Ateev Mehrotra, MD, MPH

 Healthcare payers are increasingly using price transparency and benefit design to encourage patients to choose lower-priced providers. We quantify potential savings from shifting patients to lower-priced providers. If there is limited price variation or if higher-priced providers command little market share, savings could be minimal.

 Using 2013-2014 commercial claims for 697,381 enrollees in California, we characterized within-market price variation and the relationship between providers’ market shares and relative prices for 3 nonemergent, shoppable outpatient services: laboratory tests, imaging services, and durable medical equipment (DME). In a stylized policy simulation that holds provider price and utilization constant, we computed potential savings if patients who visited providers with prices above the median price shifted to the median-priced provider in their geographic market for the same service.

 Of the service categories examined, laboratory tests had greatest within-market price variation (median coefficient of variation of 100% vs 87% for imaging services and 43% for DME). Roughly half of services (53%, 47%, and 54% for laboratory tests, imaging services, and DME, respectively) were billed by providers with prices above their market median. Shifting these patients to the median-priced provider in their markets could save 42%, 45%, and 15% of spending on laboratory tests, imaging services, and DME, respectively, together representing savings of 11% of total outpatient spending and 7% of the sum of inpatient and outpatient spending.

 Steering patients from higher- to lower-priced providers within geographic markets in targeted service categories could generate substantial healthcare savings.

Steering patients to lower-priced providers could generate substantial savings in laboratory and imaging services and, to a lesser extent, in durable medical equipment.

Potential savings from steering patients to lower-priced providers will be greater for services with higher within-market price variation and for services in which high-priced providers command greater market share.

In a simulation that holds provider prices and utilization constant, we find that steering patients to lower-priced providers could result in substantial decreases in outpatient spending.

Prices of healthcare vary substantially, even among providers within the same geographic area,

 and price is weakly correlated with quality.

 Therefore, shifting patients to lower-priced providers could yield savings without sacrificing quality. Motivated by this potential, health plans have focused on raising the salience of and exposure to prices for patients through, for example, price transparency initiatives,

 high-deductible health plans, and other benefit designs.

 Motivated by this ongoing policy interest,

 we quantify the potential savings from shifting patients from higher- to lower-priced providers in their market.

Prior work estimated that switching patients from higher-priced branded drugs to available generics could save 11% of national drug expenditures.

 We examine 3 services (laboratory tests, imaging services, and durable medical equipment [DME]) that, like drugs, are good candidates for shifting to lower-priced options. They are often used in nonemergent scenarios and do not require patients to switch primary care physicians. Moreover, most patients do not believe that price differences reflect quality differences in healthcare.

Potential savings depend on the extent of price variation across providers in a market and the share of patients receiving care from higher-priced providers. As an example, if prices vary widely but few patients receive care from higher-priced providers, shifting this small fraction of patients away from these providers may not meaningfully reduce spending. Potential savings would be minimal even if a large share of patients receive care from the higher-priced providers, but the price difference between the higher- and lower-priced providers is small.

We characterize price variation within markets and describe the relationship between providers’ market shares and their relative prices in the market. To estimate potential savings from shifting to lower-priced providers, we simulate savings on laboratory tests, imaging services, and DME if commercially insured patients visiting higher-priced providers instead received care from the median-priced provider in their market for the same service. We estimate partial equilibrium or static effects. In other words, our analysis holds other factors constant and does not incorporate potential responses in provider prices or patient utilization that might be expected over the longer term if a large number of patients in a market were steered toward lower-cost providers. Our analysis illuminates the extent of price variation, the share of spending attributable to high-priced providers in markets, and how that share varies by services and markets.

Anthem Blue Cross provided deidentified medical claims and enrollment data for California enrollees in preferred provider organization health plans with deductibles ranging from $250 to $750 between January 1, 2013, and December 31, 2014. To ensure that we observed all spending, we excluded enrollees during any quarters in which they were not continuously enrolled and enrollees with supplemental coverage from another source.

We focused on 3 service categories of outpatient medical services: laboratory tests, imaging, and DME (

]). Each Current Procedural Terminology (CPT) code for a test, image, or equipment is referred to as a “service.”

We calculated spending in each service category and for all outpatient care by enrollee and quarter. Quarters began on January 1, April 1, July 1, and October 1 of each year.

The price for a given service was defined as the sum of payments to the provider from the insurer and enrollee (deductible, co-payment, and coinsurance). Professional and facility fees for the same service performed in a hospital outpatient department (HOPD) were summed to a single price. Moreover, for a given beneficiary, payments from multiple claims for the same CPT code and service date were summed. We defined a single price for each provider—service market as the median of the price paid to the provider for that service in a calendar year. In sensitivity analyses, we defined provider prices using the mean and modal prices. To reduce the influence of outliers, we winsorized prices at the 99th percentile of each service by year before computing the provider’s price.

Markets were defined as 3-digit zip codes; any provider serving enrollees who resided in a given 3-digit zip code was considered a provider in that market. Therefore, providers may have appeared more than once if they served patients in multiple 3-digit zip codes. In a sensitivity analysis, we defined smaller markets using 5-digit zip codes.

 We calculated 2 measures of price variation—coefficient of variation (CV) and interquartile ratio (IQR)&mdash;for each market—service combination. The CV is the SD divided by the mean price multiplied by 100, which provides a unitless measure of price dispersion. The IQR is the quotient of the 75th percentile price divided by the 25th percentile price. These measures are weighted by the volume of the service and market. For each service category, we report the median CV and IQR across all market–service combinations.

For each service, a provider’s market share in that year was defined as the percentage of claims in a market billed by that provider. We assigned provider—service combinations to price deciles within their markets, which were designated by the patient’s 3-digit zip code of residence (eg, the lowest-priced providers are in the bottom decile; the providers with prices in the top 10% of the distribution are in the top decile). We report the share of claims billed by providers in each decile by service category.

