When Is a Network Adequate? Consumer Perspectives on Network Adequacy Definitions

ABSTRACT

Objectives: Most Americans have insurance that uses managed care arrangements. Regulators have long sought to ensure access to care through network adequacy regulations. However, consumers have largely been excluded from conversations about network adequacy. To our knowledge, our study is the first to assess consumer preferences for various definitions of network adequacy including those aimed at supporting health equity and reducing disparities.

Study Design: We fielded a large and demographically diverse survey of US adults (N = 4008) from June 30 to July 2, 2023. The survey queried respondents about their perceptions of what adequate provider networks look like in the abstract.

Methods: Analyses were conducted using ordinary least squares regression with survey weights as well as t tests.

Results: Consumers were overwhelmingly supportive of standard definitions of adequacy focused on the number of providers and travel distance. Majorities also favored more expansive, health equity–focused definitions such as public transportation access, cultural competency, and lesbian, gay, bisexual, and transgender (LGBT+)–inclusive care. Being a woman; having higher levels of education, worse health, and recent experiences with the medical system; and ease of completing administrative tasks were relatively consistent positive predictors of supporting more expansive definitions. More controversial definitions saw effects of partisanship and LGBT+ identification. Rurality, insurance status, education, and recent experiences with the medical system affected perceptions of reasonable appointment wait times and travel distances.

Conclusions: Our findings indicate that consumers have broad conceptions of network adequacy. Future work should assess consumer trade-offs in resource-constrained settings as well as perceptions of providers and carriers.

Am J Manag Care. 2024;30(9):424-432. https://doi.org/10.37765/ajmc.2024.89601

Takeaway Points

Consumers have largely been excluded from conversations about network adequacy. To our knowledge, our study is the first to assess consumer preferences for various definitions of network adequacy including those aimed at supporting health equity and reducing disparities. Consumers are overwhelmingly supportive of standard definitions of adequacy. Majorities also favor more expansive, health equity–focused definitions. Our findings should encourage regulators to seek more expansive definitions of network adequacy.

• Consumers favor broad definitions of network adequacy.
• Consumers favor more nontraditional definitions of network adequacy than are currently in use in most markets.
• Lesbian, gay, bisexual, and transgender–inclusive conceptions of network adequacy are most controversial.
• More work is needed on the practical limitations of network adequacy regulations.

In managed care arrangements, beneficiaries are strongly incentivized to only seek care inside their network via tiered levels of out-of-pocket costs.¹ As a result, beneficiaries and regulators have a vested interest in ensuring that managed care companies establish and maintain adequate provider networks. Concerns about network adequacy have become particularly important because of the growth of managed care products and the narrowing of provider networks over time.^2-12 Regulators have increasingly recognized the potential impact of inadequate provider networks.^13-20 All network adequacy regulations face 3 major constraints. First, network breadth, on any dimension of networks, requires carriers to commit resources and thus will be directly reflected in the price consumers pay.²¹ Excessive requirements may lead to higher prices or may push carriers to leave, so both regulators and consumers must be mindful that important trade-offs must be made and that not all dimensions of provider networks can be simultaneously maximized. Second, regulators work in an environment with a limited supply of providers that are not perfectly distributed.²² This maldistribution puts natural ceilings on many nuanced elements of the regulation of provider networks. Third, measures of network adequacy come with a variety of measurement and implementation challenges.^18,23 Lastly, it is worth noting that transformation in the provision of care such as the more extensive use of telehealth or transportation benefits may require novel conceptions of network adequacy and how to measure it.¹⁷

All that aside, one of the major obstacles in ensuring an adequate supply of providers is that the concept of network adequacy is vague and there is little empirical work to guide policy makers.¹⁷ These limitations are apparent in the wide range of quantitative standards employed across regulators, markets, and specialties.^17,18,24-27 Although the measures are diverse and a growing body of academic work is assessing network adequacy regimes,^28-31 one input into discussion of network adequacy has been particularly absent: what makes a network adequate in the eyes of consumers. To improve our understanding of consumer perceptions of network adequacy, we fielded a large national survey that queried consumers on this issue. The survey specifically asked respondents about a number of commonly regulated facets of provider networks and whether those components, in their mind, were important contributors to network adequacy.

