Anna D. Sinaiko, PhD

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.

Our findings provide insight on why patients are not price shopping for care despite numerous efforts to promote price transparency. These findings suggest that key barriers must be addressed in order to promote ongoing and future price transparency initiatives:

		The idea of price shopping is not familiar to most patients. Price transparency initiatives should remind patients, at relevant times, to shop for care and services

		The structure of current health plan benefits inhibits price shopping. Other forms of health plan benefits, such as tiered networks or reference-based pricing, may be more conducive to shopping for care.

		Price transparency initiatives may provide value to patients beyond price shopping, including tracking claims and deductibles status. Encouraging this type of usage may increase patient engagement with price data.

Growing interest in healthcare consumerism is built on the premise that individuals should make healthcare decisions by using a combination of quality data, price information, and personal preference, just as they would when making other purchases.

 Prior work has studied how individuals (ie, consumers) use quality data to inform their healthcare decisions,

 but little qualitative research has examined how people do or do not use price data. This lack of evidence is notable in light of the recent proliferation of price transparency initiatives. Over 60 publically accessible websites hosted by state-based agencies and hospital associations are now available.

Numerous employers have introduced price transparency tools to their employees,

 which aim to engage individuals with easily accessible price data so they can shop for low-cost providers. Estimates suggest that increased competition from such a shift in demand would drive down overall healthcare spending

Despite the prevalence of healthcare price transparency tools and their potential associated savings, few appear to use such tools or price shop for care.

Recent research on healthcare decision making by those in high-deductible health plans (HDHPs) found that enrollees do not use price information despite the fact that their choice of lower-cost providers could reduce their out-of-pocket costs.

 When they do shop for care, it is often by calling their insurer or provider directly,

 which can make it difficult to compare across providers.

 In theory, online price transparency tools facilitate comparative shopping, but they have had mixed effects in practice. Some study results have shown either a modest reduction in price per service or enrollees switching to lower-cost providers, but many others have shown no effects on the prices of services that patients receive and on outpatient spending.

 One key finding throughout prior research is that individuals have not used price transparency tools as much as anticipated.

The reasons for the lack of price shopping remain unclear. It is possible that individuals view healthcare decisions differently than other consumer decisions and, correspondingly, do not seek out price information. Conversely, they might be interested in using price transparency tools but face barriers to their use. To fill this gap in knowledge, we conducted a series of qualitative interviews with individuals who were offered a popular price transparency tool by their employer. Our goal was to understand respondents’ general views on price shopping, and motivations for shopping, experiences with the price transparency tool, and potential barriers to price shopping for healthcare.

The California Public Employees’ Retirement System (CalPERS) manages the health benefits of over 1.4 million members, including teachers, state employees, retirees, and their dependents. Of these members, roughly 200,000 are enrolled in one of its preferred provider organization (PPO) products. Those enrolled in a PPO product were given the option to enroll in an online price transparency tool starting July 1, 2014. In 2015, we interviewed a sample of the PPO members; these members had a $500 individual or $1000 family deductible, a $20 co-payment for office visits, and 20% coinsurance for outpatient facilities and surgery services for in-network providers.

 Members also faced cost sharing through a reference-based pricing benefits program that began in 2011. Under this program, the health plan paid a fixed contribution toward hip or knee replacement surgeries and select outpatient services (eg, colonoscopy) and members did not pay additional costs above their normal cost sharing if they elected to have their procedure at a preferred facility.

CalPERS’ PPO members were offered Castlight, an online price transparency tool that provides users with information about their out-of-pocket costs for healthcare services, including office visits, procedures, imaging, and labs, at different local providers.

All prices are specific to the user’s benefits design and provide real-time estimates that account for their deductible and out-of-pocket spending during the year. Members can also use the price transparency tool to review their claims history. For some searches, Castlight displays quality data on each provider.

CalPERS launched a consumer engagement campaign to encourage Castlight usage. Members were sent an e-mail from Castlight that invited them to open an online account and to invite other covered family members to access the tool as well. Other promotional efforts included home mailers, a 30-day lottery of iPad giveaways for employee members who created an account, live demonstrations, and additional incentives leading up to open enrollment in 2014. Continued communication with tool users included e-mail alerts when new claims information became available and monthly product updates. Four months after the introduction of the tool, 23% of eligible households had registered for the tool.

Of the roughly 200,000 PPO members who had access to the price transparency tool, CalPERS sent introductory letters to a sample of 200. We chose 200 based on an anticipated response rate with the plan to recruit more respondents if we did not saturate themes. Individuals were invited to participate in a 20-minute phone interview to discuss how they choose where to get healthcare and how price affects these decisions. If they were interested, they were asked to call or e-mail to set up an interview. We provided a $50 gift card on completion of the interview.

