state-exclusions · CMS
state-exclusions · CMS
state-exclusions · CMS
state-exclusions · CMS
The OIG List of Excluded Individuals and Entities — the LEIE — is the federal registry that screening programs treat as the master list of providers barred from Medicare, Medicaid, and other federal health programs. But it is not the only list. Every state Medicaid agency runs its own exclusion program, on its own authority and its own clock. When the two lists disagree, the disagreement is the story: a provider barred by a state but absent from the federal file is invisible to anyone who screens federally and stops there.
This study quantifies that gap. It joins the state Medicaid exclusion records Fonteum holds — ten states — against the federal OIG LEIE on the National Provider Identifier, and measures how many state-excluded providers have no federal record at all.
The gap, in one number
64.3% of NPI-identified state-excluded providers are missing from the federal list: 3,117 of 4,851 across the ten live states carry no matching record on the OIG LEIE. Put the other way, federal-only screening catches only 1,734 of the 4,851 — it clears the rest as having no exclusion on file.
The breakdown by state shows the pattern is not a single-state artifact. The three largest-sample states sit between 54% and 60%; the newer, smaller-sample states run higher still, and only Georgia and Montana — on very small matchable samples — come in lower.
| State | NPI-identified state-excluded | Not on federal LEIE | On both lists | Share invisible to federal screening |
|---|---|---|---|---|
| New York (OMIG) | 2,254 | 1,347 | 907 | 59.8% |
| Pennsylvania (DHS) | 974 | 569 | 405 | 58.4% |
| Ohio (ODM) | 673 | 363 | 310 | 53.9% |
| Iowa (Medicaid) | 313 | 263 | 50 | 84.0% |
| Maryland (MDH) | 263 | 221 | 42 | 84.0% |
| Washington (HCA) | 210 | 178 | 32 | 84.8% |
| North Carolina (DHHS) | 149 | 128 | 21 | 85.9% |
| Mississippi (DOM) | 138 | 106 | 32 | 76.8% |
| Montana (DPHHS) | 41 | 15 | 26 | 36.6% |
| Georgia (DCH-OIG) | 35 | 8 | 27 | 22.9% |
| All ten states | 4,851 | 3,117 | 1,734 | 64.3% |
The denominator throughout is NPI-identified, in-force state exclusions. A state exclusion counts as in force when it carries no reinstatement date, or a reinstatement date still in the future — the same test the federal side uses. The join is on NPI only, never on name.
Why federal-only screening misses these providers
The gap is structural, not a data error. State Medicaid agencies exclude providers under state authority for Medicaid-specific reasons, and a federal exclusion does not automatically follow. The OIG may adopt a state action under its permissive §1128(b) authority — most commonly §1128(b)(4) for a state license revocation, surrender, or suspension — but that adoption is discretionary and lagged. Many state bars never cross over at all, and those that do can take months to a year to appear on the federal file.
A second mechanism is timing. An exclusion is a trailing record on both sides, but the two trailing records do not move together. A provider can be terminated from a state Medicaid program well before — or entirely without — a parallel federal action. The federal CCN- and enrollment-side machinery moves on its own cadence, a pattern we documented in the lag between a termination event and CMS deactivation.
An exclusion is only as good as the list you check. Screen the federal LEIE alone and nearly two in three NPI-identified state-barred providers come back clean.
The companion fact from the federal side reinforces the point. As we found in who actually gets barred from Medicare and why, the single largest basis on the federal LEIE is itself a downstream record of state licensing discipline — §1128(b)(4) license actions are 41% of the list. The federal file is, in large part, a lagging echo of state decisions. The state lists are where many of those decisions land first.
How the gap differs by state
New York sets the ceiling among the large-sample states: 1,347 of its 2,254 NPI-identified state-excluded providers — 59.8% — have no federal record. Pennsylvania (58.4%) and Ohio (53.9%) follow closely. These three states carry the bulk of the matchable population and agree within six points, which is what gives the pooled 64.3% figure its weight: it is not the artifact of one outlier.
The newer states ingested into the ring push the pooled figure higher. Iowa, Maryland, Washington, North Carolina, and Mississippi all sit between 77% and 86% invisible — every one a clear majority — though each rests on a smaller matchable sample than the three anchor states. The direction is consistent across every state with a meaningful denominator: a majority of NPI-identified state bars have no federal counterpart.
Georgia's 22.9% and Montana's 36.6% sit apart because their matchable samples are tiny — only 35 of Georgia's 1,369 in-force records, and 41 of Montana's 174, carry an NPI. With denominators that small, the percentages are unstable and should be read as illustrative, not as evidence that those states' providers are better represented federally.The ring now spans ten states, from New York's 2,254 NPI-identified providers down to Georgia's 35. As it expands, each new state widens the matchable base and re-tests the gap — and so far every addition has reinforced rather than softened the headline.
