One Size Will Not Fit All: Factors That Drive Strategy in Accountable Care Organizations

The American Journal of Accountable Care®March 2020
Volume 8
Issue 1

Differences in patients’ clinical and social complexity and accountable care organization (ACO) network configuration highlight why specific strategies may have variable effectiveness in different types of ACOs.

The American Journal of Accountable Care. 2020;8(1):3-5.

Over the past decade, accountable care organizations (ACOs) have expanded across payers, including Medicare, Medicaid, and commercial insurers. Participation among provider organizations has also increased over time, to the point where companies have emerged with the sole purpose of helping health systems succeed in prominent ACOs, such as the Medicare Shared Savings Program.1

Although ACOs all possess a set of core features that define the payment model, differences across payer types and patient populations mean that no single strategy will be effective for all types of ACOs. Provider organizations would be best served considering and participating in ACOs after they have considered these differences and their implications for care delivery initiatives that can support ACO success. In this article, we leverage our perspective as physician leaders in an integrated delivery system that participates in multiple ACOs and discuss 3 factors—clinical complexity of ACO populations, social complexity of ACO populations, and ACO network configuration—to advance the discourse about payment model strategy and performance.

Clinical Complexity

Certain ACO strategies are more or less likely to be successful based on the clinical complexity of the covered population. For instance, approximately two-thirds of Medicare beneficiaries have at least 2 chronic conditions,2 whereas the majority of beneficiaries in many commercial patient populations have no chronic medical diagnoses. This difference poses significant implications for how an ACO participant organization might approach 2 crucial types of activities: assessing patients’ clinical risk (ie, risk capture) and using risk information to implement care management services (ie, risk-stratified care management).

For Medicare beneficiaries, many high-cost conditions as defined by CMS’ Hierarchical Condition Categories (HCC) model are chronic and will remain stable year over year. As a result, providers can implement risk-capture activities within Medicare ACOs via approaches such as clinical decision support within the electronic health record to populate past diagnoses and prompt clinicians to affirm or update them.

In contrast, the lower chronic disease burden among beneficiaries in most commercial insurance plans undercuts the potential benefit of such approaches in commercial ACOs. In particular, the high-cost conditions for these patients, as defined by the HHS HCC model, will be acute (eg, trauma) and therefore less likely to be stable over time (ie, less likely to be relevant in even the next calendar year). Therefore, using clinical decision support prompts based on historical diagnoses can create more “noise” than “signal,” thus causing workflow inefficiencies for clinicians.

This logic also carries implications for risk-stratified care management within ACOs and, in particular, activities directed at high-cost patients. Differences in the clinical complexity of different ACO populations exacerbate a well-established issue with implementing risk-stratified care management: that there is inherent instability in high-cost populations, with individuals cycling in and out of these populations year to year.3

Given this dynamic, care management interventions will adopt a different form within Medicare populations (eg, heavy focus on self-management of multiple chronic conditions) versus commercial populations (eg, preventive services to avoid catastrophic events such as motor vehicle accidents). Without these distinctions, provider organizations in ACOs may not achieve anticipated benefits from care management programs because of variation across different ACO populations.

Social Complexity

Social factors play an outsized role in determining many health outcomes, serving as a larger driver than even healthcare factors.4 In turn, the extent of an ACO population’s social complexity carries major implications for provider organizations’ payment model strategy and performance.

For instance, because of their predictable coverage over an extended period, Medicare fee-for-service populations tend to be relatively stable in composition. In turn, condition-based care management can be used within Medicare ACOs to deliver guideline-concordant services that not only improve outcomes but also address utilization and costs as core ACO metrics. In contrast, this approach may be less effective in populations who are less stable over time and/or marked by higher social complexity, such as those in Medicaid. Many Medicaid beneficiaries have high costs as a result of social determinants (eg, housing and/or food insecurity) rather than or in addition to clinical determinants (eg, suboptimal medication titration). Delivering clinical interventions aimed at disease management fails to address nonhealthcare root causes of utilization and costs.

The extent of a population’s social complexity may determine how patients access care and, in turn, how organizations implement interventions to perform in ACOs. For example, comprehensive primary care—which focuses on activities such as chronic and acute care management, care coordination, and care continuity—has been cited as a critical strategy to cost containment within ACOs.5 However, the value of this approach is based on the assumption that patients’ primary healthcare access point is via a comprehensive and often brick-and-mortar primary care infrastructure. Although this may be true for certain groups, such as older Medicare populations, it may not be for others.