We were interested in talking to 3 groups of respondents: 1) those who had never used the price transparency tool, 2) those who had used it infrequently, and 3) those who had used it frequently for a sustained amount of time (defined as more than 5 log-ins over a 6-month period, with each log-in separated by at least 1 month) since the tool was introduced. CalPERS had access to administrative data on who had used the tool and how frequently, but for privacy reasons, these data were not available to the Harvard research team. In order to compensate for low rates of tool usage, CalPERS preferentially sampled and sent letters to those individuals who had used it frequently for a sustained amount of time. Interviews were conducted by 4 study team members (HS, RG, AS, AM) between May and October 2015. All interviews were transcribed.

Phone interviews used a semistructured interview guide that focused on 4 areas: the respondent’s use of the price transparency tool (if any), previous use of healthcare and how he or she selects a provider, opinions about healthcare consumerism, and demographic information. Consistent with qualitative research methods, interviewers had the flexibility to engage respondents in relevant discussion outside of the structured questions.

Interviews were coded using Dedoose (SocioCultural Research Consultants, LLC, Los Angeles, CA), an online platform for analyzing qualitative research. Research team members developed a thematic coding guide around use of the price transparency tool, opinions on shopping for care, and general views of the cost of healthcare. Using Dedoose, a study team member (HS) analyzed the transcribed interviews in conjunction with respondents’ descriptive information using the coded themes. Other study team members (RG, AS, AM) also coded a random sample of interviews to confirm consistency of coding and themes.

This study and its materials were approved by the Harvard University Institutional Review Board.

We contacted 200 CalPERS members who were enrolled in a PPO product and given access to the Castlight price transparency tool through their employer. We interviewed 39 members (20% response rate); 67% were female, 65% were older than 50 years, and 73% had completed an associate’s degree or higher (

). Among respondents, 17 reported using the price transparency tool frequently (defined as 4 or more reported log-ins), 13 reported using it infrequently (defined as 1-3 reported log-ins), and 9 reported never having used it. Our findings did not vary substantively across users versus nonusers or by frequency of use of the tool. Thus, we present our results by themes and highlight the few circumstances where there are notable differences.

Views on the Cost of Healthcare and Price Shopping

Most respondents expressed frustration with the high cost of their healthcare, price variations across providers, and receiving an unexpected, costly bill after receiving care. Many felt like they did not have control over their or their family’s healthcare spending beyond taking the initial step to seek medical care. One respondent said, “‘Control,’ I mean you can decide not to go and get the care, so in that respect, you have control. But at the same time, if you want to be healthy, you really do need to [get needed care], and sometimes that means you have to go do something that’s going to cost you money. And you don’t have any control over what they’re going to charge you.”

Despite this general frustration with costs and a lack of control, a number of respondents were very conscious of their healthcare spending and carefully monitored their family’s spending and deductible status. This tracking of spending was reported both by respondents who had used the price transparency tool and those who had not.

Although relatively few had done so, most respondents had a positive opinion of the idea of price shopping and believed it could improve the value of the care they receive. One respondent said, “I don’t think that it hurts to get an idea [of price] if you’re going to get…comparable quality care at a location that’s gonna cost you a little bit less; then I think it is helpful information.” Some respondents drew parallels between healthcare and other types of purchases: “It’s just like going to get a car: [if] people are out looking around, trying to get the best price, [dealers] are going to drop the price for you because they want your business. I don’t think health care will be any different.” However, this sentiment was not universal. Several respondents did not support shopping for care based on price because they felt that money does not matter when it comes to taking care of their health.

Among those who reported prior price shopping, most attributed it to having gone through a major health event themselves or with a family member. One respondent commented, “I have a very good idea [of how much I spend on medical care]…because a couple of years ago I had a big surprise, and suddenly I started keeping track.”

Perceived and Actual Barriers to Shopping for Care

Despite frustration with the cost of care and optimism about price transparency among most respondents, many cited multiple barriers to shopping for care and using the price transparency tool (

Salience of searching for price information. 

The first set of barriers related to when and for what reason respondents reported they should search for price information. As previously noted, most supported the idea in theory; however, many respondents described price shopping as a foreign concept, as they had never considered using price to choose a doctor or going against a referral from their provider. Among those who had not used the tool, several respondents mentioned they simply forgot to use the price transparency tool when they were searching for a new provider. In particular, it was common for respondents to express disappointment during the interview if they had forgotten to use the tool after being reminded of it. Among those who had used the tool, it was common to have logged on to the tool once or twice out of curiosity but to not have returned to the website since then.

Another theme in the interviews was the belief that proactively searching for price would not impact what respondents would pay out-of-pocket for their care. For example, several respondents noted that it was unimportant to shop for care because they were already beyond their deductible or because their co-payment for an office visit was always the same. Many—in particular, those in rural areas&mdash;felt it was not useful to shop for care because there was a limited set of providers they could see within their health plan’s provider network. Others were skeptical of the usefulness of the price information on the website because it did not account for the tests or referrals the physician might order during a visit.

