Healthcare leaders often experience dissonance when engaging with value-based payment models. On one hand, the transition from payment for mere volume to incorporation of payment for standard quality processes and outcomes seems obviously right. On the other, the operational reality becomes increasingly complex and increasingly difficult to understand and execute. For example, medical groups can receive monthly performance reports, track dozens of measures and still be unable to estimate their final shared-savings result with confidence. That uncertainty is frustrating, but many of the features that make these models difficult to understand exist because the underlying problem is difficult to solve.

Accountable care organizations (ACOs), Medicaid managed care arrangements, and other value-based contracts rely on measurement, attribution, benchmarking, reconciliation, and financial distribution. These systems can be opaque, even to those responsible for administering them. Many healthcare leaders therefore question whether value-based care has become unnecessarily complex.

Much of this complexity comes from a design problem: how to compare organizations that serve different patients under different conditions without rewarding favorable selection or penalizing practices for caring for higher-need populations.¹

The question is which layers of complexity are necessary, which can be shifted away from providers, and which are no longer justified.

Why payment models become complex

Value-based payment systems must estimate the expected cost of caring for a defined population while incentivizing improved outcomes. That task quickly becomes technically demanding, because patient populations vary by age, comorbidities, social drivers of health, geography, utilization history, and access to resources. These differences affect expected spending and outcomes.

Without adjustment mechanisms, organizations serving higher-complexity populations can appear inefficient, while those serving healthier populations can appear high performing regardless of care quality. Risk adjustment is intended to reduce that distortion by estimating expected spending from documented health and demographic factors and creating a fairer baseline for comparison.²

Even with these adjustments, substantial variation in ACO spending remains. Standard risk adjustment does not capture every relevant difference across populations or markets.³ A model that is easy to explain may still be too imprecise to compare organizations fairly.

How the Medicare ACO benchmark is built

Within the Medicare Shared Savings Program (MSSP), financial performance is assessed against a benchmark based on historical Medicare expenditures for assigned beneficiaries, adjusted for patient risk, and updated with national and regional expenditure trends.⁴

Each component is intended to address a different source of distortion. Historical spending anchors the benchmark in an organization’s cost experience. Risk adjustment attempts to account for differences in patient populations. Trend factors update the benchmark as expenditures change over time.

Removing any one of those elements changes who bears the risk of error. A purely historical benchmark may reward organizations whose prior costs were already high, while a purely regional benchmark may disadvantage providers serving atypical populations or organizations still moving toward more efficient care.

These design choices do not guarantee fairness, but they show why simplification is not neutral. They also create operational uncertainty. ACOs receive data during the year, but CMS adjudicates final shared savings or losses in the fall after reach performance year.⁴ Leaders must forecast with incomplete information and avoid treating provisional results as final.

The tradeoff within ACO Primary Care Flex

The voluntary ACO Primary Care Flex model began Jan. 1, 2025, and runs through 2029. It tests prospective primary care payments within the ACO structure and shifts much of primary care payment from fee-for-service reimbursement toward a more predictable monthly payment.⁵ The model is intended to address a core limitation of prior models: Primary care organizations have been expected to manage total cost of care without consistent upfront investment.

The model simplifies one part of the operating environment (predictable cash flow) but adds layers of controls elsewhere in spending requirements and reconciliation expectations tied to those prospective funds. Participating ACOs still submit claims, Medicare payment systems zero out payment for covered primary care services, and ACOs distribute those prospective funds to participating providers. CMS also applies policies intended to ensure that the funds support primary care.

ACO PC Flex therefore does not eliminate complexity. It moves it, so that cash flow becomes more predictable, while governance and payment operations assume more responsibility.

Medicaid managed care adds a different layer

Medicaid value-based arrangements vary substantially by state, managed care organization, and provider contract. Some states prescribe specific payment models; others establish targets and allow managed care organizations more discretion in how to meet them, such as shared savings pools, performance-based incentives, risk adjustments, withholds, and comparative ranking across provider groups.⁶

Therefore, depending on the program, payments may include incentives, withholds, risk-sharing mechanisms, state-directed payments, and provider-level value-based requirements.⁷ These tools allow states and health plans to influence each domain independently. Competitive distribution mechanisms are intended to encourage continuous performance improvement, while layered incentive structures provide payers with multiple intervention points through which they can shape provider behavior and program outcomes.