Potential savings from shifting patients to lower-priced providers in their market

 We analyzed the potential savings from switching patients from higher- to lower-priced providers and report results in 2 ways. Our main results report the estimated savings that accrue if all patients who received services from higher-than-median-priced providers instead received care at the provider with the median price in their market. We also simulated the savings from switching patients who received services from the highest-priced providers to successively lower-priced providers in small increments. Specifically, we report savings from switching patients from providers above the 95th percentile to the price at the 95th percentile, then from above the 90th percentile to the 90th percentile, continuing in 5% increments down to the lowest price in the market.

We present potential savings from these simulations in aggregate, as well as the distribution of savings across markets and services. For each service category, we report the 10 markets and services with the most and least estimated savings, as well as savings stratified by Northern, Central, and Southern California. Prior work has highlighted the high prices of care provided in HOPDs.

 To examine the contributions to potential savings from price variation within each setting (eg, shifting patients from higher- to lower-priced HOPDs and from higher- to lower-priced freestanding clinics), we simulated potential savings separately in HOPDs and freestanding providers. In secondary analyses, we also examined the potential savings from shifting patients visiting high-priced (above-median) HOPD providers to the median-priced provider in their market to capture potential savings from efforts that focused on steering patients away from expensive HOPD providers. Whether a service was rendered in a HOPD or freestanding clinic was determined by the place of service designation on each claim.

We conducted several sensitivity analyses. As noted previously, we defined smaller markets using 5-digit zip code, and we defined providers’ prices by the mean and modal allowed amount. We also repeated the analyses excluding laboratory tests, imaging services, and DME received on days when enrollees had other outpatient medical care. In these situations, clinicians who are part of health systems may have directed patients to receive the service in their facility, leaving little opportunity for the patient to choose a lower-priced provider.

Our sample included 697,381 enrollees living in 60 three-digit zip codes (markets) across California (

). The average market had 28 laboratory test providers, 32 imaging providers, and 6 DME providers. Average quarterly outpatient and total nonpharmacy (inpatient and outpatient) spending was $778 and $1150, respectively. Laboratory tests, imaging, and DME constituted 10%, 14%, and 3%, respectively, of overall outpatient spending.

Price dispersion relative to the mean within markets was greatest for laboratory tests (CV = 100%) and imaging (CV = 87%) and lower for DME (CV = 41%) (

). The median ratio between the 75th and 25th percentile prices in a market was 2 for laboratory tests, 2 for imaging services, and 1 for DME claims. The extent of price dispersion varied across both services and markets (

Providers with above-median prices had 47%, 53%, and 54% of market share in laboratory tests, imaging, and DME, respectively (

). Providers with the highest prices had the most market share in imaging (11% of market share for providers in the top decile), followed by laboratory tests (6%) and DME (3%).

Potential Savings From Shifting Patients to Lower-Priced Providers in Their Market

Mean (SD) quarterly spending for laboratory tests per enrollee was $76 ($705) (Table 2). Simulated spending after shifting enrollees to pay the median price was $44 per enrollee per quarter, or 42% savings. For imaging services, mean (SD) spending was $107 ($668) per enrollee; if enrollees at providers above the median instead paid the median price, spending would have been $59 per enrollee, or 45% savings. Mean (SD) DME spending was $20 ($426) per enrollee, and simulations moving enrollees who paid above the median price to the median price would result in spending of $17 per enrollee, implying savings of 15%.

Potential savings were not the same across all geographic markets. Across geographic markets, the potential savings from shifting patients to the median-priced providers ranged from 31% to 55% for laboratory services, 27% to 58% for imaging, and 3% to 23% for DME (

). For all 3 service categories, estimated savings were higher by 1 to 4 percentage points in Northern versus Southern California (

Stratifying by site of service revealed that spending and potential for savings are significantly greater in the HOPD setting compared with freestanding providers. If we assume that patients would stay within the chosen setting, then for laboratory services, shifting patients visiting HOPD providers with above-median prices to the median HOPD price would generate about 64% savings compared with 26% savings among freestanding providers. For imaging services, potential savings were 62% in the HOPD setting compared with 19% among freestanding providers. Potential for savings in DME received in the HOPD setting were 20% compared with 10% in the freestanding office setting. Instead, patients could switch across settings. In simulations, shifting patients visiting providers with above-median prices in the HOPD setting to the median-priced provider in the market, regardless of whether HOPD or freestanding facility, would yield savings of 32%, 38%, and 6% in laboratory tests, imaging, and DME, respectively.

A second set of simulations analyzed the savings from shifting patients to lower-priced providers in smaller increments (

). Steeper slopes indicate that greater potential savings can come from moving a small number of enrollees from the highest-priced providers. Potential savings increased most steeply for imaging services followed by laboratory tests, driven by high within-market price variation in these service categories. Despite the substantially higher market share for DME providers with above-median prices, the savings from shifting patients to successively lower prices grow slowly relative to laboratory tests and imaging, due to more modest within-market price variation in DME. An extreme scenario, shifting all patients who receive care at providers above the 5th percentile to 5th percentile price in the market, would generate savings of 68% in laboratory tests, 78% in imaging services, and 38% in DME.

Altering the market definition from 3- to 5-digit zip code reduced the savings, but they remained substantial at 35% for laboratory tests, 38% for imaging services, and 7% for DME (eAppendix). Excluding laboratory and imaging claims billed on the same day as other outpatient services modestly reduced potential savings for those services to 37% for both. Changing the definition of a provider’s price from median to mean or mode had virtually no effect on estimated savings.

There is widespread interest in decreasing healthcare spending by encouraging patients to choose providers with lower prices. We find that if patients who visited a higher-priced provider switched to a median-priced provider in their market, savings would be 42% in laboratory tests, 45% in imaging services, and 15% in DME. These significant potential savings in imaging and laboratory tests can be explained by both wider within-market price variation and high market share among high-priced providers. The cumulative potential savings from all 3 service categories are equivalent to 11% of total outpatient spending and 7% of total medical spending. California Public Employees’ Retirement System alone could save $15.3 million quarterly from shifting patients to the median-priced providers for these 3 service categories. For context, the $332 in annual savings per patient is much larger than prior estimates of $45 in savings from switching from branded drugs to generics. However, it is important to acknowledge that the 2 estimates are not directly comparable because they are based on different samples and different time periods.