Perception that other factors are more important for provider choice than price. 

The second set of barriers consisted of factors that respondents judged as more important than their out-of-pocket costs when selecting a provider. Overwhelmingly, respondents did not want to shop for care because of loyalty to their current providers. This was true of those who used the tool and those who had not. Many respondents commented that differences in price were relatively unimportant when it came to their primary care physicians (PCPs) and specialists with whom they had an established relationship. Respondents also judged referrals and input from their own physicians as key reasons for making a given provider decision. 

Most respondents believed that seeking quality care was more important than the location of the facility and far more important than their out-of-pocket costs. However, there was disagreement among respondents on how to choose care based on quality. Some respondents took the provider’s location or the cost of their care to indicate quality: “[For] the more quality [care you receive], you should expect to pay a little bit higher cost as opposed to those [providers] that are discounted and may not have the attention to detail to find out exactly what you’re going in for, what’s wrong with [you]…I don’t have a problem with that.” Yet, many respondents reported that price had little to do with quality. 

Of the notion that higher-priced providers offer higher-quality care, one respondent said, “I don’t agree with that. I’ve had doctors that…provide incredible care and I’ve had other doctors that, you know, they charge huge amounts and you get 5 minutes of their time, and you’re out of there. No, I don’t see a correlation at all [between price and quality].” Respondents also reported overlooking price when it ultimately came to making a medical decision to improve their health and well-being.

Among those who used the price transparency tool, few made a decision based solely on the tool and instead used it as a supplement to other resources to make provider choices, such as online reviews and referrals from friends and family. One respondent described, “I spend quite a long time because what I do is I take the info from Castlight, and…[I try to] find the right match between what Yelp says about the office that Castlight is saying has quality.”

Situations amenable (or not) to shopping for care. 

Respondents reported that shopping for care, using either the price transparency tool or other resources, was reasonable for nonemergent services. Of the respondents who had shopped for care, particularly those who had used the price transparency tool, they most frequently reported doing so for imaging and labs. One respondent described, “I routinely have [an] MRI because…of the [

] gene…and I was concerned about how much it was going to cost…That’s why I went to Castlight.” A few respondents also described using the price transparency tool to find cheaper labs than they were currently using: “I have a condition that requires I have lab work 3 or 4 times a year…The blood work through the particular laboratory [I found on Castlight] was like 15%-16% of what I was paying for the last 5 years at the hospital labs…It’s a huge difference for me.”

Respondents also described situations in which they would not use the price transparency tool. They rarely reported using the price transparency tool to search for a new PCP. A few searched for their own PCP out of curiosity rather than with the intention of making a switch.

Other Uses for the Price Transparency Tool Beyond Price Shopping for Upcoming Care

Articles

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.

Patients' Views on Price Shopping and Price Transparency

Prior studies found that tiered provider networks channel patients to preferred providers in certain contexts. This paper evaluates whether the effects of tiered physician networks vary for different types of patients. 

 Cross-sectional analysis of fiscal year 2009 to 2010 administrative enrollment and claims data on nonelderly beneficiaries in Massachusetts Group Insurance Commission health plans. 

Main outcome measures are physician market share among new patients and the percent of physician’s patients who switch away. We utilized estimated fixed effects linear regression models that were stratified by patient characteristics. 

Physicians with the worst tier rankings had lower market share among new patients who are older and sicker, or male, representing losses in market share of 10% and 15%, respectively, than other tiered physicians. A poor tier ranking did not affect physician market share of new patients who are female or younger. There was no effect of a physician’s tier ranking on the proportion of patients who switch to other doctors among any groups of patients. 

 Loyalty to their own physicians is pervasive across groups of patients. Physicians with poor tier rankings lost market share among new patients who are older and sicker, and among new male patients. Together, these findings suggest that tiered network designs have the potential for the greatest impact on value in healthcare over time, as more patients seek new relationships with physicians.

	This paper evaluates whether the effects of tiered physician networks vary for different types of patients. I find that:

		Patients’ loyalty to their own physicians is pervasive, as all patients were no more likely to switch away from worst-tiered physicians than any other physicians.

		Physicians with the worst tier rankings lost market share among new older, sicker patients. 

		Tiered networks have promise as one of a set of mechanisms to increase the value of healthcare spending.

		Targeting these interventions at patients when they are choosing a new doctor may be more effective than strategies that interrupt existing care relationships.

