Pam Silberman, JD, DrPh

 Scholars have highlighted the importance of preventing hospital admissions and readmissions for individuals with costly chronic conditions. Providing effective care management strategies can help reduce inpatient admissions, thereby reducing rising health care costs. However, implementing effective care management strategies may be more difficult for independent physician associations (IPAs) that contract with multiple organizations that have competing interests and agendas. This study aims to identify and investigate strategies that facilitate the implementation of evidence-based best practices among IPAs.

 The research synthesized peer-reviewed literature to identify best practices in chronic disease management for Medicare beneficiaries. Subsequently, 20 key informant interviews were conducted to explore barriers and facilitators in adapting these best practices in IPA settings. Informant interviews were conducted with 3 key groups: executives, medical directors, and care managers.

 Key informant interviews were conducted to explore barriers and facilitators in implementing best care management practices.

 Key informants provided unique insights regarding the challenges of implementing best care management practices among IPAs. These challenges included implementing and sustaining the operations of evidence-based care management programs while maintaining contractual obligations to health plans, engaging physicians in large and diverse networks, and building high-touch programs in large geographic areas using risk-stratifying algorithms.

 IPA managed care organizations require unique considerations in regard to selected strategies used to manage chronic disease in Medicare populations. These considerations are critical for optimal management of the population, particularly in a risk-based or pay-for-performance environment.

Risk-stratification measures are a key component in efficiently allocating limited resources toward strategies that reduce inpatient utilization; using social determinants to enhance accuracy shows promise and warrants further exploration.

This research points to the importance of the revenue model of the independent physician association (IPA) and its contractual obligations with health plans in assessing the likelihood of success in implementing best practice—based care management programs.

Engaging physicians in best-practice programs may be linked with improved results; engagement of physicians in large networks may be more effective with high-volume or exclusive IPA physicians.

Despite decades of upward-spiraling health care costs in the United States, health care spending continues to outpace gross domestic product and will comprise an estimated 19.9% of gross domestic product by 2025.

 To mitigate the increase in spending, scholars have highlighted the importance of preventing hospital admissions, readmissions, and emergency department (ED) visits for individuals with chronic conditions, a population that accounts for a large percentage of the nation’s health care spending. Approximately 171 million Americans are expected to have at least 1 chronic condition by 2030, a 37% increase from 2000.

 Individuals with 5 or more chronic conditions spend 14 times more health care dollars than those without any chronic disease.

 As such, we need cost-effective strategies to manage this rapidly growing population.

Providing effective care management strategies and implementing best practices may help reduce ED visits, inpatient admissions, and readmissions, thereby reducing rising health care costs and increasing quality. We conducted an international systematic literature review of peer-reviewed research, which identified 6 best practices shown to significantly reduce hospital admissions, readmissions, or ED visits: (1) face-to-face interactions with patients and health care workers (primarily in the home),

 (2) multidisciplinary care teams that include social workers and pharmacists,

 (3) active engagement of physicians in care management practices,

 (4) disease-specific care programs (eg, congestive heart failure, chronic obstructive pulmonary disease),

 (5) transitional care models from inpatient to community settings,

 and (6) risk stratification of populations into subgroups with defined clinical and social needs

Because independent physician associations (IPAs) contract with multiple provider organizations and health insurance plans, IPAs implementing these 6 best practices in care management may experience unique structural and operational challenges. The challenges reported by IPAs include selecting, implementing, and sustaining the operations of evidence-based management strategies while maintaining contractual obligations to health plans; engaging physicians in large and diverse networks; and building high-touch care management programs in large geographic areas. Although IPAs comprise a significant portion of provider networks across the United States—it is estimated that 54% of managed care plans and 90% of point-of-service plans deliver care through IPAs&mdash;little research (only a few IPA-specific studies in the past 2 decades) has been conducted on how they implement evidence-based strategies, and very few of these studies focused on care management practices after the 1990s. Using key informant interviews, we explored the strategies that IPAs use to implement value-based best practices.

Between May and August 2018, we conducted 20 semistructured interviews exploring barriers and facilitators to implementing the care management practices identified within the literature review (

). Interviews discussed the ways in which stakeholders, internal pressures, and external (imposed) requirements contributed to the success or failure of implementation procedures for best practices among IPAs (for research questions, see 

). This study was approved by the University of North Carolina’s institutional review board, and informed consent of all study subjects was obtained.

