ADHD Equity Gap Decomposer: Access vs. Utilization Analytics for Children
An analytics tool for health agencies to see if racial gaps in ADHD care are caused by getting into the clinic or staying in care.
An analytics dashboard for Medicaid agencies, health plans, and children's hospitals that splits the racial/ethnic gap in children's ADHD care into a 'can't get in the door' (access) component versus a 'doesn't continue care' (utilization) component, so they know which intervention to fund.
Concept
A measurement product that answers one question for a state Medicaid agency, health plan, or pediatric system: in OUR population, is the racial/ethnic gap in children's ADHD care an access problem or a utilization problem — and therefore, which intervention should we buy?
The tool ingests the payer's or system's claims and enrollment data for children ages 5–17 with an ADHD diagnosis and reproduces the source study's decomposition: access outcomes (any past-year ADHD or psychiatric treatment visit; whether ADHD medication was ever accessed) modeled separately from utilization outcomes (number of medication fills, total mental health treatment expenditures), adjusted for race-ethnicity, age, sex, and treatment need per the Institute of Medicine disparities definition, with socioeconomic status controlled in the utilization models.
The output is an equity dashboard showing, for each racial/ethnic group, how much of the gap is access versus utilization — plus geographic and provider-supply overlays that point to concrete barriers (specialist shortage by ZIP code, no telehealth slots, transportation). It ends with a ranked, budget-sized intervention menu: first-visit navigation and school-linked referral where the gap is access-driven; adherence and cost-support programs where it is utilization-driven.
Why now
- Paper [2], using nationally representative MEPS data on 5,838 children ages 5–17 with ADHD (2011–2019), found that Black, Hispanic, and Asian children had significantly lower rates of any past-year treatment visit and lower ADHD medication access than White children — but that once treatment was accessed, utilization differences were largely accounted for by socioeconomic status.
- The study's own conclusion is the product brief: disparities "may be driven primarily by disparities in access rather than in utilization," so interventions should target access among minority children — yet most payer equity programs default to adherence and utilization campaigns, which this evidence says will not close the gap.
- [0] confirms that detection and treatment disparities in underrepresented minorities persist even as overall awareness rises, and [5] provides current national baselines for diagnosed prevalence and treatment receipt by subgroup, giving the dashboard external benchmarks to compare against.
Written by GLM · 10 Oct 2026
AI assessment
A high-utility analytics tool that transforms a specific academic finding into a decision-support engine for healthcare payers to optimize equity spending.
- Evidence strength 5/5
- The idea directly operationalizes the methodology and findings of Paper [3], which specifically distinguishes between access and utilization disparities using a large, representative dataset.
- Market pull 4/5
- Medicaid agencies and health plans have strong regulatory and financial incentives to meet health equity mandates, making them likely buyers for tools that justify specific intervention spending.
- Novelty & moat 3/5
- While it shares a user base with 'Diagnosed-but-Untreated', it moves from simple case-finding to a strategic decomposition of systemic gaps, providing a distinct analytical value proposition.
- Feasibility 5/5
- The product is essentially a data pipeline and dashboard implementing existing statistical models (logistic and generalized linear regression) on standard claims data.
- Wedge clarity 4/5
- The wedge is sharp: identifying whether a specific population's gap is an 'access' or 'utilization' problem to determine the next budget allocation.
- Simplicity / focus 5/5
- The product is focused on a single analytical output (the decomposition) and a single goal (intervention selection), avoiding platform creep.
Scored by Gemma 4 · 10 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.
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Who benefits
- California Department of Health Care Servicesorganization
Runs Medi-Cal for millions of children; the tool tells the agency whether its pediatric ADHD gap is access- or utilization-driven by race/ethnicity before it commits intervention funding, which generic equity dashboards do not.
- Centers for Medicare & Medicaid Services (CMS)organization
Tracks national health-equity metrics; a standardized access-vs-utilization decomposition gives CMS a repeatable method for identifying where state and plan interventions should target access rather than adherence.
- UnitedHealthcarecompany
Large Medicaid and commercial plan under regulatory pressure on pediatric behavioral-health equity; the decomposition tells it whether to fund first-visit navigation or adherence support in each market.
- Nationwide Children's Hospitalorganization
Large pediatric system with extensive behavioral-health intake; the dashboard shows whether minority children in its catchment are failing at the first appointment or in ongoing care, directing where to redesign intake.
Research it builds on
- ADHD Prevalence Among U.S. Children and Adolescents in 2022: Diagnosis, Severity, Co-Occurring Disorders, and TreatmentMelissa L. Danielson, Angelika Hartl Claussen, Rebecca H. Bitsko et al. · 2024 · 332 citationsAll ideas from this paper →
- ADHD Diagnostic Trends: Increased Recognition or Overdiagnosis?Elie Abdelnour, Madeline Owens Jansen, Jessica A. Gold · 2022 · 129 citationsAll ideas from this paper →
- Racial and Ethnic Disparities in Childhood ADHD Treatment Access and Utilization: Results From a National StudyKelly Guanhua Yang, Michael William Flores, Nicholas J. Carson et al. · 2022 · 43 citationsAll ideas from this paper →