Tiered physician networks are a managed care network design used by payers to contain healthcare costs and improve value in the healthcare system. Physicians are sorted into strata within the network based on their performance on quality and cost-efficiency measures, and patients are assessed lower co-payments for visits with physicians in preferred (eg, better-performing) tiers. This network design aims to channel patients to preferred physicians by virtue of the quality and value signal provided through the rating system, the financial incentive provided through the lower co-payment, or both simultaneously. Motivating providers to improve their performance so as to improve their tier ranking, either to prevent a loss in market share of patients or simply for its own sake, is a second aim of this network design.

Increases in patient out-of-pocket medical costs are known to decrease the amount of medical care that patients use,

 but the majority of evidence on patient response to changes in physician cost sharing is based on price changes that affect all physician services, not just a select, or tiered, group of physicians. Tiered network plans are relatively common in private health insurance—19% of all firms report offering a tiered network in the health plan with the largest enrollment

—but the evidence on the impact of tiered physician networks on patient choices is sparse. In previous work, I found that physicians with nonpreferred tier rankings and a $10- to $20-higher co-payment for an office visit earned a 12% lower market share of new patients than their top- or average-tiered colleagues, but were not more likely to have their existing patients switch away.

Tiered networks can also focus on hospitals, and inter-tier cost-sharing differences are usually much larger. Scanlon et al found that workers in one union were more likely to select a preferred hospital for medical visits, which allowed them to avoid 5% coinsurance (equivalent to approximately $400 in out-of-pocket payments), while workers in a second union and all patients admitted for a surgical diagnosis were no more likely to choose within the preferred tier of hospitals.

 More recently, Frank et al evaluated the Blue Cross Blue Shield of Massachusetts tiered hospital network, where patients saved $900 out of pocket ($690) if they chose a top-tiered (middle-tiered) hospital relative to the worst-tiered hospitals, finding increased patient use of hospitals on the preferred and middle tiers relative to the nonpreferred tier.

A related form of physician network design are “narrow networks” (also called “limited networks”) that exclude physicians with the worst performance on quality and cost-efficiency measures from the network, and provide little to no reimbursement to patients for healthcare services received from out-of-network providers. Following the narrowing of a PPO network in Las Vegas, Nevada, that increased patient out-of-pocket costs to see excluded physicians from a $10 co-payment to 40% of allowed charges, nearly 9 out of 10 patients of affected physicians switched away.

 However, sicker patients were observed to be more loyal to excluded physicians.

There is no evidence on whether the impact of tiered physician networks varies across patients, and the expected impact is uncertain. On one hand, older and sicker patients are likely to use more medical care and, thus, would have a greater net benefit from receiving care from better-tiered physicians. However, physician concerns about reliability of measurement (eg, if the measures used to evaluate performance do not adequately account for additional complexities and resources needed to treat sicker populations) and misclassification risk may make avoidance of higher-risk patients a potential unintended consequence of tiering.

 Information about response gradients across patients is also important if tiered networks are to achieve their greatest impact, as they will need to channel sicker patients—who are associated with the highest proportion of medical spending&mdash;to higher-value providers. In contrast, savings due to tiered networks will be less if only the youngest, healthiest patients respond. More evidence can also inform the implementation of tiered networks and the focus of marketing and outreach. 

This paper analyzes data from 5 commercial health plans in Massachusetts to test whether the impact of tiered physician networks varies across different groups of patients. Specifically, I assessed whether tier ranking affects physician market share among new patients or the proportion of patients who switch to other physicians for male versus female patients, older versus younger patients, and sicker versus healthier patients.

Group Insurance Commission Tiered Physician Networks

The Massachusetts Group Insurance Commission (GIC) is a quasi-state agency that provides health benefits to over 400,000 state and municipal employees, as well as retirees and their dependents. In fiscal year (FY) 2009 and FY2010, a 3-tiered physician network was included in all GIC non-Medicare health plans, with approximately 20% of physicians in the top tier, 65% classified in the middle, and 15% assigned to the bottom (or worst-performing) tier at each plan. In FY2009, the range of office visit co-payments was from $10 to $15 for the preferred tier, $20 to $25 for the middle tier, and $25 to $35 for the worst-performing tier; co-payments increased by $5 to $10 at all levels in FY2010.

Tier rankings were assigned based on physician quality performance first, and efficiency performance second, using a common, cross-health plan database of performance profiles. Physicians with insufficient data were assigned to the middle tier. Tiered networks in all health plans included cardiologists, endocrinologists, gastroenterologists, obstetricians/gynecologists, orthopedists, and rheumatologists. Plans could choose to implement tiered networks for additional specialties, and several also tiered allergist/immunologists, dermatologists, general surgeons, internal medicine, neurologists, ophthalmologists, and otolaryngology (for additional detail on tiering methodology and specialties, see the 

Administrative enrollment and claims data, for all non-Medicare individuals enrolled in 5 of the 6 GIC health plans over July 2006 to June 2010 (FY2007-FY2010), were obtained. Data included patient age and gender, diagnosis code, and the providing physician’s name, practice tax identifier, and tier ranking. Data also included a physician identifier variable—unique to each physician&mdash;and specialty designation for the providing physician on each claim. This was the same physician identifier used to construct the tiered physician networks by the plans, and allowed for reliable observation of unique physicians across claims data from different health plans. Claims where this identifier was unavailable were dropped (6% of claims).