The interviews were conducted with informants holding key roles within IPAs. Informants included (1) executives in leadership and decision-making roles (n&thinsp;=&thinsp;5), (2) medical directors who implement and provide clinical oversight of the best practices (n&thinsp;=&thinsp;6), and (3) case managers/nursing leaders who provide direct patient care (n&thinsp;=&thinsp;9). Respondents represented 8 IPAs (7 located in California and 1 on the East Coast). IPAs were identified through their participation in a large national association. The average length of each interview was 60 minutes.

The interviews were audiotaped and transcribed. Using Nvivo12 Plus (QSR International), 4 interviews (at least 1 from each key stakeholder group) were coded independently by 2 coders, achieving a final κ statistic of 0.66.

Many of the respondents explained that the organization’s revenue model and value-based contract arrangements heavily influenced which care management programs were ultimately adopted—this often occurred independent of evidence review (

). All stakeholder groups reported choosing programs, in part, based on the type of performance measurement required or rewarded by the payer. This implies that some IPAs base their investment in care management strategies on what is measured and incentivized by payers. For example, health plans that receive financial rewards for increasing preventive screenings (eg, colonoscopies) may incentivize or contractually require the medical group to concentrate its limited resources on improving this metric, often to the exclusion of other needs. As a result, this may leave fewer resources for investing in chronic care strategies, such as home visits, which would focus on reducing acute inpatient utilization and streamlining care transitions.

One respondent explained that his IPA selects interventions and programs to implement based predominantly on what the health plan contracts dictate—which may not be based on evidence-based best practices or the needs of their specific populations. An executive respondent explained, “In all honesty, we do certain things because the contract directs us to do it that way.” Another executive commented on the importance of the IPA’s revenue and risk arrangements in selecting care management strategies: “[T]he financial model is determining whether or not certain interventions are cost-effective. So even though they’re shown to work statistically if implemented with fidelity, they are not necessarily cost-effective for everyone because of the risk environment.” Executive respondents and medical directors suggested that IPAs that are not capitated on a full-risk basis for a population often struggle in sustaining care management programs, which take a longer time to show results and returns. Thus, it appears that IPAs may focus on achieving the metrics for which they are being paid and that IPAs with revenue models that provide up-front funds to care for their members are more likely to succeed in the implementation and sustainability of best practice—based programs.

Physician engagement refers to the provider’s willingness to implement and sustain best practices among their patient population. Stakeholders reported that physician engagement was linked with improved results across the care management practices (Table). According to a majority of respondents, the contractual relationship with the IPA and the volume of patients that the physician has within a particular IPA influenced both their capacity and their willingness to engage in best practices being deployed by the IPA.

Physician contract type (exclusive, employed, open contract) and volume of patients per physician attributed to a single IPA were the 2 features most often cited as affecting physician engagement as it relates to the implementation of best practices within individual practices. Respondents noted an increased engagement and willingness to participate in IPA-dictated best practices when the physician was either exclusive with the group or had a high volume of that IPA’s patients. Respondents stated that high-volume IPA providers tended to be more engaged in care management programs than low-volume providers. One medical director said, “If they get 50[%] or 60[%] or 70% of their patients through 1 IPA, it makes cooperation and engagement easier.” Other respondents expressed that employed physicians were also more likely to refer to available best practice—based programs.

Risk Stratification for High-Touch Programs

Respondents noted that for high-touch programs (eg, face-to-face home visits) to be cost-effective, visits needed to focus on patients most likely to benefit. However, IPA strategies for selecting individuals for high-touch programs varied widely (Table). Some care manager respondents discussed targeting the elderly and frail for home visits, whereas others homed in on those with recent ED visits and/or hospitalization history. A few care managers said that the selection process was based on the individual patients’ needs, whereas others used a variety of automated risk-stratification algorithms that included a combination of the previously mentioned factors.

The most common risk stratification tool identified by participants was the LACE index scoring tool, which is a measure of future readmission risk after an inpatient stay. The LACE score was used to determine eligibility and placement into care management programs. Other methods of risk stratification commonly used by the IPAs in this study included proprietary algorithms that are based on factors such as utilization history, age, and information from disease registries.

All participants utilized some form of risk stratification, although only 2 respondents claimed to incorporate social determinants into their methodology. The 2 respondents using social factors believed that it was helpful in identifying patients most likely to benefit. One medical director explained, “Social determinants play a huge role; we have populations with a history of substance abuse who do not have safe homes, [and this information] helps us determine which programs [to use] and what the patient needs.” For the few that used social determinants as part of their stratification method, the IPA most commonly used the status of homelessness (yes or no) as the social determinant considered.