For instance, it may not be the norm for employed, less socially complex patients in commercial populations to access care in this way, raising the potential for organizations in commercial ACOs to better meet patient needs via technological approaches such as telemedicine, which can expand access while circumventing some costs of physical locations altogether. As another example, populations with high social complexity frequently access nonemergent care via the emergency department. Consequently, ACOs may be better off investing in fast-track triage and colocated urgent care and primary care facilities rather than comprehensive primary care or telemedicine infrastructure.

Network Configuration

Just as ACO strategy should be dictated in part by differences in patients’ clinical and social complexity, it will also likely differ as a result of which providers are delivering care to patients (ie, the ACO network configuration). One size does not fit all because of differences in the networks that are needed in different ACOs.

For instance, network configuration can influence provider organizations’ approaches to managing clinical referrals among different types of clinicians—a core ACO strategy for maintaining a stable, engaged patient population and addressing their utilization, quality, and costs.6 For Medicare patients with multiple chronic conditions, ACOs may require strong networks of primary and subspecialty care within which patients can be referred and comanaged.

In contrast, for commercial populations with less need for tight primary-subspecialty coordination, it may be more strategic for ACO networks to ensure inclusion of providers who are delivering occupation-aligned services (eg, physical therapy providers for labor workers, on-site services that can be accessed at the site of work). Given the extent to which some Medicaid patients receive care from emergency departments, organizations interested in Medicaid ACOs would be well served by carefully considering whether and how to include freestanding and hospital-related sites within their networks.

Differences in network configuration can also affect organizational care standardization activities within ACOs. As methods for reducing unwarranted care variation, clinical pathways can be a promising strategy to implement within ACOs.7 However, whereas ACOs with networks emphasizing primary care providers may elect to develop and implement pathways around prevalent chronic conditions encountered in primary care (eg, diabetes, hypertension), ACOs with networks encompassing quaternary care sites may opt to instead prioritize subspecialty pathways.


Although several core features define ACOs as a payment model, several differences (eg, clinical and social complexity of patient populations, ACO network configuration) highlight why specific strategies may have variable effectiveness in different types of ACOs. Provider organizations would be well served by accounting for these issues as they consider ACO participation.

Author Affiliations: Department of Medicine, University of Washington School of Medicine (LMM, JML), Seattle, WA; Value and System Science Lab (LMM, JML), Seattle, WA; Leonard Davis Institute of Health Economics, University of Pennsylvania (JML), Philadelphia, PA.

Source of Funding: None.

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 (LMM, JML); drafting of the manuscript (LMM); critical revision of the manuscript for important intellectual content (LMM, JML); administrative, technical, or logistic support (LMM); and supervision (JML).

Send Correspondence to: Leah M. Marcotte, MD, Department of Medicine, University of Washington School of Medicine, 1107 NE 45th St, Ste 355, Seattle, WA 98105. Email:


1. Powers BW, Mostashari F, Maxson E, Lynch K, Navathe AS. Engaging small independent practices in value-based payment: building Aledade’s Medicare ACOs. Healthc (Amst). 2018;6(1):79-87. doi: 10.1016/j.hjdsi.2017.06.003.

2. Multiple chronic conditions. CMS website. Updated April 5, 2019. Accessed November 24, 2019.

3. Marcotte LM, Reddy A, Liao J. Addressing avoidable healthcare costs: time to cool off on hotspotting in primary care? J Gen Intern Med. 2019;34(11):2634-2636. doi: 10.1007/s11606-019-05285-z.

4. Schroeder SA. We can do better — improving the health of the American people. N Engl J Med. 2007;357(12):1221-1228. doi: 10.1056/NEJMsa073350.

5. McClellan M, McKethan AN, Lewis JL, Roski J, Fisher ES. A national strategy to put accountable care into practice. Health Aff (Millwood). 2010;29(5):982-990. doi: 10.1377/hlthaff.2010.0194.

6. Song Z, Sequist TD, Barnett ML. Patient referrals: a linchpin for increasing the value of care. JAMA. 2014;312(6):597-598. doi: 10.1001/jama.2014.7878.

7. Marcotte LM, Chen A, Hwang CS, Liao JM. Primary care—oriented pathways to support population-based payment models. J Clin Pathw. 2019;5(7):39-42. doi: 10.25270/jcp.2019.09.00092.

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