The analysis sample consisted of physicians in a tiered network in FY2009 and FY2010; the sample included 15,401 physician-year observations, of which 6236 were included in multiple plans’ tiered networks. The main outcomes were the physician’s market share among new patients, stratified by patient characteristics, and the percent of a physician’s patients that switch to another doctor, also stratified by patient characteristics.

Following methods published previously, all office visits with tiered physicians were assessed for whether they were with a new physician or if the patient was a “potential switcher.”

 Patients who switched health plans during this period were excluded, because switches could be based (at least in part) on tiering (eg, to switch to a plan where one’s physician has a better tier ranking) and subsequently bias the results. A patient is defined as “new” if neither the patient nor any other person in the patient’s family had a visit within the same specialty in the previous 2 years. “Potential switchers” are patients who had at least 2 visits in prior years with a specific tiered physician and then had an office visit with any physician of the same specialty type in FY2009 or FY2010. Patients who saw a new, different doctor are classified as switchers. Patients who return to see the previous doctor are classified as non-switchers.

The effect of tiering on these outcomes was assessed within 6 subgroups of patients: 1) “older” patients, who were 46 years or older—the median patient age of patients of tiered physicians; 2) “younger” patients, who were younger than the median age; 3) “sicker” patients, who had a diagnosis in the study year or earlier in one of the 70 condition categories identified for risk adjustment in the Medicare CMS-Hierarchical Condition Categories (HCC) models

—this method for risk-adjustment was used because it focused on identifying individuals expected to have higher than average medical expenditures, and because it does not require information about the Diagnosis-Related Group assigned to hospitalizations, which was not available in our data; 4) “healthier” patients, who did not have any of these prior diagnoses; 5) males; and 6) females.

Each physician’s market share of new patients by plan and patient group was calculated (eg, Dr Smith’s share of new male patients in health plan X, Dr Smith’s share of new male patients in health plan Y) as the physician’s total number of new patients within a patient group and plan (eg, the number of Dr Smith’s new male patients enrolled in health plan X) divided by the total number of new patients in that group and enrolled in that plan (eg, the total number of new male patients enrolled in health plan X). The percent of a physician’s patients by plan and patient group who switched away was equal to the number of switchers among each group of patients and each plan (eg, the number Dr Smith’s male patients in plan X who switched away) divided by the number of potential switchers in each group and plan (eg, the number Dr Smith’s male patients in plan X who were potential switchers). To avoid misclassifying visits where a patient is seeking a second opinion as a switch, in sensitivity analyses a requirement was imposed that a switch entail a minimum of 2 visits with the new physician in a year.

Following earlier methods, I estimated multivariate models to analyze the impact of tiering. Models included dummy variables for plan, year, and specialty to control for differences in plan benefits, generosity, and tiered-network structures. The key empirical fact is over half (53%) of physicians tiered by at least 2 GIC health plans had different tier rankings across plans (eg, Dr Smith was ranked in the most-preferred tier in Plan A and the middle tier in Plan B in the same year). These differences occurred because the actual cut-points between tiers varied across plans, plans may have considered additional data (beyond the cost and quality ratings calculated from all-payer data aggregated by the GIC) on performance when determining tier rankings, and because plans with more selective or smaller networks may have ranked the same physician lower (in percentile terms) than a broad network simply because they excluded lower-performing physicians from the network. Models included a dummy variable for each physician to control for unmeasured differences among physicians that might differentially attract new patients (eg, reputation). With this control, the coefficient on the variable indicating a physician’s tier ranking is the effect of tiering.

Results are presented as predicted probabilities based on regression models. Additional detail on data and methods is provided in the eAppendix. The Harvard T.H. Chan School of Public Health Institutional Review Board approved this study.

Among patients who had an office visit with a tiered physician in FY2009 or FY2010, 97,896 patients were new patients and 109,270 were potential switchers. New patients were more likely to be female (63%) and 41% had a prior diagnosis of a major medical comorbidity (

). Potential switchers had similar characteristics, but were sicker (eAppendix Table).

Physicians with the worst tier rankings (eg, they had the lowest performance scores and highest office visit co-payments) earned lower market shares of new patient visits among male patients and new older patients (

). In contrast, tier rankings did not affect physician market share of new patients who were female or younger. Relative to their average-tiered colleagues, physicians with the worst tier ranking have a predicted market share of new male patients that is 0.08-percentage points lower (market share of 0.76% vs 0.88%), and of new older patients that is 0.12-percentage points lower (market share of 0.81% vs 0.69%). Although the magnitude of these differences is small, it is meaningful on a relative basis as it represents losses in market share of 15% among new male patients (ie, [0.89%-0.76%]/0.89% = 15%) and 15% of older patients.