Revenue Model May Dictate Best-Practice Use

Contractual obligations and financial incentives between health plans and IPAs greatly influence which care management programs IPAs adopt, regardless of whether that particular strategy is considered a best practice or is most appropriate given the needs of a specific population. It is therefore paramount that financial rewards are appropriately aligned with evidence-based findings and attuned to the needs of local populations. Further, our findings support the concept that IPAs that do not assume full risk and/or that participate in revenue models with limited up-front revenue are less likely to adopt and sustain best practices that require substantial up-front investment costs. This in turn limits the type and scope of effective programs that can be implemented. Revenue models that support and sustain care management strategies should be proactively evaluated by stakeholders.

IPAs that do not assume full risk generally have less flexibility in spending their limited resources. This decreases the opportunity to invest in programs that either are more effective or require extended periods to demonstrate benefit. For the latter, IPAs could explore the duration of funding to ensure adequate time to accurately assess program effectiveness.

Understanding the structure of an IPA’s revenue model helps determine which types of care management strategies can successfully be implemented within its environment. Models associated with effective best practice use include full-risk capitation, global capitation, and other value-based models that reward based on health care outcomes while providing the revenue to care for the patient prior to spending on services. Alternatively, models without up-front investment were more likely to result in failed programs either during or shortly after implementation. This may be the case because it is more difficult to finance and sustain these care management best practices for longer periods of time. It is therefore recommended that prior to developing a care management strategy, IPAs evaluate their revenue model, contractual obligations, and the sustainability of funding necessary to support the program and see results.

Physician Engagement Used to Improve Care Management Program Outcomes

Because physician engagement and active involvement in care management programs have been linked with improved results, developing a physician engagement strategy is critical for IPAs. However, knowing which physicians to target in a diverse network spread over a large geographic area can be a formidable challenge.

Because it has been found that a robust network of exclusive, employed, or high-volume providers are better positioned to both administer best care management strategies and sustain them over time, it is recommended that IPAs target high-volume providers and exclusive providers first as a tiered approach when attempting to implement best-practice strategies across broad and diverse physician networks and patient populations.

Risk Stratification as a Tool to Increase Efficacy of Best Practices

Respondents discussed the high cost of implementing face-to-face care management programs, especially if patients are geographically dispersed, often leading to the failure of otherwise effective initiatives. To address this, the IPAs that were able to implement this best practice successfully targeted individuals who were most likely to benefit from the intervention using a risk-stratification system. Focusing in-person care management on those most likely to benefit helped IPAs demonstrate a positive return on investment. Therefore, it is recommended that IPAs take advantage of evidence-based risk stratification systems to identify patients who will benefit from resource-intensive interventions such as home visits. Those groups using social factors as a key piece within their risk stratification systems may have an advantage, as chronic disease is often associated with certain social determinants.

 It is recommended that future research might compare various risk-stratification methods, particularly those that integrate social determinants.

There were several limitations to this study. First, all but 1 IPA was on the West Coast, which may limit generalizability of the results. Further research is needed to determine if the barriers and facilitators that we identified are similar to those in other parts of the country. Additionally, there was lack of standardization across IPAs when evaluating the efficacy of their internal best practices. This made comparing similar best practices across various IPAs challenging. Efforts that allow for more direct comparisons will help to further elucidate the barriers and facilitators of best practices nationally.

Recommendations from this study include assessing the organization’s revenue model and contractual obligations to health plans when developing care management strategies, concentrating best-practice dissemination among high-volume or exclusive providers, and using risk stratification including social factors to target members most likely to benefit from the interventions. Ultimately, it is critical to align appropriate funding models to achieve optimal outcomes by supporting the adoption of robust best practices by multiple stakeholders, including IPAs, health plans, and government payers. More research is needed to further explore facilitators and barriers to best-practice implementation among IPAs nationally.

 Regal Medical Group (JND), Laguna Niguel, CA; Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill (MW, PS), Chapel Hill, NC.

 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 (JND, MW, PS); acquisition of data (JND); analysis and interpretation of data (JND, MW, PS); drafting of the manuscript (JND); critical revision of the manuscript for important intellectual content (JND, MW, PS); statistical analysis (JND); provision of patients or study materials (JND); and supervision (PS).