Although these results imply significant opportunities for savings if payers can steer patients to lower-cost providers, the most effective way to do so remains unclear. Inducing patients to switch to lower-priced providers may be difficult because of patient loyalty to their providers

 and because often patients themselves do not benefit financially from switching to lower-priced providers (eg, once above the deductible).

 Offering patients price transparency tools has, to date, had limited impact in steering patients.

 On the other hand, alternative benefit designs such as reference-based pricing and tiered provider networks, where patients face much larger out-of-pocket costs if they choose higher-priced providers, have shown early success in shifting patients to lower-priced providers.

We focused on laboratory tests, imaging, and DME because choosing a lower-priced provider for these services does not require switching physicians; still, a physician’s recommendation for a laboratory or imaging center may strongly influence the patient’s choice of provider.

 Physicians often recommend that patients get tests within the same health system in part because the results are more easily accessible in electronic health records or because they have financial incentives to do so. If, for these reasons or others, patient “stickiness” to physicians extends to choice of laboratory, imaging, and DME providers, then strategies to lower prices, rather than inducing patients to switch providers, may be more effective in achieving savings. Top-down strategies, such as all-payer rate setting, would also reduce price variation.

 Policies that promote competition in the healthcare delivery system, such as stricter antitrust enforcement, may limit the ability of increasingly large health systems to negotiate higher prices, which puts downward pressure on prices for healthcare services.

 Relatedly, curbing alleged anticompetitive practices by large systems, such as the inclusion of antisteering clauses in payer contracts, may also be effective.

 The presence of these contracting arrangements inhibits ability to reduce healthcare spending through consumer-directed strategies. Our results suggest that a majority of the potential savings would come from steering patients away from the hospital-based providers. For example, 32% of the 45% in overall estimated savings in imaging would come from simply shifting patients visiting an above-median hospital-based provider to the median-priced provider. In laboratory tests and imaging, focusing on patients visiting above-median hospital-based providers would account for 71% and 40% of overall estimated savings, respectively. However, due to the competitive power wielded by hospital systems, as discussed, steering patients away from these providers may be most difficult.

Moreover, specific market conditions could affect both the potential for savings from successfully steering patients to lower-cost providers and the ability of a payer to actually steer patients. Although the differences were not large, potential savings are higher in Northern compared with Southern California. This may reflect the fact that Northern Californian markets are less competitive, and, on average, less competitive markets have higher prices. Although less competitive markets may have greater opportunities for savings, influencing patient behavior may be more difficult in these markets. The dominant providers in these markets may have the means to more effectively retain their patient base through measures such as advertising. Moreover, longer-term effects of steering patients away from dominant providers in less competitive markets could also differ. For example, although our analyses hold price constant, providers in less competitive markets may wield their market power by threatening to leave the payer’s network or demanding higher prices in response to payer efforts to shift patients away from them.

This analysis has several limitations. First, it is limited to enrollees of a single insurer in a single type of plan, and savings are conditional on the network and range of provider prices in this sample. Wide variation in negotiated prices for individual insurers has been observed in other US regions,

 but changes in market structure since 2014 may have altered the extent of price variation and distribution of market share. By looking at a large insurer, we might expect less variation in prices than if we examined results from a smaller insurer with less market power. In contrast, studying an insurer with a broad network will yield savings greater than if the insurer had a small network (although narrowing the network may be one way to capture the savings).

Second, we assume that 3-digit zip codes are markets. Although sensitivity tests that defined smaller markets using 5-digit zip codes also demonstrated potential savings, in reality, markets may be bigger or smaller and are likely to differ based on the service and the burden on patients from travel. Future work should evaluate other geographies and commercial insurers.

Third, providers may be misclassified as freestanding or hospital owned if our method, which is based on the place of service codes in the claims, does not produce accurate designations. However, if the misclassification is not systematically related to the provider’s relative price in the market, noise due to such misclassification should not bias the results.

Fourth, our analysis does not consider that price variation and greater market share of higher-priced providers could be justified by variation in provider quality or in patients’ perceptions of quality. If providers who charge higher prices provide higher quality of care, then savings from shifting patients to lower-cost providers could be offset by reduced patient satisfaction or worse health outcomes. However, past work suggests that higher-priced providers do not necessarily generate better health outcomes compared with lower-priced providers.

 Also, variation in quality may be less of a concern in laboratory tests, imaging, and DME than in physician services.

Finally, our simulation does not consider potential long-term, general equilibrium effects of shifting a large volume of services to lower-priced providers or of higher-priced providers substantially reducing their prices. If lower-priced providers increase their share of patients and increase their prices in response, our simulated savings would be overestimates. If, instead, price competition due to enrollee shifting leads to further price decreases, our simulated savings would underestimate savings. Longer-term effects of changes in market share or other downward pressure on healthcare prices are important topics for further research.

Articles

Steering patients who visit providers with above-median prices to their market’s median-priced provider would save 42%, 45%, and 15% of laboratory, imaging, and durable medical equipment spending, respectively.

What Are the Potential Savings From Steering Patients to Lower-Priced Providers? A Static Analysis

 There is robust evidence that implementation of reference-based pricing (RBP) benefit design decreases spending. This paper investigates employer adoption of RBP as a strategy to improve the value of patients’ healthcare choices, as well as facilitators and barriers to the adoption of RBP by employers.

 We conducted a qualitative study using 12 in-depth interviews with human resources executives or their representatives at large- or medium-sized self-insured employers.

 Interviews were conducted and recorded over the phone between March 2017 and May 2017. Interviewees were asked about their adoption of RBP and facilitators and barriers to adoption. We applied thematic analysis to the transcripts.

 Despite broad employer awareness of RBP’s

potential for cost savings, few employers are including RBP in their benefit design. The major barriers to RBP adoption were the complexity of RBP benefit design, concern that employees could face catastrophic out-of-pocket costs, lack of a business case for implementation, and concern that RBP could hurt the employer’s competitiveness in the labor market. The few employers that have adopted RBP have implemented extensive, year-round employee education campaigns and invested in multipronged and proactive decision support to help employees navigate their choices.