However, these same structures produce operational consequences. Each component introduces its own measurement requirements, reporting expectations, and process workflows. A value-based arrangement functions less like a discrete contract than an ongoing operational program that requires significant staffing, management, and data processing.

Success depends not only on model design but also whether the organization can execute its recurring requirements consistently.

Why complexity persists: adaptive design and system response

Payment models also evolve in response to participant behavior. Risk-score caps, benchmark refinements, and other guardrails may be added to address unintended consequences such as coding intensity or selective participation.⁸

In our participation in ACO arrangements, we have seen this dynamic repeatedly as benchmark methodologies, quality measure specifications, and payment rules evolve from year to year. The ACO PC Flex model, for example, includes additional payment guardrails for FQHCs. Those provisions add complexity, but they exist because policymakers determined that earlier designs created incentives and risks that needed to be addressed.

The resulting process is cyclical. Policymakers introduce an initial model design, participants respond strategically to the incentives embedded within that design, and regulators subsequently modify the model to address behaviors that emerge. Each adjustment is intended to correct an observed distortion, yet each correction typically introduces additional structural complexity. Over time, payment models accumulate layers that reflect the history of problems they were created to solve.

Some of those controls still correct active distortions; others may remain after the original problem has changed. Simplification should begin by identifying what each layer was designed to prevent and testing whether it still does so at a reasonable cost.

The tradeoff and its limits: usability, precision, and structural complexity

The central tradeoff is between usability and precision.

Simpler models are generally easier to understand and administer. They can reduce administrative burden and improve transparency, but they also introduce more statistical noise, misattribute performance, and/or disadvantage organizations serving particular populations. More complex models attempt to improve the fairness and accuracy of comparisons, but they can raise administrative costs, reduce transparency, and create barriers to participation.

Not all healthcare complexity arises from the same source or serves the same purpose. Complexity used to improve fairness within a payment model differs from complexity created by fragmented financing and inconsistent payer requirements.

Medical groups may face different quality measures, data formats, attribution rules, documentation requirements, and payment methods across Medicare, Medicaid, and commercial contracts. CMS identifies multi-payer alignment as one way to reduce that administration burden through more consistent measures, payment approaches, data sharing, and documentation.⁹

That is a stronger target for simplification than risk adjustment itself. Standardizing payer-facing requirements can remove duplicate work without sacrificing the precision needed inside an individual value-based payment model.

Maintaining this distinction is important. The complexity within value-based models reflects, at least in part, an attempt to reconcile differences across patient populations and care delivery realities. Elsewhere in the system, similar complexity may arise from entirely different incentives and constraints and may warrant different solutions.

The goal of value-based care is to improve outcomes relative to cost, not to minimize complexity. Simplicity is desirable only to the extent that it does not undermine that ultimate and worthy objective.⁶

What the evidence shows

Evidence does not show that all value-based models work equally well, but ACO participation has been associated with lower Medicare spending. A 2025 difference-in-difference study of more than 8 million patients found mean differential reductions of $142 per patient over three years and $294 over six years. Physician group and small ACOs generated larger reductions.¹⁰ At the same time, provider experience within these models is frequently characterized by administrative burden, uncertainty, and skepticism regarding the fairness of underlying assumptions.⁶

The models can work without feeling like they work. That gap between technical performance and operational experience is itself a finding, and it is the one leaders live with.

Reframing the question: is the complexity justified?

For the past decade, my health center executive team has wrestled with a practical question: At what point does another requirement stop improving fairness or quality and begin to impose disproportionate operational cost? That cost is not merely financial. At my health center, it has included dedicated population health workflows, recurring quality reporting activities, risk-adjustment efforts, and increasing demands on staff across clinical, operational, financial, and compliance functions.

Some complexity is necessary, some complexity is corrective, and some complexity may be residual or unnecessary. Leaders need a test they can apply before assigning a contract and throughout implementation.

