-income subgroup and by about 15 percentage points for the chronic conditions subgroup (Figure). These findings regarding the impact of CDHPs on the probability of having very high OOP spending are supported by the results of the impact of CDHP enrollment on the probability of having excessive financial burden (Table 2b). For the entire sample, enrollment in a CDHP increased the probability of an enrollee being exposed to excessive financial burden (defined as OOP spending being ≥3% of household income) by 4.3 percentage points (95% CI, 4.0-4.6; &#8203;

<.001; baseline, 9.7%). The effects were about 3 times larger for the lower-income subgroup (12.3 percentage points; 95% CI, 10.7-13.8; 

<.001; baseline, 32.9%) and 2 times larger for the chronic conditions subgroup (8.0 percentage points; 95% CI, 6.9-9.1; 

<.001; baseline, 25.3%). These effects persisted in the second year of CDHP enrollment and were robust to alternative definitions of excessive financial burden (Table 2b).

We estimated the impact of CDHP enrollment on financial burden due to cost sharing incurred at the point of service when utilizing care. We found that CDHP enrollment led to a significant increase in OOP spending and that this higher financial burden was particularly pronounced for the lower-income and chronic conditions subgroups. For example, more than half of the lower-income subgroup and more than one-third of the chronic conditions subgroup faced excessive financial burden from OOP spending after enrollment in a CDHP. Moreover, we found that CDHP enrollment increased the probability of having very high OOP spending. 

Our sample was subject to potential selection concerns because we were unable to evaluate whether CDHP enrollees actively chose a CDHP among a menu of plan options or whether it was the only option made available to them by their employer, although the fact that the level and trend of the key outcome variables were similar between the CDHP and traditional plan groups in the baseline period reduces these concerns (eAppendix Figures 1 and 2). Additionally, the household income data were based on a prediction model and thus may be subject to measurement error, although the predictions were based on very local geography. We were also unable to link dependents to subscribers, so we could not assess financial burden due to OOP spending at the household level; however, because the income levels represent household (rather than individual) income whereas we assessed individual-level OOP spending, our estimates may thus represent a lower bound of financial burden on OOP spending pooled across all household members. Finally, our data did not include additional detail, such as plan premiums or whether and how much the employer contributed to the individual’s HSA or HRA, so we could not directly evaluate the impact of CDHP enrollment on overall spending.

Our findings, and in particular, the variation in the impact of CDHP enrollment on OOP spending across the distribution, should be considered in the broader context of overall healthcare spending, including premiums and potential HSA/HRA contributions. Nationally representative data on employer-sponsored health benefits indicate that, in 2013, the average annual worker contribution to premiums for single coverage was $1058 for CDHPs with an HRA, $726 for CDHPs with an HSA, and $1027 for non-CDHP plans. This suggests that enrollees in CDHPs with an HSA saved $301 ($1027 minus $726) in employee-paid premium contributions compared with those enrolled in non-CDHPs, on average. Additionally, 34% of covered workers in CDHPs with an HSA worked for an employer that did not make an HSA contribution but, among those that did, the average annual HSA contribution for a single employee was $950.

At the lower end of the distribution, we found very modest increases in OOP spending at the point of care among CDHP enrollees compared with traditional plan enrollees. Thus, it is likely that these individuals may actually be financially better off by enrolling in CDHPs, as the savings in employee-paid premium contributions and/or the benefits of employer contributions to a tax-advantaged savings account likely more than offset the increased OOP spending at the point of care. However, at the high end of the distribution, the increased OOP spending would still heavily outweigh the reduced spending on employee contributions to premiums, particularly if the individual was among the one-third of covered workers who did not receive an HSA contribution from their employer. Moreover, even if those individuals at the lower end of the actual spending distribution may have lower total expenditures on premiums plus OOP spending at the point of care in a given year, due to the unpredictable nature of catastrophic health events, they still face the potential risk of having very large OOP spending under a high deductible, which may be particularly problematic for lower-income individuals with limited assets.

The potential consequences of CDHP enrollees experiencing high financial burden are significant. Previous work suggests that high financial burden may cause CDHP enrollees to delay or skip needed healthcare.

 Additionally, individuals who face financial burden often have to change their employment or lifestyle, or make other sacrifices, to make ends meet.

 We do not know the extent to which individuals considered potential downstream OOP costs when enrolling in CDHPs. However, prior research suggests that many individuals make poor health plan choices and frequently choose worse plans when alternative choices that have both lower financial risk of OOP spending and lower premiums are available.

 A possible explanation for these poor choices is that many individuals do not understand the basics of benefit design, such as the meaning of deductibles and co-payments.

 Moreover, this problem of poor health insurance literacy is more acute among the low-income population,

 suggesting that these individuals may not understand the potential financial burden associated with enrollment in CDHPs. Although prior work suggests that providing information on OOP costs can improve health plan choice,

 more work is needed to design effective interventions for improving plan choice. Additionally, further research on the impact of financial burden associated with CDHP enrollment on the use of appropriate care, unmet medical need, and other health outcomes is warranted.

School of Pharmacy (XZ), Price School of Public Policy (ET, NS), and Schaeffer Center for Health Policy and Economics (XZ, ET, NS), University of Southern California, Los Angeles, CA.

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. 

Enrollment in a consumer-directed health plan increases the financial burden associated with healthcare utilization, especially for those with lower incomes and with chronic conditions.

Erin Trish, PhD

Articles

Policies to Address Surprise Billing Can Affect Health Insurance Premiums

Provider-Owned Insurers in the Individual Market

Financial Burden of Healthcare Utilization in Consumer-Directed Health Plans

Provider-Owned Insurers