 Unless several fundamental barriers are addressed, uptake of RBP will likely continue to be low. Our findings suggest that simplifying benefit design, providing employees protection against very high out-of-pocket costs, understanding which decision-support strategies are most effective, and enhancing the business case could facilitate wider employer adoption of RBP.

Reference-based pricing (RBP) benefit design gives patients a financial incentive to switch their care to lower-priced providers.

Despite broad employer awareness of its potential for cost savings, very few US employers are including RBP in their health plans.

Key barriers to RBP adoption are concerns about the complexity of design, risk of employees potentially facing catastrophic costs, weak business case for RBP, and potential to hamper the ability to retain and attract workers.

Redesign and development of easy-to-implement, effective employee education and decision-support strategies could facilitate wider adoption of RBP.

US healthcare prices vary widely both across the country and within local markets.

 Reference-based pricing (RBP) benefit design gives patients a financial incentive to switch to lower-priced providers. Under RBP, the health plan determines a “reference price” for a given medical service, and the plan will cover medical expenses for that service up to that price. If a patient receives care from a provider whose negotiated payment is above the reference price, the patient is responsible for paying all medical costs above the reference price (ie, “balance billing”), along with all other cost sharing. RBP is applied only to select medical services, usually those that are nonemergent and for which patients have a choice of providers.

In contrast to deductibles, where the patient pays the “first dollar” of medical expenses, under RBP, the patient is responsible for the “last dollar.” The intent is to avoid the observed patient response to high-deductible health plans (HDHPs), for which the evidence suggests almost no effect on price shopping but reductions in needed services,

 and instead to have patients focus on where to receive care. In many cases, a plan’s maximum out-of-pocket cost does not apply, so even patients who have reached their maximum in their plan year still have an incentive to select lower-priced providers for RBP services.

Reference pricing has been used for pharmaceuticals in Europe and Canada for more than 2 decades.

 Only recently has RBP been implemented in the United States, with a focus largely on nonpharmaceutical services. Evaluations of US RBP programs have found reduced spending between 13.9% and 31.0% for joint replacement surgery,

Despite this robust evidence, recent Aon Hewitt surveys report that only 5% to 6% of employers were using RBP in 2015-2016.

 Little is known on why there has been low uptake. To better understand this landscape, we conducted a qualitative study of employers’ views of RBP. Specifically, we sought to understand employer perspectives of RBP as a strategy to engage employees in healthcare decision making, their adoption of RBP, and their concerns about RBP.

We identified a convenience sample of 13 individuals across 12 organizations, including human resources executives at large self-insured employers (6 interviewees at 6 organizations) and representatives of consulting firms and purchasing coalitions who support employer health benefits decision making (7 interviewees at 6 organizations). Interviewees were selected because they had adopted (n = 4) or closely considered (n = 9) RBP for their organization or on behalf of other purchasers. Neither the proportion of employers who adopted RBP nor these opinions are intended to be representative of all self-insured employers; they are the perspectives of individuals who have considerable experience with employer-sponsored insurance purchasing and who have undertaken serious consideration of issues related to RBP programs.

We developed a semistructured interview guide with open-ended questions focused on 3 domains: (1) efforts to engage employees in their healthcare decision making, (2) perceptions of RBP as a strategy to steer patients to higher value among alternatives (eg, HDHPs, price transparency, narrow provider networks), and (3) facilitators and barriers to implementing RBP. For those who implemented RBP, we asked additional questions about the experience with RBP and lessons learned.

Telephone interviews were conducted from March 2017 to May 2017. They lasted between 30 and 45 minutes and were recorded and transcribed. We analyzed interview data using accepted methods for qualitative analysis.

 We began with a preliminary set of deductive codes derived from the interview guide, but also allowed for inductive codes to emerge. We identified key themes characterizing employer perceptions of RBP, as well as facilitators and barriers to RBP adoption, and selected quotations to illustrate these themes. The Institutional Review Board at the Harvard T.H. Chan School of Public Health approved this study.

Four key themes characterized employer perspectives on adoption of RBP: (1) Although cognizant of its potential, very few employers have implemented RBP; (2) There are concerns about the complexity of RBP, employee risk of catastrophic out-of-pocket costs, and need for significant communication and decision support; (3) The business case for RBP is not compelling; and (4) Adoption of RBP may hinder retention of and competition for workers. A fuller description of each of these themes follows.

Theme 1: Although Cognizant of Its Potential, Very Few Employers Have Implemented RBP 

There was universal agreement with the need to engage employees in their healthcare decision making as a component of a larger strategy to lower healthcare spending. All interviewees believed that RBP could achieve cost savings, and several noted the potential to engage employees as well (see 

 for illustrative quotes). However, adoption of RBP remains low. One representative comment was: “Reference pricing is more on the far end in terms of what employees and employers are actually doing.” Key barriers to RBP adoption are captured in the remaining 3 themes.

Theme 2: There Are Concerns About the Complexity of RBP, Employee Risk of Catastrophic Out-of-Pocket Costs, and Need for Significant Communication and Decision Support

RBP is perceived as being a complex benefit design and as complex to implement because employees at a single large employer often work in many different markets in the United States (see 

 for illustrative quotes). There was widespread concern that if all employees face a single, national reference price, employees in some markets would have poor access to providers below the reference price. Adjusting the reference price by market would address this concern but would be more difficult to operationalize and communicate to employees.

The potential for employees to face catastrophic out-of-pocket costs was another concern. Under RBP, employees treated by a high-priced provider are responsible for the entire cost above the reference price. As one interviewee noted, “The company saves money, which is great; however, the employee gets hit with that delta. So it doesn’t feel very good.” Adverse employee outcomes could also lead to negative publicity, something employers are anxious to avoid.

There was broad awareness of the need for extensive, continuous communication with and education of employees, so they would know about potential cost sharing when seeking care. Interviewees felt that making sophisticated and user-friendly supplemental decision support available through multiple communication channels (eg, telephone and web-based) was critical to help employees choose providers. Dissatisfaction with the currently available tools was one of the factors that drove employers not to offer RBP.