A layer of complexity is more defensible when it:

• corrects an identifiable distortion;
• materially improves fairness, accuracy, or accountability;
• can be explained to participating organizations;
• produces information leaders can act on during the performance period;
• costs less to administer than the value it adds; and
• cannot be handled more efficiently by the payer or model administrator.

These tests separate necessary technical sophistication from burden that has simply been transferred to participating organizations. They also focus attention on who is best positioned to perform the work.

For each proposed measure, adjustment, or reconciliation rule, leaders should ask what error it prevents, which team owns it, what data will arrive during the performance year, how final payment can be estimated, and how much staff time it consumes. A requirement without clear answers deserves scrutiny.

Conclusion

Healthcare leaders are navigating systems and arrangements that reflect the full weight of competing value-driven policy goals, operational realities, and financial incentives that are complex because the problems they are attempting to solve are complex. Some of that complexity is necessary. Some of it is corrective. Some of it may be excess. And some of it, importantly, arises not from the demands of care delivery or population health, but from the structure of the system in which that care is financed.

Leaders should demand that every requirement correct a defined problem, remain understandable to participants, and produce enough operational or clinical value to justify its cost.

Complexity that passes those tests may be necessary. Complexity that does not should not survive merely because it has become familiar.

Notes:

1. Government Accountability Office (GAO). Highlights of a forum: Reducing spending and enhancing value in the U.S. health care system. Washington (DC): GAO; 2025 Jun. https://www.gao.gov/products/gao-25-107465
2. Centers for Medicare & Medicaid Services (CMS). Risk adjustment. Baltimore (MD): CMS; 2023 Aug 14. https://www.cms.gov/priorities/innovation/key-concepts/risk-adjustment
3. Rose S, Zaslavsky AM, McWilliams JM. “Variation in accountable care organization spending and sensitivity to risk adjustment: Implications for benchmarking.” Health Aff (Millwood). 2016;35(3):440–448. https://pmc.ncbi.nlm.nih.gov/articles/PMC5436972/
4. Centers for Medicare & Medicaid Services (CMS). Medicare Shared Savings Program: shared savings and losses and assignment methodology specifications. Baltimore (MD): CMS; 2023 Jan. https://www.cms.gov/files/document/medicare-shared-savings-program-shared-savings-and-losses-and-assignment-methodology-specifications.pdf-2
5. Centers for Medicare & Medicaid Services (CMS). ACO Primary Care Flex Model. Baltimore (MD): CMS; 2026. https://www.cms.gov/priorities/innovation/innovation-models/aco-primary-care-flex-model
6. Medicaid and CHIP Payment and Access Commission (MACPAC). Final report on state strategies to promote value-based payment through Medicaid managed care. Washington (DC): MACPAC; 2020 Mar. https://www.macpac.gov/publication/final-report-on-state-strategies-to-promote-value-based-payment-through-medicaid-managed-care 
7. Medicaid and CHIP Payment and Access Commission (MACPAC). Medicaid managed care capitation rate setting. Washington (DC): MACPAC; 2022 Mar. https://www.macpac.gov/publication/medicaid-managed-care-capitation-rate-setting/ 
8. Centers for Medicare & Medicaid Services (CMS). Calendar year 2024 Medicare Physician Fee Schedule final rule: Medicare Shared Savings Program. Baltimore (MD): CMS; 2023 Nov 2. https://www.cms.gov/newsroom/fact-sheets/calendar-year-cy-2024-medicare-physician-fee-schedule-final-rule-medicare-shared-savings-program 
9. Centers for Medicare & Medicaid Services (CMS). Multi-payer alignment. Baltimore (MD): CMS; 2024 Oct 18. Accessed 2026 Jul 16. https://www.cms.gov/priorities/innovation/key-concepts/multi-payer-alignment 
10. Bond AM, Civelek Y, Schpero WL, et al. "Long-term spending of accountable care organizations in the Medicare Shared Savings Program." JAMA. 2025;333(21):1897-1905. doi:10.1001/jama.2025.3870