METHODS

To learn more about Americans’ perspectives on network adequacy, we administered a national survey from June 30 to July 2, 2023, via Lucid (Cint Group AB). Lucid hosts a high-quality survey panel using a double opt-in procedure that relies on quota sampling.³² Lucid was compensated $1.50 per completed response. A total of 7831 respondents opted into the survey; 7596 (97%) consented, and 4008 of those respondents completed the survey (53%). Respondents were eliminated due to failure to pass 2 standard attention checks (46%). Data were weighted on gender, race, income, and education based on the US Bureau of Labor Statistics’ Current Population Survey. The study received approval from the institutional review boards at Texas A&M University.

Dependent Variables: Types of Network Adequacy Requirements Analyzed

Prior to asking respondents about the various components of network adequacy, we briefly introduced them to the topic (see eAppendix [available at ajmc.com]). We then asked them about 12 specific aspects of network adequacy that were identified in a recent survey of state regulations and statutes, including wait and travel times for primary care, specialty care, and mental health providers.¹³ After asking about each specialty type, we asked respondents what time frame they considered to be reasonable. A recent survey of provider network adequacy regulation¹³ noted that a smaller number of regulators had expanded traditional definitions of adequacy. These expanded definitions offer clear benefits for traditionally marginalized populations who often struggle to access health care services in general as well as health care services that meet their specific needs. We thus asked respondents a series of 6 questions that specifically focused on (1) language barriers, (2) accessibility via public transportation, (3) care outside regular business hours, (4) cultural competency, (5) accessibility for people with disabilities, and (6) inclusive care for lesbian, gay, bisexual, and transgender (LGBT+) populations (see eAppendix for details). The eAppendix contains all question wording.

Explanatory Variables

To assess correlates of support for the various facets of network adequacy, we included a number of covariates. We expected that respondents’ health status would show strong correlations with more expansive definitions of adequacy and preference for shorter appointment wait times and travel distances. We measured health status in 2 ways. First, we included a measure for self-assessed health status—a standard 5-point scale from poor to excellent. We also asked respondents whether they identified as an individual with chronic conditions or a disability (binary). We hypothesized that these individuals would support disability access as a component of network adequacy.

Partisanship has emerged as a crucial correlate of many public perceptions related to health care regulation.³³ We included indicators for both Republicans and Democrats in our models, with nonpartisans serving as the reference category. We expected that Republican respondents would be less supportive of expansive definitions of adequacy but especially for provisions for LGBT+ people.

We also expected that recent experiences with the medical system would shape respondents’ preferences such that respondents who sought care from a provider in the past year would be supportive of more expansive definitions of adequacy. We used binary indicators for primary care, mental health care, and specialty care. We also thought that respondents who struggle with administrative tasks, a variable commonly used in the administrative burden literature,^34,35 would be supportive of more expansive definitions of adequacy (5-point scale). The variable is based on the question, “How difficult is it for you to complete such administrative tasks as renewing your driver’s license, registering your car, or signing up for insurance?” with options from extremely easy to extremely difficult (5-point scale).

In addition, we had several assumptions related to respondents from traditionally marginalized communities. First, we expected that minority racial/ethnic group respondents as well as those of lower socioeconomic status would generally favor more expansive definitions focused on underserved populations. We defined minority racial/ethnic group respondents as individuals who indicated that they were Black, Asian, Hispanic, or another category, with White respondents serving as the reference group. We utilized both educational attainment (a 4-point measure: less than high school [omitted], high school, some college, college or more) and income (a 6-point measure: < $14,000 [omitted], $15,000-$24,999, $25,000-$34,999, $35,000-$49,999, $50,000-$74,999, ≥ $75,000) as proxies for socioeconomic status. Moreover, we expected that individuals who did not identify as heterosexual (binary) would be more supportive of the LGBT+-inclusive care component of network adequacy. We also expected that individuals who speak a language other than English at home (binary) would be more supportive of adequacy definitions that include language provisions.

Lastly, we included controls for age, women (binary), rural residency (binary), and insurance status (Medicare, Medicaid, employer sponsored, individual market, with the uninsured as the excluded category; all binary).

Analyses

To assess support for the various items in definitions of adequacy and desired wait time and travel time, we relied on weighted least square regression utilizing survey weights. The approach facilitates comparison and interpretation of the results.³⁶ In several analyses, we also utilized t tests as appropriate. For all analyses, we considered a P value less than .05 statistically significant.