The few employers that offered RBP implemented extensive communication and decision-support strategies. Some conducted targeted outreach (via letters or phone calls) to employees who were scheduled to receive care at a higher-cost provider to inform them about RBP and the out-of-pocket cost consequences of their provider choice. All but 1 employer with RBP offered a “concierge” service that employees could call for help identifying a low-priced provider. The concierge was also intended to help employees communicate with providers about a high-balance bill if they got one. RBP-focused communication was separated from other benefits communications; one employer developed animated videos to describe RBP, and another printed information about RBP on office supply materials that employees worked with every day. Finally, proactive education about RBP with select referring physicians was also mentioned as a strategy to support employee choices.

Theme 3: The Business Case for RBP Is Not Compelling

The low potential savings from RBP were also a barrier. Although prior research has shown that RBP adoption may yield substantial savings for a given clinical area, the potential savings were low on net across all services. Therefore, RBP did not justify the necessary investment in communication, marketing, and decision support or the efforts to respond to employee disenchantment from more restricted benefits. One interviewee noted: “The savings were not that substantial, and the main reason is procedures are not…high-dollar procedures. [It] was going to save like $80,000 or something, and it was like, ‘OK, for $80,000, this is not worth the hassle.’&thinsp;”

In contrast, employers using RBP emphasized a mission-driven, “do-the-right-thing” motivation, noting that RBP was the beginning of a longer journey toward improving the value of healthcare spending. They said, for example, “There’s more to it than just saving money. It’s about saving money the right way.” These employers also noted that starting with a small RBP program, despite low savings, allowed for the close monitoring of the program (eg, providing support for employee travel when necessary, allowing for exemptions for nonroutine cases) to ensure acceptable levels of quality and access are maintained.

Theme 4: Adoption of RBP May Hinder Retention of and Competition for Workers

The slowdown in the growth in healthcare spending over the past few years in combination with broader economic growth has resulted in private-sector employers facing increased competition for workers. Employers were hesitant to adopt RBP out of concern that it would be viewed negatively by employees. One interviewee noted: “[Many employers] say saving money is not their top priority. [The top priority] is making sure that their employees are happy, making sure they have these nice benefits, making sure the employees are taken care of if they have a problem.” Along these lines, firms know “they are competing with…companies who are offering very rich benefits, and they know if they want to get the talent, they also need to do that.”

Due to this reluctance to penalize employees for their provider choices (beyond HDHPs, which interviewees noted are pervasive), use of wellness programs, price transparency tools, disease management programs, and access to vendors that provide second opinions are preferred strategies to engage employees in their healthcare choices. These programs, as one interviewee described, rely “less on sticks and more on carrots.” Competition for workers through generous benefits was mentioned only among private-sector employers. When interviewees discussed public employers and union funds, they described growing pressure to control healthcare spending and more consideration of adopting RBP.

Despite strong evidence that RBP can decrease healthcare spending, our findings suggest that it is unlikely that there will be wide adoption of RBP in its current form in the US commercial health insurance market. Perspectives gleaned from these interviews suggest 3 strategies to facilitate wider adoption of RBP.

First, simplify. Exempting an entire category of low-priced providers from RBP, as CalPERS did for colonoscopies and ambulatory surgery centers (ASCs),

 gives patients a simple heuristic to guide them (eg, have your colonoscopy at an ASC) versus having to go provider by provider to determine whether they are below the reference price. Improved decision support is also needed. Second, establishing out-of-pocket maximums for RBP so that employees are not at risk of catastrophic costs could alleviate employee disruption and risk. Finally, “turnkey” solutions for employee communication and education, based on best practices that have demonstrated effectiveness, would address concerns about the necessary levels of communication and potential employee backlash.

Employers could also implement alternative forms of benefit design that encourage patients to switch providers but have less of a “stick.” Tiered network plans, which sort providers into strata and require patients to pay higher cost sharing if they choose a provider that is in a nonpreferred tier, are similar in conception to RBP but avoid the risk of catastrophic out-of-pocket costs. Several studies have demonstrated that tiered networks lead to savings.

 Another “carrot” option is to implement rewards programs in which patients receive money if they go to a lower-priced provider. Although these are becoming more popular,

 there have been no rigorous evaluations of their impact.

This paper has important limitations. The sample was purposefully selected using a limited number of respondents who had adopted RBP or had seriously considered its adoption, and the findings may differ in other settings. However, participants’ responses, viewed collectively, enable us to report on a broad range of opinions held across employer representatives with expertise on this topic.

In the past year, growth in healthcare spending has begun to increase again, which will likely place increasing pressure on all employers to decrease spending. RBP holds great promise as a strategy to lower spending. Yet without redesign of RBP so as to achieve broader take-up by employers, this promise of RBP appears likely to remain unrealized.

 Harvard T.H. Chan School of Public Health (ADS), Boston, MA; Harvard Medical School (SA, AM), Boston, MA; Beth Israel Deaconess Medical Center (AM), Boston, MA.

 Funding from the Robert Wood Johnson Foundation (grant #73721) for this research is gratefully acknowledged.

 Dr Mehrotra reports that, unrelated to him, Harvard has implemented rewards for its employees, a related form of benefit design. 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 (ADS, AM); acquisition of data (ADS, SA); analysis and interpretation of data (ADS, SA); drafting of the manuscript (ADS); critical revision of the manuscript for important intellectual content (SA, AM); provision of patients or study materials (ADS); and obtaining funding (ADS, AM).

 Anna D. Sinaiko, PhD, MPP, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Room 409, Boston, MA 02115. Email: asinaiko@hsph.harvard.edu.

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5. Sinaiko AD, Mehrotra A, Sood N. Cost-sharing obligations, high-deductible health plan growth, and shopping for health care: enrollees with skin in the game. 

. 2016;176(3):395-397. doi: 10.1001/jamainternmed.2015.7554.

6. Zhang X, Haviland A, Mehrotra A, Huckfeldt P, Wagner Z, Sood N. Does enrollment in high-deductible health plans encourage price shopping? 

. 2018;53(suppl 1):2718-2734. doi: 10.1111/1475-6773.12784.

7. Sood N, Wagner Z, Huckfeldt P, Haviland A. Price shopping in consumer-directed health plans. 

. 2013;16(1):1-19. doi: 10.1515/fhep-2012-0028.