Physicians with the worst tier rankings earned a lower market share of new patients with and without a major medical comorbidity; however, age and the number of comorbidities are highly correlated. To assess which of these characteristics is driving the results, I looked within the group of sicker patients and analyzed older patients versus younger patients. Physicians with the worst tier ranking did not lose market share among sicker, but rather, younger patients relative to their average-tiered colleagues (Table 2). However, among older and sicker patients who were selecting a new physician, physicians with the worst tier rankings experienced market share that was 0.11-percentage points lower than that earned by their top- and average-tiered colleagues. This is equivalent to a relative loss in market share of 10% among these patients. 

There was no effect of physician tier ranking on the proportion of a physician’s patients who switch to other doctors within any of the groups of patients. This result was unchanged in sensitivity analyses where patients were classified as having switched physicians only if they had a minimum of 2 visits with the new physician in a year.

This paper is one of the first to examine whether tiered physician networks have different effects on different types of patients. Patients’ “stickiness” to their own physicians is pervasive, as all patients—including men, women, and patients who are older, younger, sicker, or healthier&mdash;were no more likely to switch away from lower-tiered physicians than higher-tiered physicians. When choosing a physician for the first time, however, tiered physician networks channeled new older, sicker patients, and new male patients away from tiered physicians with the worst ranking.

Multiple mechanisms could be at work, as patient choice of new physician could be a result of patients deciding for themselves, physicians using tier-ranking information in their decisions about where to refer their patients, or both. Regardless of whether certain demographic groups or their physicians are more likely to make different choices, the effect of tiering is that the worst-ranked physicians earned lower market share of certain groups of new patients. Another question arising from these findings is why tiering consistently channeled groups away from the worst performers with no movement between the average and the best tier levels. One explanation, is that individuals evaluate options not in terms of absolutes, but relative to reference points. Thus, patients may simply want to avoid physicians with the worst rankings but not move all the way to the top tier. It is also possible that a low number of top-tiered physicians, and capacity constraints in their practices, will prevent patients who want to choose them from doing so.

The most prevalent conditions observed among the “sicker” patient subgroup were diabetes, heart disease, and depression (Table 1)—3 chronic conditions associated with high use of the healthcare system and older age. The finding of the responsiveness among this patient group makes economic sense, as it suggests that tiering is having an effect on choices among the population most likely to consume more care and, thus, who has more to gain from choosing a higher-performing physician and more to save with lower co-payments if they expect to have multiple visits. It is also possible that these patient flows are due to actions on the part of the worst-tiered providers to avoid these patient groups as older patients with these conditions may be more complicated. Further research should investigate this question. 

The finding that tiered networks are channeling male patients, but not women, is more surprising. Men and women are known to have different rates of utilization of healthcare services across all types of care due both to healthcare needs and behavioral and attitudinal differences.

 Kozhimannil et al found that men were more responsive to the introduction of a high-deductible health plan than women, cutting back on use of care—specifically emergency department visits&mdash;more than women, and suggesting that men respond to cost-sharing incentives in health insurance differently than women.

Within a tiered-network design, patients can continue to see nonpreferred physicians if they are willing to pay a higher co-payment for each visit. Unlike “narrow network” health plans, which provide patients with almost no coverage for services provided by out-of-network providers, tiered networks allow patients to have continued access to a broad network of providers for nominal increases in cost sharing, and, therefore, are likely to be preferable to many consumers who value having a choice of physician. If tiering providers in a network, instead of excluding them, can still channel patients to more efficient, higher-quality physicians, they offer a tool to improve value that is less severe than narrowed networks. 

For physicians, the fact that tiered networks could channel patients with the highest medical spending away from certain physicians could be, in some cases, to the advantage of those facing global budgets. However, under new payment models, such as accountable care organizations (ACOs), where physicians are financially accountable for the care their patients receive outside the ACO, as well as within (often called “patient leakage”), these selection effects may be unfavorable. In fact, these findings suggest that tiering is potentially a tool to encourage high-value choice in the context of ACOs and similar accountability models. Tiered network designs could be used to encourage patients to seek care within an ACO, for example, by sorting specialty physicians into tiers according to their ACO affiliation so that patients would pay lower co-payments for visits to physicians within their ACO, thereby aligning patient incentives with those of the ACO providers. Currently, there are no such incentives for patients to seek care within ACOs. 