 Jennifer N. Dunphy, DrPH, MBA, MPH, Regal Medical Group, 8510 Balboa Blvd, Ste 150, Northridge, CA 91325. Email: Jennifern.Dunphy@gmail.com.

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Articles

The objective of this research was to identify strategies that significantly lower unnecessary inpatient utilization among Medicare beneficiaries with chronic disease.

Strategies for Implementing Best Practices in Independent Physician Associations

To estimate the effects of selecting a narrow provider network on outpatient utilization and outpatient out-of-pocket (OOP) expenditures among individuals who chose to enroll in a narrow network plan in 2014. 

 Claims data from a large insurer in the southeastern United States. 

The sample consisted of individuals continuously enrolled for 2 years (2013-2014) who had Affordable Care Act—compliant plans in 2014. We compared unadjusted results and then used difference-in-differences (DID) models to determine the effect of narrow networks on the number of outpatient visits and outpatient OOP expenditures. 

Our DID model found no significant change in visits or outpatient OOP expenditures for individuals who selected a narrow network plan in 2014. However, unadjusted outpatient OOP expenditures and premiums were lower for individuals who selected narrow network plans. 

Our findings suggest that individuals who selected narrow network plans in 2014 were able to keep costs low without changing their overall number of outpatient visits. Narrow network plans can reduce costs to beneficiaries without affecting the volume of outpatient visits, if appropriate incentives to visit participating providers are followed.

We analyzed claims data from a large insurer to estimate the effect of narrow network health plan selection on outpatient visits and the cost of outpatient visits to the beneficiary. 

		Selection of a narrow network plan did not cause individuals to decrease the number of outpatient visits their first year in a narrow network plan. 

		Selection of a narrow network plan did not have a statistically significant effect on outpatient out-of-pocket costs. 

		Narrow network plans were associated with lower premiums, which would lower total costs for individuals who did not frequently access out-of-network care.

The Affordable Care Act (ACA) of 2010 established health insurance marketplaces in which individuals could access and choose among health plans from various insurers. To lower premiums, insurers created narrow network products for which they negotiated lower reimbursement rates with groups of providers. In return for lower reimbursement rates, providers participating in narrow networks anticipated increased volume through concentrated visits from narrow network plan beneficiaries.

 Proponents of narrow network plans claim they can facilitate better care coordination by ensuring that patients receive more care from providers within a certain community or hospital group, making it more likely that providers have working relationships and/or compatible electronic health records systems.

 In addition to better care coordination, narrow network beneficiaries should benefit from lower premiums and lower-cost services when using in-network providers. 

In practice, taking advantage of lower out-of-pocket (OOP) expenditures can be challenging. First, not all individuals in a narrow network were aware of the plan restrictions on providers and facilities. A 2014 survey found that 26% of individuals enrolled in a qualified health plan (QHP) were unaware of their plan’s network size.

 Second, even if beneficiaries were aware of the restricted network, they still may have had problems identifying participating providers and facilities. Finally, some individuals may not have fully understood how narrow network plans work.

 One source of confusion was that out-of-network care was subject to its own deductible, coinsurance, and co-payments, which can have nontrivial consequences on OOP expenditures.

Despite these challenges, in 2014 (the first year of marketplace operation), narrow network plans were popular offerings, available to 92% of the population and accounting for 48% of all ACA plans offered in the marketplace.

 The recent resurgence of narrow network plan offerings was mainly driven by consumer interest in lower premiums. A 2014 survey of individuals enrolled in ACA-compliant QHPs found that individuals in narrow network plans were more likely to report that they purposely chose one of the lowest-priced products in the marketplace.

 Although individuals selected narrow network plans based on premium price, the effect on overall OOP costs to the beneficiary has not been studied.

In this paper, we examined the extent to which selection of a narrow network plan affected the number of outpatient visits and outpatient OOP costs to the beneficiary for individuals who chose a narrow network plan in 2014. We used a difference-in-differences (DID) model to control for differences in outcomes during the year prior to the introduction of narrow networks and other exogenous occurrences that could have affected the outcomes. We hypothesized that selection of a narrow network plan would not affect the number of outpatient visits, but would decrease outpatient OOP expenditures. 

Our data came from a large insurer in the southeastern United States. In 2014, this insurer was 1 of only 2 companies offering plans in the state marketplace; in 2013, it had 86% of the non—group market.

 Typically, insurers do not release contractually negotiated reimbursement rates, but this dataset included the price of all services, including the cost of care to the beneficiary.

