Accountable care organizations (ACOs)—provider networks that form voluntarily to coordinate care and accept collective responsibility for the quality and cost of care (1)—provide a framework within which providers and payers can collaborate to improve patients’ health status and care experience while reducing costs. While ACOs have been growing fast for Medicare beneficiaries and commercially insured populations, state Medicaid programs have also been increasingly adopting comparable approaches to payment and care delivery (2–4). Currently, 14 states are implementing ACO models for their Medicaid enrollees (5). Although Medicaid ACOs differ in organization and structure across states (4), they share common goals of keeping patients healthy, improving quality, controlling costs by aligning provider incentives to value instead of volume, coordinating care, and reducing inappropriate utilization (6).

Oregon's Medicaid program, Oregon Health Plan (OHP), has pioneered innovative ways to deliver health care services to low-income and disabled individuals. In August 2012, OHP started to enroll its Medicaid beneficiaries in Coordinated Care Organizations (CCOs), Oregon's version of accountable care (7, 8). Most CCOs became operational in January 2013 (7, 8), and by 2016, over 90% of OHP beneficiaries were enrolled in CCOs (9).

While CCOs are consistent with the typical ACO delivery concept, such as shared savings and performance standards, they are also unique in that CCOs are geographically defined networks of health care providers, required to integrate medical and behavioral health care, assigned Medicaid beneficiaries automatically by residential ZIP codes, and receive risk-adjusted global payments (7). CCOs’ self-reported performance data show beneficial impacts on most performance domains. For example, from 2011 to 2018, avoidable ED visits dropped significantly from 14.2 to 6.3 visits per 1,000 member months, and all-cause hospital readmissions declined slightly from 12.9% to 11.1% of all discharges (10). McConnell et al. (11) find a statistically significant increase in inpatient days during the second year of CCO implementation. Most prior studies report positive overall impacts of the CCO model, including declines in ED visits among CCO enrollees over the two years of early implementation (11, 12) and improvement in access to prenatal care, neonatal outcomes, and reproductive health outcomes among women of reproductive age (7, 13–17).

This study contributes to the literature by examining the heterogeneous impacts of the CCO model on preventable ED visits and hospital admissions by rurality/urbanicity of residence among adult Oregon Medicaid beneficiaries. Rural residents deserve particular focus due to substantial disparities in health and access to health care compared to their urban counterparts (18, 19). These disparities stem from structural barriers, including provider shortages, longer travel distances, and limited availability of primary and urgent care services (19). As a result, rural residents often have fewer alternatives to EDs for addressing urgent health needs, which may lead to a greater reliance on EDs even for conditions that could be prevented or treated in outpatient settings (20). The scarcity of critical resources, such as health information technology, further limits the capacity of rural health systems to support integrated care models, presenting seriously challenges rural ACOs (21–24). Implementing innovative delivery models like CCOs can be resource-intensive. However, rural areas often face financial constraints and lack the robust provider networks needed to fully support such system transformation (25). Even so, some ACOs in rural areas large enough to have hospitals may opt to repurpose and absorb resources to transform existing hospitals into community health hubs, which might produce positive patient outcomes (21). This situation may disproportionately benefit patients in large rural areas more than those in small/isolated rural areas. Therefore, we hypothesize that the beneficial impact of the CCO model on preventable ED visits and hospital admissions diminishes with increased rurality of residence. CCOs are incentivized to avoid preventable ED utilization through pre-established performance standards. However, preventable hospitalizations were not included as part of the performance standards. Therefore, we hypothesize that the effect of CCOs on preventable hospital admissions, if any, is smaller than the effect on preventable ED visits.