There are a few important limitations. This analysis uses data from the late 2000s, which was a different environment than exists today. However, these data are from a unique natural experiment, and as the prevalence of tiered networks has continued to grow, understanding variations in impact within subpopulations of patients has remained an unanswered question. The focus of this study is a commercially insured, employed population in 1 state. Although GIC beneficiaries consist of a diverse range of workers, the study setting may limit the generalizability of these findings. The financial incentives in the tiered networks studied here are minor, and, thus, this analysis is not a test of the impact of tiered networks when incentives are large; however, many tiered physician networks in commercial plans include financial incentives of this magnitude. The study period focuses on the first few years of the GIC tiered networks initiative, and choices of physician may change as consumers become more familiar with the networks in their health plans, and as healthcare markets change. Finally, data limitations prevent the study of whether tiered networks vary for patients along other important dimensions besides age, gender, and health status, such as income, geography, and racial/ethnic gradients, which are also important in evaluations of the costs and benefits of tiered network designs.

Although there is no easy solution to reducing cost and improving efficiency in healthcare, tiered networks seem to have promise as a part of a set of mechanisms to increase the value of healthcare spending—particularly among those patients associated with the highest proportion of medical spending. Targeting these interventions to encourage patients to make higher-value choices so they reach patients when they are choosing a doctor to see for the first time may be better received by patients, and be more effective than strategies that interrupt existing care relationships. Future work should focus on potential adverse effects of this network design, such as provider avoidance of high-risk patients or patient decisions to stop going to the doctor altogether rather than to switch to a lower-cost one. Such evidence will allow for refinements to the design and implementation of physician networks to maximize their benefits while limiting harm and inequity.

 Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA.

 Funding from the National Institute for Health Care Management (NIHCM) and the Health Care Financing and Organizations (HCFO) Initiative at the Robert Wood Johnson Foundation is gratefully acknowledged.

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

Health plans with tiered physician networks channel patients associated with the highest proportion of medical spending to higher value providers.

Variations in Patient Response to Tiered Physician Networks

	The Medicare Advantage (MA) program continues to grow and thrive, despite plan payment cuts imposed through the Affordable Care Act. What explains this surprising outcome (one that is strikingly different than the experience of MA plans in the late 1990s, when payment cuts led to dramatic shrinkage in enrollment and curtailment of plans)? This analysis argues that a combination of factors, including the way payment cuts were imposed, the plan offerings, the characteristics of beneficiaries, and the way they make choices together explain the program’s current health. Understanding these factors is important for MA, Medicare, and, more generally, for participants in new payment models.

	Despite the recent introduction of reductions in plan payments, the Medicare Advantage (MA) program continues to thrive. Four factors explain MA’s continued growth: 

		Unlike the payment cuts to plans in the late 1990s, cuts in plan payments legislated in 2010 are less severe and are being phased in over time. 

		Medicare beneficiaries today have more experience and comfort with managed care. 

		There is vastly improved variety and quality in MA plans. 

		Cognitive biases in Medicare beneficiary decision making—including the tendency for beneficiaries to stay in their health plans over time&mdash;are likely favoring MA plans.

The Medicare Advantage (MA) program gives Medicare beneficiaries the option to choose a private health plan instead of fee-for-service (FFS) traditional Medicare. However, MA plans must provide benefits that are at least actuarially equivalent to traditional Medicare. The vast majority of MA plans have been managed care plans, primarily HMOs, which use primary care gatekeeping, utilization management, and selective provider networks to reduce healthcare spending. In exchange for these restrictions, MA beneficiaries typically avoid either the substantial cost sharing in traditional Medicare or paying an additional premium for supplementary coverage. MA beneficiaries also usually enjoy coverage for some additional services, such as vision and hearing. MA plans fully cover their enrollees’ care, receiving in return a risk-adjusted, monthly, per enrollee payment from the Medicare program.

MA was developed to provide beneficiaries with meaningful choices beyond traditional Medicare and to introduce managed care’s benefits and cost savings to Medicare. The metrics for MA’s performance on these dimensions include the range of available plans and their quality, enrollments, and program costs—to the government and to beneficiaries&mdash;relative to traditional Medicare. MA’s performance on these indicators over the 30-year lifespan of the program has been mixed: at several stages, MA has been “overpaid,” receiving more than traditional Medicare would have received for the same beneficiaries.

Recent legislative attempts to correct for overpayments to MA have yielded a response strikingly different from an earlier effort’s results. After the MA-plan payment cuts imposed through the Balanced Budget Act of 1997, HMO availability dropped by nearly 50%: enrollment fell from 16% of the market in 1999 to 12% in 2002. In contrast, the MA market scarcely shrank at all in response to recent legislative “belt tightening” imposed through the Affordable Care Act (ACA) of 2010 (legislation also known as “Obamacare”). MA enrollment, now at its highest level ever (28% of total Medicare) is continuing to grow.

 Moreover, the disparity in health status of beneficiaries between MA and traditional Medicare—MA beneficiaries are, on average, healthier and lower-cost, which has been a competitive advantage for MA&mdash;has shrunk from that in earlier years.