This qualitative study finds low employer adoption of reference-based pricing (RBP) benefit design and that redesign of RBP may be necessary to overcome barriers to adoption.

Why Aren’t More Employers Implementing Reference-Based Pricing Benefit Design?

 Despite public reporting of wide variation in hospital cesarean delivery rates, few women access this information when deciding where to deliver. We hypothesized that making cesarean delivery rate data more easily accessible and understandable would increase the likelihood of women selecting a hospital with a low cesarean delivery rate.

 We conducted a randomized controlled trial of 18,293 users of the Ovia Health mobile apps in 2016-2017. All enrollees were given an explanation of cesarean delivery rate data, and those randomized to the intervention group were also given an interactive tool that presented those data for the 10 closest hospitals with obstetric services. Our outcome measures were enrollees’ self-reported delivery hospital and views on cesarean delivery rates.

 Intent-to-treat analysis using 2-sided Pearson’s χ

 There was no significant difference across the experimental groups in the proportion of women who selected hospitals with low cesarean delivery rates (7.0% control vs 6.8% intervention; 

 = .54). Women in the intervention group were more likely to believe that hospitals in their community had differing cesarean delivery rates (66.9% vs 55.9%; 

 <.001) and to report that they looked at cesarean delivery rates when choosing their hospital (44.5% vs 33.9%; 

 Providing women with an interactive tool to compare cesarean delivery rates across hospitals in their community improved women’s familiarity with variation in cesarean delivery rates but did not increase their likelihood of selecting hospitals with lower rates.

Many states and consumer-focused organizations publicly report hospital cesarean delivery rates, but few women know where to access these data. We conducted a randomized controlled trial of 18,293 Ovia Health mobile app users to evaluate whether directly giving women access to cesarean delivery rates of local hospitals affected their choice of hospital. We found that providing women with this information improved their familiarity with these data but did not change their choice of hospital.

Cesarean deliveries are believed to be overused in the United States, and rates vary widely across hospitals.

Many organizations have begun publicly reporting cesarean delivery rates to help guide women’s choice of hospital.

Giving women an interactive tool on a mobile app to compare the hospitals in their community did not increase use of hospitals with low cesarean delivery rates.

This study illustrates the potential and limitations of using a modern method of communicating with the public to engage women in considering variation in quality of care among hospitals.

Across the United States, hospital cesarean delivery rates vary dramatically, independent of women’s health status, demographic characteristics, or personal preferences.

 Although cesarean deliveries are often clinically necessary, as many as 45% may be unindicated.

 More than three-fourths of women would prefer not to have an unindicated cesarean delivery.

 Compared with vaginal deliveries, cesarean deliveries are associated with 3-fold higher rates of maternal complications and 50% higher costs.

In recent years, consumer advocates such as The Leapfrog Group and Consumer Reports, as well as more than 20 state departments of public health, have begun to publicly report hospital-level cesarean delivery rates. However, research has found that few women know where to access these data. Furthermore, women prioritize the selection of their obstetrician or midwife over selection of their hospital and believe that a hospital’s cesarean delivery rate will not affect the care that they receive.

We hypothesized that making cesarean delivery rate data easily accessible to women who either are trying to conceive or are early in their pregnancy, and then pairing these data with an explanation of how their choice of hospital may affect their odds of having a cesarean delivery, would increase the likelihood of women selecting a hospital with a low cesarean delivery rate. A hospital with a low cesarean delivery rate was defined as one that meets the Healthy People 2020 target of a 23.9% (or lower) cesarean delivery rate for nulliparous term singleton vertex (NTSV) deliveries.

We conducted this trial using 2 mobile apps, Ovia Fertility and Ovia Pregnancy, from the Ovia Health mobile app suite. Trial recruitment and retention are outlined in the CONSORT diagram (

]). We presented advertisements in the in-app information feeds of Ovia Fertility users who indicated they were trying to conceive and Ovia Pregnancy users in their first trimester (

). The advertisements linked to a short article that explained the potential risks of unnecessary cesarean deliveries and the variation in hospital-level cesarean delivery rates (

). Women who clicked on a hyperlink at the end of the article offering the opportunity to learn more were randomized 1:1 through the app to the control or intervention group.

Women randomized to the control group were shown a short article that encouraged considering cesarean delivery rates when selecting an obstetric hospital and explained where to find publicly reported data (text available in 

). Women in the intervention group were shown the same article plus an interactive tool presenting NTSV cesarean delivery rate data for the 10 hospitals closest to their location. Women could also enter another zip code in the tool to see a different set of hospitals. The cesarean delivery rates were self-reported by hospitals to The Leapfrog Group. On the interactive tool, hospitals were color-coded green if they met the Healthy People 2020 target NTSV cesarean delivery rate (23.9%), red if they did not meet the target, and yellow if they did not report their cesarean delivery rate to The Leapfrog Group.

Our primary outcome was the proportion of women who selected a hospital that met the Healthy People 2020 cesarean delivery rate target. Women enrolled in the trial were shown an advertisement in their feed incentivizing them to report their chosen hospital in the app for the opportunity to enter a lottery for a gift card (

). Because any user of the app could report their hospital in their app settings, a small fraction of women in the trial entered their hospital choice before they enrolled in the trial. Our secondary outcomes were responses to 3 survey questions about cesarean delivery rates. The survey was delivered to women enrolled in the trial as another advertisement in their feed (

Limited demographic data were available through Ovia, including enrollees’ age, zip code of residence, and pregnancy risk status, which was calculated by Ovia Health based on their age, body mass index, number of gestations, and a structured, self-reported medical history (

). Using each woman’s zip code, we linked our data set with data from the US Census on median annual household income, the proportion of residents with a bachelor’s degree, and the urban or rural status of the county. We characterized a woman as having “hospital choice” if there was at least 1 hospital that met the target (green) and at least 1 hospital that did not meet the target or did not report (red or yellow) among hospitals that she saw or would have seen (if she was randomized to the control group) based on her zip code.