RESULTS

Adequacy and Provider Types

Overall, we found overwhelming public support for definitions of adequacy that focused on timely access to primary care, specialist, and mental health providers (Figure 1). Across the 3 provider types, more than 80% of respondents either somewhat or strongly agreed with the statement that appointments should be available in a reasonable time frame, including 81.9% (95% CI, 80.3%-83.5%) for primary care providers, 82.6% (95% CI, 81.0%-84.1%) for specialists, and 82.6% (95% CI, 81.0%-84.1%) for mental health providers. Conversely, less than 8% of respondents (95% CI, 6.9%-9.1%) disagreed with the statement for primary care providers, and opposition dropped below 6% for specialists (95% CI, 5.0%-7.1%) and mental health providers (95% CI, 4.6%-6.1%). Mean support on a 5-point scale was 4.28 (95% CI, 4.24-4.33) for primary care, 4.31 (95% CI, 4.27-4.35) for specialty care, and 4.36 (95% CI, 4.28-4.36) for mental health care. In our follow-up question, we further inquired about what respondents considered a reasonable time frame for appointment wait times. Here, respondents indicated a mean of 7.31 days for primary care (95% CI, 6.85-7.77), 10.15 days for specialty care (95% CI, 9.65-10.69), and 7.46 for mental health care (95% CI, 7.02-7.89). Differences were statistically significant (P < .001) when comparing primary care with specialty care and mental health care with specialty care.

We found the same overwhelming support from respondents for reasonable travel times for all 3 types of providers (78.6% for specialty care [95% CI, 76.9%-80.2%], 80.5% for primary care [95% CI, 78.9%-82.1%], and 80.6% for mental health care [95% CI, 79.0%-82.2%]). This compares to oppositional responses for less than 5% of respondents for each of the 3 provider types. The respective means for support on a 5-point scale were 4.16 (95% CI, 4.13-4.21), 4.22 (95% CI, 4.18-4.25), and 4.25 (95% CI, 4.22-4.29). The follow-up question indicated that respondents considered maximum acceptable travel distances to be 26.04 minutes for primary care (95% CI, 25.26-26.82), 30.34 minutes for specialty care (95% CI, 29.35-31.34), and 27.49 minutes (95% CI, 26.59-28.40) for mental health care. All comparisons between specialties were statistically significant (P < .001).

Adequacy and Equitable Access to Care

Respondents’ assessment of network definitions related to equitable health care access generally did not score as high as those related to provider type (Figure 2). However, accessibility for people with disabilities scored particularly high, with 86.2% (95% CI, 84.5%-87.4%) of respondents agreeing that adequate networks should contain accessible providers compared with 3.6% (95% CI, 2.9%-4.5%) in opposition. Similarly, language accessibility was an important component of network adequacy for 78.4% of respondents (95% CI, 76.6%-80.1%), with only 6.4% (95% CI, 5.5%-7.5%) opposed. Roughly 2 in 3 respondents considered public transportation access (67.3% [95% CI, 65.3%-69.2%]) and extended office hours (62.0% [95% CI, 60.0%-64.0%]) to be important. Notably, support dropped below 60% for both cultural competency (57.4% [95% CI, 55.4%-59.4%]) and LGBT+-friendly environments (57.2% [95% CI, 55.1%-59.2%]). Mean support on a 5-point scale varied from as high as 4.43 for disability access (95% CI, 4.40-4.67) to as low as 3.67 for cultural competency (95% CI, 3.61-3.72).

Regression Results

We estimated a series of weighted least square models. Table 1 contains the regression results regarding the timely access and travel distance measures for primary care, specialty care, and mental health care (6 measures). All dependent variables were on 5-point scales. Our regression analyses indicate that experiences seeking mental health care over the past year were consistently associated with higher levels of support across all 6 measures (0.179-0.351; P < .002), as were higher levels of education (0.060-0.115; P < .011); women were also consistently more supportive than men (0.101-0.186; P < .009). Conversely, those who struggled with administrative tasks showed lower levels of support across all 6 measures (–0.153 to –0.098; P < .001), as did those with higher levels of self-assessed health (–0.104 to –0.055; P < .013). Other correlates were less consistent. Those seeking primary care over the past year were more supportive only in measures of primary care adequacy (0.127 [travel time] and 0.139 [wait time]; P < .019). Rural residents were less supportive of including wait time standards for mental health care (–0.134; P = .011), and Democrats (compared with nonpartisans) were more supportive of travel time standards for specialty care (0.098; P = .032) and mental health care (0.095; P = .034). Lastly, those with a disability or chronic condition were more supportive of adequacy definitions including primary care travel time (0.113; P = .012) and mental health travel time (0.110; P = .015). Race and ethnicity variables were inconsistent.