The analytical sample included individuals who: 1) were aged 18 to 64 years on January 1, 2014 (working-age adults who do not transition to Medicare), 2) were enrolled in a nongroup QHP in 2014 and residing in a county where narrow network plans were offered in the marketplace by this insurer, 3) had 12 months enrollment in both 2013 and 2014 with the same insurer, and 4) were enrolled in a 2014 narrow or broad network preferred provider organization (PPO) QHP. 

In 2013, all individuals in this sample were in broad network health plans; no QHPs or narrow network plans were offered in the nongroup market. In 2014, all individuals switched plans (no individuals were enrolled in a 2014 grandfathered plan); the entire sample was offered the choice between a broad network QHP and a narrow network QHP, and everyone in the sample made an enrollment decision.

In this sample, broad network plans, still defined as PPOs, had contracts with almost all providers in an area, while narrow network PPOs contracted with a smaller group of providers in each market. Narrow network plans were identified as such using internal data from this commercial insurer.

We had 2 outcome variables: outpatient visits and outpatient OOP expenditures related to outpatient visits. Visits were identified using 2013 and 2014 claims; we included both professional and facility claims, but limited them to those where the provider designated for reimbursement was a physician or mid-level provider. We excluded inpatient and emergency department (ED) visits so that individuals had more control over location and provider selection. 

Outpatient OOP expenditures were defined as all outpatient-related healthcare expenditures for which the beneficiary was responsible in each year of the study. We chose this outcome because individuals are more likely to make utilization decisions based on the portion of the bill for which they are responsible, not the total cost of a service (the amount paid by both member and insurer). Costs were logged and capped at the 99th percentile to minimize the influence of outliers.

 We also looked at unadjusted changes in annual premiums. 

In 2013, this insurer did not offer narrow network plans in the nongroup market. In 2014, narrow network QHPs were introduced as an option in the marketplace, along with broad network QHPs. We take advantage of this quasi-experimental situation by using DID models to examine the effects of selection of a narrow network plan on changes in: 1) the number of outpatient visits and 2) outpatient OOP expenditures for individuals who chose a narrow network QHP in 2014. A DID model controls for observable differences between treatment and control groups and for any extemporaneous changes to the outcome variable over the time period. 

We used random-effect generalized linear equations to model each outcome; outpatient visits were represented as a count variable and modeled using a Poisson distribution with a log link, whereas OOP costs were represented as a continuous, nonnegative variable and modeled using a normal distribution with an identity link. 

Outcome = α + δ(Narrow Network) + ρPost + γ(Narrow Network × Post) + βX + ε

Our key independent variable was selection of a 2014 narrow network PPO QHP. Narrow network affiliation in 2014 defined the treatment group in this study; the narrow network label applied to the same group of individuals in both periods even though narrow network plans were only available in 2014. Those in the sample who did not select a 2014 narrow network plan were referred to as broad network beneficiaries (in both 2013 and 2014). 

In the formula on the previous page, α was the intercept representing the pre-period (2013) for broad network beneficiaries; δ was the effect of the narrow network in the pre-period (2013) (this was also the preperiod difference between the narrow and broad network groups); ρ was the effect of the post period (2014) on the outcome for the broad network beneficiaries, also representing any changes in the outcome variable over the time period that were not a result of the introduction of the narrow networks; and γ, the effect of the narrow networks in the post period, was the main effect of the model. This is the effect of the narrow network plan on the outcomes, controlling for extemporaneous changes and observable characteristics. X represents other variables in the model.

Health status, represented by the risk score, is included as a time-varying control. Risk scores have been used in previous studies to represent health status.

 We used an episode-based health risk score generated by an Ingenix Symmetry algorithm accounting for age, sex, and healthcare claims from the previous 12 months.

 Our model utilized the prospective risk score, which combines demographics and claims data from the past 12 months to calculate a risk score that predicts the risk of an individual being high-cost in the subsequent year.

 Two risk scores were generated for each individual in the sample—one to predict risk of high health expenditures in 2013 and the other for risk in 2014. 

We explored both fixed-effects and random-effects models by comparing the coefficients on our main variables; we found no difference in the estimates and, since fixed effects are less efficient and random effects were unbiased, we used random effects in our DID models. A random-effects model allows for the inclusion of time-invariant controls; in these models, we controlled for age, gender, and presence of chronic conditions (asthma/chronic obstructive pulmonary disease, heart failure, hypertension, or diabetes). We also controlled for ACA-specific factors, such as an indicator for each rating region in the state and any applied premium tax credits (APTCs) received by the beneficiary. Standard errors in the DID models were obtained by bootstrapping more than 1000 replications.