We conducted a 2-sample, 2-sided test of proportions to compare the proportions of women selecting a hospital that met the target in the control group and in the intervention group. We used the intent-to-treat framework and assumed that women who did not report their hospital did not choose a hospital that met the cesarean delivery rate target. We also performed 3 post hoc sensitivity analyses to test whether the results were consistent after excluding women who reported their hospital before enrolling in the trial, did not report their hospital, or did not have a choice of at least 1 hospital that met the target and 1 that did not in their local area.

We compared answers to survey questions between the control and intervention groups using a 2-sided Pearson’s χ

 test across all survey respondents. Due to a coding limitation in the Ovia apps, survey responses could only be linked to patient demographic information halfway through the trial period (48% of all survey respondents). For this subset of respondents, we compared the proportion answering “yes” to each survey question in the intervention and control groups for each demographic subcategory. All analysis was performed using Stata version 15.0 (StataCorp; College Station, Texas).

The trial was approved by the Institutional Review Board of the Harvard T.H. Chan School of Public Health and registered at ClinicalTrials.gov (NCT02987803). All participants consented through their agreement to the terms of use and privacy policy for the Ovia apps. A Data Safety Monitoring Board (DSMB) comprising experts in the study content and statistical methods reviewed interim results in June 2017.

During an 11-month period (September 2016 to July 2017), we enrolled 18,293 women in our trial (9125 intervention and 9168 control). In our sample, most women were aged 26 to 34 years (61.4%) and lived in an urban county (79.5%) (

). Half (49.9%) of the women resided in a zip code with a median household income of less than $52,000 and half (52.9%) resided in a zip code in which 30% or fewer of the residents held a bachelor’s degree. More than two-thirds of women (67.8%) lived in area with a choice of local hospitals, defined as including at least 1 hospital that met the cesarean delivery target and at least 1 other hospital among the choice set of the 10 hospitals closest to their zip code. Approximately half (54.8%) of women were classified as having a high-risk pregnancy based on self-reported medical history. Sample characteristics were balanced across the control and intervention groups. Compared with the national population of women who give birth annually, our study sample was slightly younger, more likely to reside in lower-income and less-educated areas, more likely to reside in rural areas, and more likely to have a high-risk pregnancy (

One-fourth of enrolled women were recruited via the Ovia Fertility app before they were pregnant. The other women were recruited via the Ovia Pregnancy app and, on average, were 7 weeks pregnant when they enrolled. Almost one-third of the women enrolled in the trial (31.8%) reported their hospital selection in their profile, and they had an average gestational age of 15 weeks at the time they provided this information. Because women could enter their hospital information at any time, 3.2% of the participants reported their hospital before enrolling in the trial. Among women who were pregnant when entering the study and reported their hospital after enrolling in the trial, the average length of time between trial enrollment and hospital entry was 9 weeks.

There was no significant difference between the control and intervention groups in the proportion of women who selected hospitals that reported cesarean delivery rates that met the Healthy People 2020 target (7.0% in control vs 6.8% in intervention; 

). The mean (SD) cesarean delivery rate of hospitals selected was the same among women in the control and intervention groups (26.3% [9.1%] in control; 26.2% [8.9%] in intervention). These results were consistent in 3 post hoc sensitivity analyses, the first of which excluded women who reported their hospital before enrolling in the trial (

), the second of which included only women who reported their hospital information (

), and the third of which included only women who had a choice of at least 1 “green” hospital and at least 1 hospital of another color on the interactive tool (

Of the 18,293 women enrolled in the trial, 3687 (20.2%) answered the 3 survey questions. Women in the intervention group were more likely to believe that hospitals in their community had differing cesarean delivery rates (66.9% vs 55.9%; 

). They were also more likely to say that they looked at cesarean delivery rates when choosing their hospital (44.5% vs 33.9%; 

 <.001). Although women in each group were equally likely to believe that the hospital they chose would affect their chances of having a cesarean delivery (55.6% vs 55.3% for intervention and control, respectively), women in the intervention group were less likely to answer no to this question (22.8% vs 25.9%) and more likely to answer that they did not know (21.7% vs 18.8%) (

We conducted exploratory analyses on the differential impact of the intervention on women’s responses across demographic lines for the 48% of respondents (n = 1778) for whom we could link demographic data to survey responses (

). Although for most demographic subgroups there was no consistent pattern of differential impact of the intervention, the intervention did have a stronger impact on beliefs about cesarean deliveries among women who resided in higher-income areas. Among women residing in areas with median household incomes above $70,000, there was an 18.8-percentage-point difference (

 <.001) between the proportions of the intervention and control enrollees who answered that the hospitals in their community have differing cesarean delivery rates compared with a 3.5-percentage-point difference (

 = .069) among women residing in areas with median household incomes of $38,800 or less.

This randomized controlled trial was motivated by the hypothesis that making cesarean delivery data more easily accessible would encourage and enable women to use these data in the selection of a hospital to deliver their baby. Women in the intervention arm of this trial were significantly more likely to believe that hospitals in their community had different cesarean delivery rates and to look up those rates before selecting a hospital. Nonetheless, they were not more likely to believe that the hospital they chose would affect their personal risk of cesarean delivery, and they ultimately were not more likely to select a hospital with a lower cesarean delivery rate. Notably, the cesarean delivery rate data appeared to have the greatest effect on the responses of women in higher-income areas, who may be better positioned to understand, interpret, and/or utilize the data.

State departments of health, payers, purchasers, and consumer advocates are publicly reporting hospital-level cesarean delivery rates to women who are pregnant or considering pregnancy in the hope that women will use these data to choose a hospital. Many of these existing reports are challenging to access, navigate, and interpret, but our study results show that simply closing these accessibility gaps may be insufficient to influence hospital selection. The lack of impact of the intervention on women’s selection of hospitals may be explained, in part, by a pervasive belief among many pregnant women that hospital-level outcomes have little bearing on how they will deliver their baby. Most women believe that their obstetrician or midwife and their own preferences will drive whether they have a cesarean delivery.

 Although we provided an explanation of why hospital-level data were important, it was clearly not sufficient, as nearly half of women in the intervention group still believed that the hospital they chose would not influence their chances of having a cesarean delivery.

Cesarean delivery rates may be challenging to interpret without a significant degree of health literacy or numeracy.