Table 2 presents the results for reasonable wait times and travel distances. We found that Republicans (compared with nonpartisans) consistently favored shorter wait times for all 3 provider types (–1.858 to –1.171; P < .049). Democrats (compared with nonpartisans) favored shorter wait times for specialty care (–2.233; P = .012) and mental health (–1.884; P = .001) and shorter travel distances for mental health (–2.051; P = .039) and specialty care (–3.391; P = .003), with primary care just above our cutoff value for statistical significance. Those who sought specialty care in the past year found longer wait times in days for mental health (1.529; P = .003) and specialty care (1.625; P = .001)—but not primary care—and longer travel distances in minutes for all 3 provider types (2.501-3.276; P < .029) more acceptable. Those with higher levels of self-assessed health favored shorter travel distances for primary care (–0.921; P = .026) and specialty care (–1.101; P = .032). Those with disabilities or chronic conditions sought shorter travel distances for mental health providers (–2.393; P = .021). More difficulties with administrative tasks were associated with lower tolerance for travel minutes (–0.944; P < .044) and wait days (–0.629; P = .026) for specialty care.

In terms of insurance coverage, Medicare beneficiaries (–5.505 to –3.785; P < .027) and Medicaid beneficiaries (–6.310 to –4.464; P < .019) favored shorter travel distances, as did those purchasing insurance in the individual market (–6.431 to –5.154; P < .009). Those with employer-sponsored care favored shorter travel distances for specialty care (–3.905; P = .039). All comparisons are with respondents without these types of insurance coverage. Compared with White respondents, Hispanic respondents (–3.292 to –1.493; P < .010) and respondents of other race/ethnicity (–3.527 to –2.340; P < .001) favored shorter wait times across all 3 categories of providers, and Black (–5.825; P < .001) and Asian (–5.373; P < .005) respondents particularly favored shorter travel time for specialty care. Those with higher levels of education were willing to accept longer wait times across all 3 provider types (0.663-1.422; P < .012) and longer travel distances for specialty care (1.752; P = .003) and mental health care (1.066; P < .030). Rural residents were also willing to accept longer travel distances for all 3 types of providers (3.465-5.539; P < .003).

Lastly, our assessments of conceptions of adequacy focusing on traditionally marginalized populations are presented in Table 3. Republicans (compared with nonpartisans) were less supportive of definitions of adequacy including access to public transportation (–0.113; P = .045) and LGBT+-inclusive care (–0.451; P < .001). Conversely, Democrats (compared with nonpartisans) were more supportive of definitions including language access (0.109; P = .025), public transportation access (0.161; P = .001), cultural competency (0.154; P = .003), and LGBT+-inclusive care (0.414; P = .001). Members of the LGBT+ community strongly favored definitions focused on LGBT+-inclusive care (0.523; P < .001) and also favored cultural competency (0.163; P = .024) and public transportation access (0.138; P =.046). Women were consistently more supportive of expansive definitions for language access, public transportation access, disability access and LGBT+-inclusive care (0.129-0.181; P < .003). Higher levels of education were consistently associated with support for inclusion (0.068-0.100; P < .017) except for language access. Respondents with better self-assessed health opposed more expansive definitions of adequacy (–0.078 to –0.047; P < .038) except for cultural competency and more expansive opening hours. Those struggling with administrative tasks were less supportive of more expansive definitions of adequacy (–0.117 to –0.052; P < .019) except for cultural competency. Respondents seeking mental health care in the past year consistently favored more expansive adequacy standards (0.164-0.280; P < .008), whereas those who sought primary care were more supportive of public transportation access (0.166; P = .003) and LGBT+-inclusive care (0.224; P = .001). However, those who sought specialty care were less supportive of the latter (–0.141; P = .013). We found stronger support for LGBT+-inclusive care among Medicare (0.356; P < .001) and Medicaid (0.235; P = .018) beneficiaries and those getting insurance from their employer (0.284; P = .003). Black (–0.146; P = .030) and Asian (–0.204; P = .014) respondents were more unfavorable toward language access.