The providers with no NPI at all
15,293 in-force state exclusion records carry no NPI — and they are a screening problem of their own. Across the ten states, only a minority of exclusion records include an NPI at all: 4,851 distinct NPIs sit inside 20,696 in-force records. The remainder name an excluded party with no identifier that maps to the federal list.
| State | In-force records | With no NPI | Distinct NPI-identified providers |
|---|---|---|---|
| New York | 8,904 | 6,633 | 2,254 |
| Pennsylvania | 4,913 | 3,712 | 974 |
| Ohio | 1,980 | 1,248 | 673 |
| Maryland | 1,603 | 1,339 | 263 |
| Georgia | 1,369 | 1,333 | 35 |
| Iowa | 1,150 | 795 | 313 |
| Washington | 244 | 34 | 210 |
| Mississippi | 193 | 53 | 138 |
| Montana | 174 | 132 | 41 |
| North Carolina | 166 | 14 | 149 |
| All ten states | 20,696 | 15,293 | 4,851 |
These records are excluded from the matchable denominator above, because matching them to the federal list would require a name match — and a name match is not a defensible identity assertion. We do not guess. But the practical implication is blunt: NPI-based federal screening cannot reach these parties at all, so they compound the gap rather than shrink it. The same limitation applies in reverse, and it is the reason the federal LEIE itself carries an NPI on only about one record in ten, as documented in the LEIE reference study.
What this means for screening compliance
A federal-LEIE-only screen is not a complete exclusion check, and the magnitude here puts a number on the shortfall: 64.3% of NPI-identified state-barred providers fall outside it. The OIG's own guidance is that an employer or contractor must screen against all applicable exclusion lists — federal and the relevant state Medicaid lists — before hiring or contracting, and on an ongoing basis. Employing or contracting an excluded party in a federally billable role carries civil monetary penalty exposure under a "knew or should have known" standard.
The constructive read is that the two layers of lists do something neither does alone. The federal LEIE is national but lagging and NPI-sparse; the state lists are current and program-specific but jurisdictionally fragmented. Checked together, across frozen point-in-time snapshots, they close gaps in each other. Fonteum exposes both layers through a single NPI lookup — the state exclusion data and the federal OIG LEIE — so a "barred anywhere on the lists we hold" answer does not depend on which single list a screener happened to check. It is a screening aid: re-confirm any match against the primary source before acting, and read the absence of a match as "nothing in the lists Fonteum currently holds", never as a guarantee that none exists.
Methodology
Every figure is a direct join between two public, read-only Postgres tables: state_exclusions (the State Exclusion Ring — ten state Medicaid programs) and oig_leie_exclusions (the OIG monthly LEIE bulk download, release 2026-05-08, 68,055 active records). The join key is the 10-digit NPI, trimmed of whitespace; a name is never used to assert a match.
A record is treated as in force when its reinstatement date is null or still in the future relative to the publish date — the same test the production exclusion lookup applies, and applied identically to both tables. The matchable denominator is the set of distinct, in-force, NPI-identified providers per state; records with no NPI are excluded from it and reported separately. The federally-invisible count is the subset of those NPIs with no row in oig_leie_exclusions. The exact SQL is in the reproducibility block below and the provenance methodology documents the source-provenance contract. Methodology version: exclusion-gap/v1.
Limitations
- Ten states, not fifty. Coverage is New York, Ohio, Georgia, Pennsylvania, North Carolina, Maryland, Washington, Iowa, Mississippi, and Montana. The 64.3% figure describes the ten live states, not the nation.
- NPI is the floor, not the ceiling. 15,293 in-force state records carry no NPI and cannot be matched to the federal list by identifier; they are reported separately, never guessed at by name.
- Snapshot, not cumulative. Both lists are point-in-time. State files and the federal release shift over time; these figures reflect the current ingested snapshots.
- Some states are small samples. Georgia and Montana carry only 35 and 41 NPI-identified records, so their per-state shares are illustrative, not stable; the newer mid-size states rest on smaller denominators than the three anchor states.
- A compliance signal, aggregate-only. Exclusion counts are an enforcement and screening signal, never a measure of care quality. No individual excluded party is named, surfaced, or attached to any provider profile in this study.
Sources
- OIG LEIE — online database and monthly downloads — the federal exclusion list and the comparison anchor.
- OIG — effect of an exclusion (screening duty, civil monetary penalties) — the obligation to screen all applicable lists.
- New York OMIG — Medicaid exclusions — the New York state source.
- Ohio Department of Medicaid — provider exclusion and suspension list — the Ohio state source.
- Georgia DCH — Office of Inspector General — the Georgia state source.
- Pennsylvania DHS — sanctioned providers — the Pennsylvania state source.
- Fonteum — state Medicaid exclusion data, all ingested states — the consolidated state exclusion layer across all ten states.
- 42 U.S.C. § 1320a-7 (Social Security Act § 1128) — the federal exclusion statute, including the permissive §1128(b)(4) license-action authority.