Table 1 provides pretreatment means and standard deviation of the variables, unweighted and inverse-probability weighted. Approximately 79% of the unweighted sample were CCO enrollees over the study period, and 58% of the observations were for the post-CCO period. The average age was 40.8, and females comprised 63.3% of the sample. The majority of the sample was White (77.7%) followed by Black (4.55%), Asian (2.35%), American Indian/Alaskan Native (2.07%), and Native Hawaiian/Pacific Islander (0.24%). Eleven percent of the sample was Hispanic. The majority (81.7%) of the sample lived in urban areas. Large and small rural residents comprised 14.4% and 3.9%, respectively. CCO enrollees were slightly younger and more likely to be female than non-CCO enrollees. CCO enrollees were more likely to be White, Black, or Asian but less likely to be American Indian and Alaskan Native, Native Hawaiian and Pacific Islander, or Hispanic. CCO enrollees were less likely to live in rural areas than non-CCO Medicaid beneficiaries, as expected. Notwithstanding, the baseline characteristics of CCO and non-CCO enrollees overall do not appear to be substantially different from each other, which is even more likely for inverse-probability weighted means.

For the entire sample, the average number of all-cause ED visits was 0.072 per person per month (or 72 visits per 1,000 persons per month); preventable ED visits averaged 0.062 per person per month; and about 1.6% of the entire observations had all-cause hospital admissions, while 0.8% had preventable hospital admissions. Overall, CCO enrollees were more likely than non-CCO enrollees to have ED and hospital admissions.

Table 2 presents coefficients from the fixed-effects DID models that investigated the overall effects of the CCO model. The coefficient on the interaction term of CCO-enrollee and post-CCO period indicators is of primary interest. It was negative and statistically significant for both any-cause and preventable ED visits, which might suggest a decrease in ED visits following the implementation of the CCO model. In comparison, although negative, the coefficients on the main interaction term for the hospital admission outcomes were statistically insignificant and small in magnitude. Both the number of ED visits and the probability of hospital admissions decreased as the degree of rurality increased: compared to urban residents, rural residents, especially in small/isolated rural areas, were less likely to visit EDs and be admitted to hospitals. The coefficients on the linear time trend and its interaction with the CCO enrollee indicator were jointly significant, which indicates group-specific time trends in the outcomes. Specifically, the time trend coefficient indicated an overall decline in ED visits for non-CCO enrollees, whereas CCO enrollees experienced an overall increase during the study period. For hospital admissions, there was no significant time trend among non-CCO enrollees, but CCO enrollees showed a significant upward trend over time.

Figure 1 depicts average marginal effects that measure the magnitude of the discovered relationships. We focus on main results and Supplementary Table S5 presents the full results. The CCO model overall was associated with decreases in the number of all-cause ED visits and preventable ED visits by 0.025 and 0.020 per person per month, respectively. The discovered relationships appear substantively significant as well in that among CCO enrollees, the 0.020 decrease in preventable (all-cause) ED visits represents ∼31% of the pre-CCO average of all-cause ED visits (0.082 per person per month), and the 0.020 decrease in preventable ED visits represents ∼29% of the pre-CCO average of preventable ED visits (0.069 per person per month).

The CCO model was significantly associated with decreases in all-cause and preventable ED visits for CCO enrollees throughout the rurality/urbanicity of residence location. Notwithstanding, the magnitude of the relationship decreased almost monotonically and statistically significantly as the level of rurality increased from urban to small/isolated rural areas. In particular, the CCO model, on average, was associated with significant declines in preventable ED visits by 0.018, 0.009, and 0.005 visits per person per month among urban, large rural, and small/isolated rural residents, respectively.

The marginal effect of the CCO model on hospitalization was always negative, but overall, it was not statistically discernable. The CCO model was significantly associated with 0.30 and 0.20 percentage-point declines in the probability of all-cause and preventable hospital admission per person per month, respectively, among those living in small/isolated rural areas. However, the statistical significance disappeared in robust checks, as shown below.

Several states have adopted ACO models as an integrated health care delivery approach for their Medicaid beneficiaries (3). This study examined the impacts of CCOs, an ACO model implemented in Oregon Medicaid, on preventable ED and hospital admissions with focus on rural-urban heterogeneity. Our findings indicate that the CCO model significantly reduced ED visits, largely through preventable ED visits over the first three years of implementation. Although not directly comparable to other estimates from the literature, our estimates are qualitatively consistent with an 11% decrease in the probability of all-cause ED visits in two years of CCO implementation reported in prior analysis (11). Our findings are also consistent with the broader literature evaluating Medicaid ACO performance, which documents more frequent primary care visits, reduced hospital admissions, and shorter inpatient stays associated with ACO models for Medicaid beneficiaries. However, their effectiveness appears to be relatively more limited than that of Medicare ACOs, likely due to variations in state Medicaid programs (35).