DISCUSSION

How do consumers define network adequacy, and how do those definitions align with current regulatory approaches? Most states currently have standards for wait times and travel distance in place. We display a selective number of these standards in eAppendix G. We found that wait time and travel standards tended to be somewhat out of line with consumers’ definitions of adequate networks. For example, in California, wait time standards are 10 days for primary care and mental health appointments and 15 days for specialty care providers,³⁷ but a review of Medicaid programs found that wait times ranged from 10 to 45 days for primary care, 10 to 60 days for specialty care, and 7 to 21 days for mental health care.³⁸ Newly proposed standards for federal Affordable Care Act marketplaces specify maximum wait times of 15 days, 30 days, and 10 days, respectively.³⁹ Our findings are similar with regard to travel time (eAppendix G). In addition, we found that a majority of consumers have much broader conceptions of network adequacy than is currently implemented in many jurisdictions, particularly for standards for disability access, language access, and public transportation.¹³

In terms of individual predictors, we found that women consistently favored more expansive definitions of adequacy. This may be because women tend to serve as the primary decision maker for health care decisions in most households.⁴⁰ We also found that seeking mental health care over the past year was a relatively consistent correlate. This may be reflective of the challenges respondents faced when seeking mental health care.³¹ Higher levels of education were also consistently associated with stronger support for the various adequacy definitions. Counter to our expectations, those who struggle with administrative tasks were less supportive of expansive adequacy definitions, as were those respondents with better levels of health. However, people with more administrative struggles tended to care more about travel time. Lastly, for the most controversial component of adequacy, LGBT+-inclusive care, we saw strong opposition from Republicans and strong support by Democrats.

Limitations

Several methodological and substantive limitations apply to our study, including all common limitations of cross-sectional survey research using online panels. However, our data were generated from the panel of a reputable Internet-based survey platform, which is the predominant approach to survey research today. Lucid’s data have also been validated and found to be of high quality.³² Data quality was also improved by 2 attention checks and reCAPTCHA verification. Respondents also answered questions about an issue that they may not have considered previously. This means their opinion may be subject to change as they learn more about the issue. Substantively, we note that our focus here is to learn more about consumer preferences in the abstract. Although, to our knowledge, this work is the first to analyze consumer perceptions on this topic, we know nothing about providers’ thoughts on this issue; we are interested in what makes a network adequate for consumers. As noted previously, we did not highlight or ask respondents about potential trade-offs (such as higher premiums or fewer plan choices, for example). Importantly, we also acknowledge that the realities of provider distribution and opportunity costs prevent regulators from fully aligning network regulations with consumer preferences in the abstract. Future research should particularly focus on how consumers evaluate trade-offs between the various values and which dimensions are important for them, as well as provider and insurer perceptions of regulations. Of course, limitations in provider supply may preclude many regulations from being adopted in the first place.

CONCLUSIONS

Managed care offers many benefits to consumers. However, it entails trade-offs because it imposes various restrictions on consumer choice. One of these restrictions, provider networks, holds significant sway over the ability of consumers to access medical care. Inadequate provider networks can have significant implications for the health and financial well-being of consumers. There are also reasons to believe that inadequate provider networks may further inequities and disparities in health care. Regulators have recognized the important impact of provider networks and have responded with various regulations to ensure adequate networks for consumers. However, to our knowledge, consumers have been completely left out of conversations about what makes a network adequate.

Our findings carry several implications. First, we show that, in the abstract, consumers have relatively broad definitions of network adequacy. However, as aforementioned, network adequacy regulations run into challenges related to costs, implementation, provider supply, and secular developments in the provisions of care, making trade-offs inevitable while setting natural boundaries to the extent that regulation is feasible or even desirable. Second, with regard to consumers, further clarification is needed on the degree to which they value the various dimensions of provider networks in relationship to each other when trade-offs are required. Our findings here provide an important starting point for this future work. Third, we need to gain a better understanding of the practical restraints that network adequacy regulations have both in terms of the available supply and the potential response from carriers avoiding overregulated environments. Lastly, finding appropriate measures to assess the various dimensions of network adequacy remains an underdeveloped field. Nonetheless, more attention to this issue, by both academics and policy makers, is crucial as managed care arrangements continue to serve more and more Americans.

Author Affiliations: Department of Health Policy and Management, School of Public Health, Texas A&M University (SFH), College Station, TX; Division of Health Services Management and Policy, College of Public Health, The Ohio State University (WYX), Columbus, OH.

Source of Funding: Robert Wood Johnson Foundation.

Author Disclosures: 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.

Authorship Information: Concept and design (SFH, WYX); acquisition of data (SFH, WYX); analysis and interpretation of data (SFH, WYX); drafting of the manuscript (SFH); critical revision of the manuscript for important intellectual content (SFH, WYX); statistical analysis (SFH); and provision of patients or study materials (SFH).

Address Correspondence to: Simon F. Haeder, PhD, MPA, Department of Health Policy and Management, Texas A&M University, 4226 Wallaceshire Ct, College Station, TX 77845. Email: sfhaeder@tamu.edu.

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