aceTreatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning
Medicaid care-management programs typically allocate scarce outreach capacity to beneficiaries with the highest predicted risk of an acute event, assuming that risk and responsiveness are aligned and stable across short intervals. The authors tested whether targeting outreach by predicted individualized treatment effect-the conditional average treatment effect (CATE) recomputed each calendar month-outperforms risk-based targeting. The authors analyzed 164,063 adult Medicaid beneficiaries (2,670,806 person-months…
Modeling case managers' care planning decisions for community dwelling disabled elders in Medicaid home and community based services waiver programs by Degenholtz, Howard United States. Health Care Financing Administration. Public domain
Researchers compared two ways to allocate scarce Medicaid care-management phone outreach each month for 164,063 beneficiaries in Washington and Virginia. Using a causal forest to estimate individualized treatment effects, they found targeting the top decile by predicted effect prevented 13.3 acute events per 2000 members per month, compared with 2.5 events under conventional top-decile risk targeting.
The result matters because it suggests fixed outreach capacity can prevent substantially more hospital use without increasing disparities, if allocation shifts from who is riskiest to who is most responsive. Uncertainty remains about generalizability beyond the two states, durability outside the 2023-2025 study window, and operational feasibility of monthly CATE recomputation in routine Medicaid programs.
- Study included 164,063 adult Medicaid beneficiaries and 2,670,806 person-months in Washington and Virginia from January 2023 to December 2025.
- CATEs were estimated using a causal forest with cross-fitted propensity and outcome-model nuisance functions, augmented by within-person fixed effects.
- Policy values were estimated via doubly-robust off-policy evaluation comparing top-decile risk versus top-decile CATE allocation rules.
- Within-person variance accounted for 63.6% of total CATE variance, supporting monthly recomputation of treatment effects.
Monthly allocation of Medicaid care-management outreach by predicted individualized treatment effect prevented substantially more ED visits or hospital admissions than risk-based allocation at the same 10% capacity.
The rundown
The authors analyzed 164,063 adult Medicaid beneficiaries enrolled in community-based care management in Washington and Virginia between January 2023 and December 2025. Exposure was a completed care-manager telephone contact within a calendar month; outcome was emergency department visit or hospital admission within 30 days.
Two monthly allocation rules at 10% capacity were compared using doubly-robust off-policy evaluation: top decile predicted event probability versus top decile predicted CATE. Validation included cross-state replication, a marginal structural model targeting for time-varying confounding, and a staggered-rollout instrumental variable with pretrend, exclusion-restriction, and monotonicity diagnostics.
Sources
- Peer-reviewedPopulation Health Management2026-07-24
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