Access Signals

Access Signals — Is the System Reaching People?

Access isn't just about enrollment numbers. It's about whether people can actually see a doctor when they need one. When they can't, they end up in the ER. When providers are stretched thin, quality suffers. This page tracks the warning signs.

Provenance

Methodological Guardrails

ER Reliance

When people use emergency rooms for care that a primary care doctor could handle, it usually means one thing: they couldn't get an appointment. ER visits cost 3-10x more, don't provide continuity, and signal a system that isn't meeting people where they are.

ER vs Primary Care Beneficiaries Over Time

SELECT * FROM medicaid.er_vs_primary_care

ER vs Primary Care Spending

ER Share of Combined Spending

ER-to-Primary-Care Beneficiary Ratio

A ratio above 1.0 means more people are visiting ERs than primary care offices in a given month. [Inference, Medium confidence] Even a ratio that stays flat while beneficiary touchpoints rise means the system isn't steering people toward lower-cost, higher-quality primary care.

Citation: er_data (source medicaid.er_vs_primary_care).

Provider Capacity

Provider counts alone don't tell the access story. What matters is whether there are enough providers relative to the people who need them.

Provider Count Over Time

SELECT * FROM medicaid.provider_attrition
WHERE category = '${inputs.capacity_category.value}'

Beneficiaries per Provider

The Squeeze: Supply vs Demand

The most important access question: are we adding providers fast enough to keep up?

Growth Comparison: Jan 2018 to Oct 2024

WITH bookends AS (
    SELECT
        category,
        month,
        active_providers,
        total_beneficiaries,
        ROW_NUMBER() OVER (PARTITION BY category ORDER BY month ASC) AS rn_first,
        ROW_NUMBER() OVER (PARTITION BY category ORDER BY month DESC) AS rn_last
    FROM medicaid.provider_attrition
)
SELECT
    f.category,
    f.active_providers AS start_providers,
    l.active_providers AS end_providers,
    ROUND((l.active_providers - f.active_providers) * 100.0 / NULLIF(f.active_providers, 0), 1) AS provider_growth_pct,
    f.total_beneficiaries AS start_beneficiaries,
    l.total_beneficiaries AS end_beneficiaries,
    ROUND((l.total_beneficiaries - f.total_beneficiaries) * 100.0 / NULLIF(f.total_beneficiaries, 0), 1) AS beneficiary_growth_pct,
    ROUND((l.total_beneficiaries - f.total_beneficiaries) * 100.0 / NULLIF(f.total_beneficiaries, 0), 1)
    - ROUND((l.active_providers - f.active_providers) * 100.0 / NULLIF(f.active_providers, 0), 1) AS gap
FROM bookends f
JOIN bookends l ON f.category = l.category AND l.rn_last = 1
WHERE f.rn_first = 1
ORDER BY gap DESC

The Gap column shows the difference between beneficiary growth and provider growth in percentage points. Positive values mean demand is outpacing supply. Every row with a positive gap represents a category where access is getting harder, not easier.

This is the fundamental tension in Medicaid: expanding coverage is necessary but not sufficient. Without investment in provider capacity, enrollment gains become paper coverage — people have a card but can't find a doctor who will see them.

Provider Attrition by Specialty

Which types of providers are leaving Medicaid — or entering it? By joining billing data to the NPI Registry's taxonomy codes, we can track active provider counts for specific specialties over time.

207Q00000X — Active Providers Over Time

SELECT
    taxonomy_code,
    SUM(paid_millions) AS total_paid_m
FROM medicaid.attrition_by_specialty
GROUP BY taxonomy_code
ORDER BY total_paid_m DESC
LIMIT 15

Reproduce This Page

cd dashboard
export EVIDENCE_SOURCE__medicaid__token="<your_motherduck_token>"
export EVIDENCE_SOURCE__medicaid__database="medicaid"
npm run sources
npm run build
npm run preview
# then open http://localhost:3000/access