1%Medicaid Spending (0.24% of Total Health Spending)

Improving Auto-Assignment in Medicaid Managed Care

Authors: Chima Ndumele, Yale University; Jacob Wallace, Yale University

Issue Summary: The Medicaid program accounts for approximately 28% of state budgets, covers 77 million people, and represents 16% of US health care spending. Approximately 70% of Medicaid enrollees are now enrolled in Medicaid managed care plans operated by private firms. There is a growing body of work illustrating that insurance plan design (including the design of Medicaid managed care plans) can have a substantial influence on enrollee health, satisfaction, and spending. In particular, health plan networks can be structured so as to meaningfully reduce health spending without harming quality or satisfaction. Currently, a significant portion of Medicaid managed care plan enrollees do not make an active choice to enroll in a particular plan, and instead are assigned to a plan by the state via a process known as “auto-assignment.” Assuming auto-assignment rates are stable over the next decade, states will auto-assign approximately 25 million Medicaid enrollees to their respective Medicaid managed care plans during this time. This creates an opportunity to develop novel methods for auto-assignment that capitalize on differences in plan quality and networks to reduce Medicaid spending without adversely impacting enrollee health or satisfaction.

Policy Proposal: We encourage states to adopt smart defaults when auto-assigning Medicaid enrollees to managed care plans. States have an opportunity to leverage auto-assignment as a tool to better understand the efficiency of the plans in their market and then as a tool to steer enrollees into plans that constrain spending without reducing enrollee satisfaction or health. For example, research suggests that auto-assigning Medicaid enrollees into narrower network plans that include their usual providers can lower cost without harming enrollee satisfaction or health.

Potential Savings: Based on evidence from one of the largest Medicaid managed care programs in the United States, we estimate the adoption of smarter defaults would lower spending in Medicaid by approximately 1%. This translates to $3.7 to $9.2 billion in annual savings, or a 0.10% to 0.24% reduction in national health expenditures in the US.


The Medicaid program provides health insurance coverage to over 77 million individuals and accounts for approximately 16% of US health spending at a cost of $613.5 billion per year. A significant portion of Medicaid costs are borne by states, with Medicaid program costs now accounting for approximately 28.7% of state governments’ expenditures. The vast majority of states now rely primarily on risk-based managed care plans operated by private firms to provide benefits to Medicaid enrollees. At present, approximately 70% of Medicaid enrollees are in a risk-bearing managed care plan.

There is a growing body of work suggesting that insurance plans can have a substantial impact on enrollee health spending and outcomes. These effects can be on a similar scale as—or larger in magnitude than—the effects of demand-side interventions like patient copayments or deductibles. Much of the academic research suggests that the differences in enrollee outcomes across insurance plans are the result of differences in the networks of participating providers across plans. Recent evidence from New York State, for example, illustrates that if enrollees are assigned to narrower network plans that include their usual providers, health spending can be reduced by 2% to 5% without adversely impacting enrollee satisfaction or health.

A large share of Medicaid enrollees do not make an active choice to enroll in a particular Medicaid managed care plan. As we illustrate in Figure 1, in some states, all of the state’s Medicaid enrollees are auto-assigned to a plan. The best estimates are that nationwide, 25 million individuals will need to be auto-assigned to a Medicaid managed care plan over the next decade. As a result, there is significant scope for state Medicaid programs to structure auto-assignment to plans in ways that make their Medicaid programs more efficient.

Figure 1. Auto-Assignment Rate by State

Chart Auto-Assignment Rate by State
Note: Hawaii and Tennessee auto-assign all beneficiaries to a health plan and then offer an opportunity to change plans. Author calculations based on Kaiser Family Foundation (KFF 2015, 34).

The Effects of Medicaid Managed Care Plans on Enrollee Outcomes and Health Spending

There is significant variation in how states currently auto-assign Medicaid enrollees to managed care plans, reflecting the lack of a clear consensus on best practice. It is common for states to include logic that keeps family members together or assigns enrollees to plans they’ve previously chosen. However, many auto-assignees are allocated to plans via algorithms that attribute them at random to qualifying plans. In some states, the allocation of auto-assignees is equal across plans, but in others plans with larger market shares receive more (or fewer) auto-assignees. While the diversity of approaches likely reflects, in part, the competing priorities of policy makers in different states, the lack of a clear best practice indicates that substantial potential exists to improve the efficiency of the Medicaid program with smarter default policies.