Importantly, our findings suggest that the beneficial effect of the CCO model on reductions in ED visits, both all-cause and preventable, dissipates gradually with increasing rurality. Smaller decreases in ED visits among rural CCO enrollees may reflect a limited numbers of providers in rural areas, offering fewer alternatives to EDs for urgent care needs. Moreover, the adoption and performance of CCOs hinge on both financial and non-financial resources. The ACO literature documents that while many infrastructures and configurations are essential for all ACOs to succeed, rural ACOs often face acute shortages of capital and providers (24, 36), making the upfront investments required to develop and operate these innovative models difficult to justify without short-term returns (21). Therefore, financial sustainability efforts such as investments under the CCO model can disproportionately challenge rural providers, potentially influencing the rural-urban differences observed.

Information technology (IT) infrastructure is another big challenge facing rural ACOs. The literature well documents the critical roles of IT infrastructure and health data in ACOs’ success (21–24). Robust electronic health records systems and data-sharing platforms can facilitate better care coordination and targeted management of high-need patients across the continuum of care. However, IT infrastructure remains uneven in rural areas (24), and smaller practices are less likely to implement advanced electronic health systems (37). Despite these challenges, an earlier study shows that some rural ACOs have successfully repurposed existing resources, such as converting a hospital into a community health hub (21), to extend their reach and absorb resources (such as patients, providers, and revenues) from a broader geographic area. Strong IT infrastructure in rural hospitals and clinics can facilitate care coordination and data sharing, which are critical to the success of the CCO model. Therefore, we suggest that CCOs re-think such a strategy that may incentivize rural health care providers to integrate and coordinate care more effectively and thereby benefit their service populations.

While the Oregon Medicaid CCO model was successful in reducing preventable ED visits, this did not translate into a measurable reduction in preventable hospital admissions during our study period. One potential explanation is that preventable hospital admissions were not included among the CCO performance measures, so CCOs lacked direct incentives to reduce preventable hospital admissions. Alternatively, hospital admissions, particularly planned admissions, may be less readily amenable to reduction than ED utilization in Medicaid populations.

At the first glance, the finding of no effect of CCOs on hospital admissions might seem to contradict Yoon et al. (17), which found a substantial reduction in unscheduled, preventable hospital additions among reproductive-age women enrolled in Oregon CCOs. However, these results are not necessarily irreconcilable once we consider differences in populations. Women of reproductive age experience high-volume, ambulatory care-sensitive conditions (e.g., obstetric complications) that are especially responsive to enhanced primary care and care coordination. In contrast, our analysis encompasses the entire Medicaid population—across all ages, comorbidity profiles, and utilization patterns—some of which (e.g., surgical admissions) are less preventable via primary care and care coordination. In a heterogeneous statewide population with lower average preventable-admission rates, the same system-level intervention may yield effects too small to detect statistically. Taken together, our findings and the CCO literature illuminate how both the design of accountable-care arrangements and the characteristics of the populations they serve shape their impact on hospital utilization among Medicaid beneficiaries. Of direct relevance to our analysis, examining rural-urban heterogeneity in the effect of CCOs on preventable hospital admissions represents an essential agenda for future research, as it may shed further light on the mechanisms through which CCOs incentivize providers.

Interpretation of these findings should be considered within the context of several limitations and directions for future research. The effect of the CCO model might spill over into the non-CCO Medicaid beneficiaries, for example, if physicians in a group practice see both CCO and non-CCO patients. Such an effect, if any, would lead our estimates to be attenuated, underestimating the actual effects of CCOs on ED use and hospital admissions. Our data, however, do not provide further information to explore these aspects of providers’ decision-making.

Some Medicaid enrollees were exempt from mandatory CCO enrollment but voluntarily enrolled in CCOs. Potential explanations include an individual's health belief in integrated care, influence by other family members, or personal preferences. This current study lacked data to explore reasons for voluntary CCO enrollment. Nevertheless, our sensitivity analyses, such as dropping dually eligible individuals and running inverse-probability weighted regression, all showed robust results. Future research may include qualitative study components.