 Although our intervention was designed to address these potential barriers, the complexity and personal nature of healthcare decisions is a significant challenge in engaging the public with hospital-level quality indicators. In addition, other important determinants of hospital choice, such as insurance provider network, existing relationships with physicians or midwives, and recommendations from friends and family members, may have outweighed the value that women placed on these quality metrics.

Our results should be interpreted in the context of limitations of our study. First, due to design constraints, women were only able to view the cesarean delivery rates of local hospitals once and were not able to save the tool or find it again on the app. Given this limited exposure, women would need to remember the data or actively seek them from another source later when they were making their decision of where to deliver. The 1-time access also limited women’s ability to share the data with others whose advice may affect their decision making, such as partners, family, friends, or their primary care provider.

Second, our ability to encourage women to select hospitals with lower cesarean delivery rates depended on women having this option. One-third of women in our trial potentially did not have the option of choosing from a set of hospitals that had at least 1 hospital that met the Healthy People 2020 target and at least 1 hospital that either did not meet the target or did not report. Moreover, health insurance plans increasingly stipulate preferred provider networks, and although we could not observe these data in our trial, this too may have limited women’s options. The lack of options due to insurance network limits the impact of efforts by consumer groups and governmental organizations to publicly disseminate provider quality information. The results of our trial suggest that the data and information presented were not sufficient to persuade women to select hospitals with lower cesarean delivery rates; however, we cannot definitively conclude whether this was because the women in our trial did not have access to these hospitals or because of the other barriers to using this information, as discussed above.

Third, there were limitations related to our documentation of primary and secondary outcomes. We relied on women to self-report their hospital and observed this primary outcome for 32% of the sample. In addition, only a subsample of hospitals nationwide reports their cesarean delivery rates to Leapfrog (36% of hospitals). For our secondary outcomes, 20% of the women who were enrolled in the trial responded to the survey. To guard against bias from differential nonreporting, we used an intent-to-treat analysis. Ultimately, we did not recruit the full sample necessary to achieve 80% power to detect a 5-percentage-point effect size for our primary outcome; however, our observed effect size was very small and, based on those findings, our DSMB recommended that we discontinue the trial and report these results.

Despite these limitations, our study results illustrate the utility of using a modern method of communicating with the public to engage women in considering variation in quality of care among hospitals. The Ovia Health mobile app platform allowed us to reach a large and diverse sample of women during a critical time frame before or early in their pregnancy. Providing women 1-time access to an interactive tool showing cesarean delivery rates of hospitals in their area was effective in enhancing their understanding of hospital-level differences within their community and their likelihood of looking at cesarean delivery rates during their provider selection. Clinicians should be aware of how their patients are engaging with these media to inform important decisions about their care. Future research should investigate how these types of platforms can be more effectively utilized to help women navigate their options to select hospitals, as well as other decisions that may affect their pregnancy outcomes.

The authors acknowledge Ovia Health and the members of the Data Safety Monitoring Board for their helpful contributions to the study.

 Department of Health Care Policy (RAG, AM) and Department of Obstetrics, Gynecology and Reproductive Biology (NTS), Harvard Medical School, Boston, MA; Division of General Internal Medicine and Primary Care, Beth Israel Deaconess Medical Center (AM), Boston, MA; Ariadne Labs at Brigham and Women’s Hospital and the Harvard T.H. Chan School of Public Health (GG, ACP, NTS), Boston, MA.

 The study was funded by Square Roots. Square Roots is a philanthropic organization focused on improving pregnancy health and wellness that funds programs in academia, public policy agencies, and private companies. Square Roots was not involved in the study design; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit the article for publication. Ms Gourevitch’s time was partially supported by T32HS000055 from the Agency for Healthcare Research and Quality.

 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 (RAG, AM, GG, ACP, NTS); acquisition of data (AM, ACP, NTS); analysis and interpretation of data (RAG, ACP, NTS); drafting of the manuscript (RAG, NTS); critical revision of the manuscript for important intellectual content (RAG, AM, GG, ACP, NTS); statistical analysis (RAG, AM); obtaining funding (GG, NTS); administrative, technical, or logistic support (GG); and supervision (AM).

Rebecca A. Gourevitch, MS, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115. Email: gourevitch@g.harvard.edu.

1. Cáceres IA, Arcaya M, Declercq E, et al. Hospital differences in cesarean deliveries in Massachusetts (US) 2004-2006: the case against case-mix artifact. 

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2. Kozhimannil KB, Arcaya MC, Subramanian SV. Maternal clinical diagnoses and hospital variation in the risk of cesarean delivery: analyses of a National US Hospital Discharge Database. 

. 2014;11(10):e1001745. doi: 10.1371/journal.pmed.1001745.

This randomized controlled trial finds that a hospital cesarean delivery rate comparison tool affects women’s perceptions but not where they choose to deliver.

Does Comparing Cesarean Delivery Rates Influence Women’s Choice of Obstetric Hospital?

Driven by the growth of high deductibles and price transparency initiatives, patients are being encouraged to search for prices before seeking care, yet few do so. To understand why this is the case, we interviewed individuals who were offered access to a widely used price transparency website through their employer.

We interviewed individuals enrolled in a preferred provider organization product through their health plan about their experience using the price transparency tool (if they had done so), their past medical experiences, and their opinions on shopping for care. All interviews were transcribed and manually coded using a thematic coding guide.

 In general, respondents expressed frustration with healthcare costs and had a positive opinion of the idea of price shopping in theory, but 2 sets of barriers limited their ability to do so in reality. The first was the salience of searching for price information. For example, respondents recognized that due to their health plan benefits design, they would not save money by switching to a lower-cost provider. Second, other factors were more important than price for respondents when choosing a provider, including quality and loyalty to current providers.

We found a disconnect between respondents’ enthusiasm for price shopping and their reported use of a price transparency tool to shop for care. However, many did find the tool useful for other purposes, including checking their claims history. Addressing the barriers to price shopping identified by respondents can help inform ongoing and future price transparency initiatives.

The authors interviewed patients with access to a price transparency website. Despite a positive opinion of price shopping in theory, respondents reported barriers to doing so in reality.

Ateev Mehrotra, MD, MPH

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