 All analyses were done in SAS version 9 (SAS Institute; Cary, North Carolina).

The analytical sample contained 12,314 individuals, 37.5% of whom were in a narrow network health plan in 2014 (

). There was no statistically significant difference between the mean age of the narrow and broad network groups (mean age, 45 years), but females were less likely to choose a narrow network plan (55.6% vs 59.1%; 

 <.0001). Individuals in narrow network plans were at a lower risk of having high healthcare expenditures (

 <.0001) and fewer had chronic conditions (29.1% vs 31.2%; 

, we present the unadjusted means and 95% confidence intervals for our 2 main outcome variables, as well as the percentage change across years and between groups. Individuals in the narrow and broad networks saw increases in the mean number of outpatient visits between 2013 and 2014 (7.6% and 9.7%, respectively). In both years, the mean number of visits for the narrow network group was lower than for the broad network group (25% to 27% lower).

The unadjusted results show that individuals in narrow network plans had lower mean outpatient OOP expenditures in both the pre-period (—20.5%) and the post period (–22.6%). Although OOP expenditures decreased between 2013 and 2014 for both groups, mean unadjusted expenditures for individuals in narrow network plans decreased more than for individuals in broad network plans (–27.3% vs –23.9%). 

Although premiums increased between 2013 and 2014 for both individuals who selected narrow network plans and those who did not, 2014 premiums were 10% lower for individuals in a narrow network plan than for individuals in a broad network plan. These numbers reflect the 2014 premiums before APTCs were applied. Individuals on narrow network plans were more likely to have an APTC (58.3% vs 41.2%; 

<.0001); on average, their APTC was 27.5% higher ($213 vs $167;

<.0001) compared with individuals in broad network plans.

 shows the results of our DID model. After controlling for preperiod differences and adjusting for risk and other variables, we found the effect of narrow network plan selection on outpatient visits to be statistically insignificant. After controlling for observable characteristics and any extemporaneous changes, we also found no significant change in outpatient OOP expenditures for individuals who self-selected narrow network plans.

Narrow network plans were popular choices for consumers in the first year of the health insurance marketplaces. Although there was heterogeneity in the creation and execution of narrow network plans, the underlying concept was a product with lower premiums, which incentivized beneficiaries to visit a specified set of providers or face a higher cost for services. To date, few studies have been conducted on the effects of narrow network plans on costs and utilization in the nongroup market.

 The results from our study support the idea that individuals who select narrow network plans can save money without reducing the number of outpatient visits. 

Although the DID model did not find a significant reduction in outpatient expenditures, individuals in narrow network plans did not incur higher adjusted expenditures as a result of the narrow network, indicating that they were not obtaining out-of-network care (purposively or inadvertently). Individuals in narrow network plans who visited in-network providers incurred lower OOP costs for their visits and, by plan design, had lower monthly premiums, which could decrease overall costs throughout the year.

Early reports suggested that individuals chose narrow network plans because of the lower plan premiums, and our results provide support for this finding.

 In this sample, the individuals who selected narrow network plans had lower unadjusted premiums the year before narrow network plans were offered; the 2013 unadjusted average monthly premium for the narrow network group was 27.3% lower than the average for the broad network group. The 2013 premiums were medically underwritten, which means that lower premiums may have been a function of a healthier population; however, a higher percentage of individuals in narrow networks had deductibles over $5000 in 2013 (34.3% vs 26.6%), indicating that individuals who selected narrow network plans had a history of choosing health plans with lower premiums but more cost-sharing restrictions.

Although individuals in the narrow network group had fewer unadjusted outpatient visits in both 2013 and 2014, joining a narrow network plan did not lead to any statistically significant changes in the mean number of outpatient visits, providing support that narrow networks did not decrease overall utilization. This is consistent with the objective of narrow network plans: to steer individuals toward in-network providers, rather than reduce utilization.

The authors examined the effect of narrow network plan selection on beneficiaries’ outpatient visits and outpatient out-of-pocket expenditures in the 2014 nongroup health insurance market.

Pam Silberman, JD, DrPh

Articles

Strategies for Implementing Best Practices in Independent Physician Associations

The Effect of Narrow Network Plans on Out-of-Pocket Cost