Oregon chose not to impose prescriptive organizational strategies on CCOs. They have wide latitude in organizing provider networks, paying providers, and coordinating behavioral and physical health care. This flexibility, designed to allow for innovation and local collaboration, provides the framework for a natural experiment in evaluating different care delivery and quality improvement approaches. Future research could benefit from this unique opportunity to explore heterogeneity among an array of Medicaid ACO models in Oregon and elsewhere. These findings could inform health care delivery transformation for low-income populations in other states.

Our study was limited to utilization outcomes (ED and hospital admissions) and improvements in utilization should ideally translate into better patient outcomes (such as improved health status or reduced mortality) as well as lower costs. Future evaluations of the CCO model should examine direct patient outcomes and cost-effectiveness to fully assess the value of care.

Another noteworthy limitation is that the all-cause hospital admission outcome includes pregnancy-related hospitalizations. While preventable admissions—defined using AHRQ's PQIs—do not include maternity-related hospital stays, all-cause admissions do not make such distinctions. As a result, obstetric admissions (e.g., labor, delivery, or complications thereof) may have diluted the observed effect of the CCO model on all-cause hospitalizations, given that these events are typically less amenable to outpatient prevention strategies. Future research should consider constructing non-pregnancy-related hospitalization measures or conducting subgroup analyses excluding women of childbearing age to better isolate the effects of the accountable care intervention on avoidable hospital use.

We applied PQIs—which are originally designed for assessing population- or area-level performance of ambulatory care—to individual-level claims data. While this approach allows for identification of potentially preventable hospital admissions at the person-month level, PQIs were not intended for evaluating outcomes at the individual level. As such, their application in this context may introduce misclassification or measurement error, and the findings should be interpreted with caution. Future work should consider validation studies or alternative methods more specifically tailored to individual-level preventability.

Although empirically investigating the mechanisms underlying the attenuated CCO effect in rural areas is beyond the scope of our current analysis, future research could leverage administrative database such as ours to elucidate underpinning pathways. For example, linking established indices of community vulnerability (e.g., the CDC Social Vulnerability Index) to medical claims at the enrollee ZIP-code level would allow assessment of whether social determinants, such as poverty and travel distances or drive times to the nearest hospitals, mediate CCO performance. Such analysis will yield a more granular understanding of how community context and spatial barriers influence CCO effectiveness across the rural–urban continuum.

Since its inception, Oregon's Medicaid delivery system transformation through CCOs has emphasized the identification and management of high-cost patients, especially those with severe and chronic behavioral health problems, such as beneficiaries with severe mental illness and comorbid conditions. A further investigation of how CCOs affect frequent health care users will help illuminate mechanisms that incentivize providers’ practices in the networks. As such, the outcomes on rural-urban heterogeneity might differ for other subpopulations of CCO enrollees (e.g., age, race/ethnicity, family income, employment), so examining subpopulation-specific effects could be a valuable extension of this research.

Oregon initiated CCO 2.0 in January 2020, further emphasizing health equity and social determinants of health, such as education, housing, community planning, and transportation barriers, which is especially crucial due to Oregon's predominantly rural and frontier landscape (38). Future research should investigate whether CCO 2.0 better address needs of Medicaid beneficiaries, promoting rural-urban health and health care equity.

Our findings suggest that Oregon's CCO model achieved meaningful reductions in preventable and all-cause ED visits, though these benefits were less pronounced in rural areas. The diminished impact in rural settings likely reflects persistent structural barriers, such as limited provider capacity and weaker health IT infrastructure. We did not observe a measurable reduction in preventable hospital admissions during the study period; however, this does not necessarily indicate that CCOs failed to influence inpatient care. Rather, such effects may take longer to materialize, may operate through aspects of care not captured by our measures (e.g., care quality or length of stay), or may have been constrained by structural and clinical factors beyond the scope of early CCO incentives.

These findings underscore the importance of context-sensitive ACO design and implementation, particularly in rural environments. As states continue to expand Medicaid ACO models, future research should prioritize understanding rural-urban disparities and refining performance measures to ensure that accountable care arrangements equitably benefit all Medicaid populations.