What explains MA’s robust health and the restrained consumer response to these recent payment cuts by the government? Four factors provide an answer: first, the reductions in plan options expected to follow ACA payment cuts has likely been tempered because, unlike the payment cuts to plans in the late 1990s, the 2010 cuts are less severe and are being phased in over time. They are also combined with concurrent direct bonus payments to plans, depending on each plan’s quality rating. On net, MA plans today receive payments 6% above beneficiaries’ expected FFS costs (

Second, MA beneficiaries today differ notably from those in the 1990s, as they have had much more and better experience with managed care. When MA plans terminate, despite Medicare policy that “nudges” them to traditional Medicare as the default option, the vast majority of MA beneficiaries indicate their persistent preference by actively choosing to enroll in another MA plan.

Third, the products offered today by MA are superior to those of the past. Since 2003, many plans have offered more expansive physician networks, such as preferred provider organizations (PPOs) and private FFS plans. Today, 95% of Medicare beneficiaries have access to a local HMO or PPO, and 71% to a regional PPO.

 Access to private FFS plans, however, has declined given that (as of 2011) these plans were required to establish provider networks. The measured quality of MA plans, including patient satisfaction, meets and at times exceeds that of traditional Medicare.

 Beneficiary comfort with managed care and the variety and quality of plans have likely led significant numbers of newly eligible beneficiaries to choose an MA plan rather than traditional Medicare.

Fourth, multiple cognitive biases afflicting consumers’ decision making, such as status quo bias (the behavioral proclivity of individuals to persist with current choices when alternatives might be superior), are likely favoring MA plans.

 A further example: Medicare beneficiaries tend to value plan premium dollars more than total out-of-pocket dollars.

 MA plans, designed to capitalize on these tendencies, have shrewdly kept their premiums low, including offering “zero-premium” plans, while introducing other revenue-enhancing and cost-saving measures, including higher cost sharing and narrower physician networks.

 Without higher premiums to induce them to depart, many MA enrollees simply continue with their MA plans.

MA plans have suffered 3 competitive blows in recent years: a substantially reduced premium-surplus relative to traditional Medicare, a requirement to create provider networks, and a less favorable selection of clients. Nonetheless, MA plans are thriving, with a record percentage of enrollees. Unless cognitive biases are extremely powerful, it seems that MA plans represent a more attractive choice for a major fraction of beneficiaries.

The MA experience has broad implications for Medicare. One projection holds that many of the pilot accountable care organizations (ACOs) currently being established will evolve into private plans that accept full risk. How will these ACOs manage beneficiaries’ unrestricted choice of healthcare providers and minimize “leakage” (such as office visits) to providers outside the contracting organization? The MA experience is instructive. Successful ACOs will design incentives that reward clients for their participation and create a choice architecture that recognizes consumers’ decision-making biases. For example, such ACOs may make significant efforts to direct members to providers within the ACO when they are choosing a new doctor. Then, due to a combination of satisfaction and inertia, these patients will be likely to continue with these physicians in the future.

For organizations that provide health insurance, skillful melding of patients’ preferences and practices to promote choices that lead to cost-effective care is a likely recipe for success. 

Department of Health Policy and Management, Harvard T.H. Chan School of Public Health (ADS), Boston, MA; Harvard Kennedy School, Harvard University (RZ), Cambridge, MA. 

Research for this paper was supported by the National Institute of Aging (P01 AG032952).

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 (ADS, RZ); acquisition of data (ADS); analysis and interpretation of data (ADS, RZ); drafting of the manuscript (ADS); critical revision of the manuscript for important intellectual content (ADS, RZ); administrative, technical, or logistic support (ADS).

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

1. Kaiser Family Foundation. The role of Medicare Advantage.

. 2014;312(10):990. doi:10.1001/jama.2014.10090. http://jama.jamanetwork.com/article.aspx?articleid=1902237. Accessed October 2015.

2. Sinaiko AD, Zeckhauser R. Selection effects versus default power: the choices of terminated Medicare Advantage clients [unpublished working paper]. Harvard University; 2014.

3. Report to the Congress: Medicare payment policy. MedPAC website. http://medpac.gov/documents/reports/mar14_entirereport.pdf. Published March 2014. Accessed August 19, 2014.

4. Newhouse JP, McGuire TG. How successful is Medicare Advantage? 

5. Sinaiko AD, Afendulis CC, Frank RG. Enrollment in Medicare Advantage plans in Miami-Dade County: evidence of status quo bias. 

6. Abaluck J, Gruber J. Choice inconsistencies among the elderly: evidence from plan choice in the Medicare Part D program. 

Payment policy, health plan characteristics, and Medicare beneficiary characteristics come together to foster continued growth in the Medicare
Advantage program.

Anna D. Sinaiko, PhD

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