When states randomly assign Medicaid enrollees to plans, they offer researchers the opportunity to identify how Medicaid managed care plans causally impact enrollee outcomes and health spending. Here, academic research suggests that the lowest-spending Medicaid managed care plans can reduce health spending by over 30% (Geruso, Layton, and Wallace 2020). Unfortunately, in some instances, these lower-spending plans constrain cost by reducing enrollees’ utilization of both needed and unneeded services, rather than simply eliminating the use of wasteful services.

However, recent research suggests that narrower network plans could reduce enrollee spending without harming consumer satisfaction or increasing the likelihood of adverse health events. Wallace (2020) studies auto-assignment in New York and finds that enrollment in a narrower network plan can reduce enrollee spending by approximately 10%. Although these reductions are driven by decreases in the quantity of care consumed, including some preventive services, they do not appear to increase the likelihood of adverse health events. While narrower networks do reduce enrollee satisfaction, the evidence suggests this effect can be mitigated by ensuring that enrollees retain access to their preferred providers.

The experience in New York suggests that the potential for auto-assignment to influence outcomes is substantial and that there are opportunities to lower cost without sacrificing enrollee satisfaction. Relative to a policy that randomly assigns enrollees to plans, a smarter default for auto-assignees could reduce spending by nearly 3% while maintaining enrollee satisfaction. These savings come via shifting enrollees to narrower network plans (to reduce spending) that include the enrollees’ usual sources of care (which increases satisfaction).

Policy Recommendation

Because Medicaid managed care is mandatory in most states, but many Medicaid enrollees do not actively choose a managed care plan within a designated choice period, policy makers need a method to auto-assign enrollees to plans. Whenever possible, policy makers should structure these auto-assignments in ways that improve health, maintain enrollee satisfaction, and reduce health spending. Early evidence suggests that one mechanism to lower spending without adversely impacting quality or enrollee satisfaction is to auto-assign enrollees to narrower network plans that include the enrollees’ usual providers within their networks. Going forward, more work should be done to determine how to most efficiently match enrollees to plans in ways that increase the efficiency of state Medicaid programs.

Potential Savings

Based on evidence from one of the largest state Medicaid programs in the United States, we estimate that the adoption of smarter defaults in state auto-assignment algorithms could lower total spending in Medicaid by approximately 1%. This translates to approximately $3.7 to $9.2 billion in annual savings, or a 0.10% to 0.24% reduction in current national health expenditures in the US. The range in spending comes from different assumptions about the potential cost savings that could be achieved via smart defaults. The more conservative projection assumes that smarter defaults will only leverage the comparative advantage of plans, reassigning enrollees to maximize efficiency without changing plan market shares. The more aggressive projections relax the constraint that plan shares be maintained. Without this constraint, the projected savings are larger, but so is the potential for reshaping the market in unintended ways. Hence, states may initially prefer a more incremental approach.


Center for Medicare and Medicaid Services. 2020. “NHE Fact Sheet.” Centers for Medicare and Medicaid Services. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet#:~:text=Projected%20NHE%2C%202019%2D2028%3A&text=Because%20national%20health%20expenditures%20are,to%2019.7%20percent%20in%202028.

Geruso, Michael, Timothy Layton, and Jacob Wallace. 2020. “Are All Managed Care Plans Created Equal? Evidence from Random Plan Assignment in Medicaid.” NBER Working Paper 22762.

Kaiser Family Foundation. 2015. “Medicaid Reforms to Expand Coverage, Control Costs and Improve Care: Results from a 50-State Medicaid Budget Survey for State Fiscal Years 2015 and 2016.” Kaiser Family Foundation. http://files.kff.org/attachment/report-medicaid-reforms-to-expand-coverage-control-costs-and-improve-care-results-from-a-50-state-medicaid-budget-survey-for-state-fiscal-years-2015-and-2016.

Kaiser Family Foundation. 2020. “Total Monthly Medicaid and CHIP Enrollment.” Kaiser Family Foundation. https://www.kff.org/health-reform/state-indicator/total-monthly-medicaid-and-chip-enrollment/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D.

MACPAC. 2016. “Medicaid’s Share of State Budgets.” Medicaid and CHIP Payment and Access Commission.https://www.macpac.gov/subtopic/medicaids-share-of-state-budgets/.

Swaminathan, Shailender, Chima Ndumele, Sarah Gordon, Yoojin Lee, and Amal Trivedi. 2020. “Association of Medicaid-Focused or Commercial Medicaid Managed Care Plan Type with Outpatient and Acute Care.” JAMA Internal Medicine, 180 (12): 1672–1679.

Wallace, Jacob. 2020. “What Does a Provider Network Do? Evidence from Random Assignment in Medicaid Managed Care.” Mimeo accessible via: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3544928.