Frequently asked questions
- What is the federal–state exclusion gap?
- It is the share of providers excluded by a state Medicaid program that carry no matching record on the federal OIG List of Excluded Individuals and Entities (LEIE). Across ten state Medicaid programs, 3,117 of 4,851 NPI-identified state-excluded providers — 64.3% — are absent from the federal list. An organization that screens the federal LEIE alone never sees them.
- Why would a provider be excluded by a state but not by the federal OIG?
- State Medicaid agencies run their own exclusion programs and act on their own authority and timeline. A state can bar a provider for a Medicaid-specific reason — a state license action, a state fraud referral, an administrative termination — without an OIG exclusion ever following. The OIG can adopt many of these under its permissive §1128(b) authority, but adoption is discretionary and lagged, so a large standing set of state bars never reaches the federal list.
- How many state-excluded providers does federal-only screening miss?
- Among providers with an NPI, 3,117 of 4,851 — 64.3% — across the ten live states. New York alone accounts for 1,347 of them, Pennsylvania 569, and Ohio 363; the seven newer states add the rest. These are the providers a federal-LEIE-only check would return as having no exclusion on file.
- Does the gap change if you count federal exclusions that were later reinstated?
- No. The OIG removes reinstated parties from the published LEIE, so the federal file is already a current-active snapshot. The 64.3% figure is identical whether or not an in-force filter is applied on the federal side.
- What about state exclusions with no NPI?
- A further 15,293 in-force state exclusion records carry no NPI at all. They cannot be matched to the federal list by identifier, so NPI-based federal screening cannot reach them either. They are excluded from the matchable denominator and reported separately rather than guessed at by name.
- Which states are included, and why not all 50?
- Ten states publish a usable Medicaid exclusion file and are ingested: New York (OMIG), Ohio (ODM), Georgia (DCH Office of Inspector General), Pennsylvania (DHS), North Carolina (DHHS), Maryland (MDH), Washington (HCA), Iowa (Medicaid), Mississippi (DOM), and Montana (DPHHS). State Medicaid exclusion data is fragmented and inconsistently published, so coverage expands one primary source at a time.
- Can I reproduce these numbers?
- Yes. Every figure is a direct join between the public state_exclusions and oig_leie_exclusions tables on NPI. The exact SQL is published in the reproducibility block below; each count resolves to specific rows in specific frozen snapshots, and no match is ever inferred from a name.
Who uses this data
The source data behind this study is public
Compliance teams, journalists, and researchers work from the same federal source families cited above — queried by NPI or facility identifier through Fonteum’s open dataset pages and API. Every figure traces to a frozen, downloadable snapshot you can reproduce yourself.
Datasets used
Reproducibility
Every claim, reproducible
The SQL
-- The federal–state exclusion gap — fully reproducible query.
--
-- Question: how many providers excluded by a STATE Medicaid program are
-- invisible to FEDERAL-only screening — i.e. carry no record on the OIG LEIE?
--
-- Sources:
-- public.state_exclusions — State Medicaid exclusion lists (the State
-- Exclusion Ring: ten states — NY OMIG, PA DHS,
-- OH ODM, IA, MD, WA, NC, MS, MT, GA DCH-OIG).
-- Public, read-only.
-- public.oig_leie_exclusions — OIG List of Excluded Individuals/Entities,
-- federal monthly bulk download, release
-- 2026-05-08, 68,055 active records (7,025 with
-- an NPI). Public, read-only.
--
-- Join key: NPI only (10-digit, btrim). We never match on name — a name match
-- is not a defensible identity assertion, so rows with no NPI are excluded from
-- the matchable denominator and reported separately (see no-NPI query below).
--
-- "In force" mirrors the production exclusion lookup (src/lib/exclusions): a row
-- is in force when reinstatement_date IS NULL OR reinstatement_date > today.
-- Applied to BOTH tables. Date basis: current_date (2026-06-15 at publish).
--
-- Every headline figure in the study resolves to one of the rows below.
WITH se_inforce AS (
SELECT state,
nullif(btrim(npi), '') AS npi
FROM public.state_exclusions
WHERE reinstatement_date IS NULL OR reinstatement_date > current_date
),
fed_inforce AS (
-- Distinct federal NPIs in force. (The LEIE drops reinstated parties from the
-- published file, so this set equals "any LEIE row by NPI" — the 64.3% gap is
-- identical whether or not the in-force filter is applied on the federal side.)
SELECT DISTINCT btrim(npi) AS npi
FROM public.oig_leie_exclusions
WHERE nullif(btrim(npi), '') IS NOT NULL
AND (reinstatement_date IS NULL OR reinstatement_date > current_date)
),
state_npi AS ( -- distinct NPI-identified, in-force provider per state
SELECT DISTINCT state, npi FROM se_inforce WHERE npi IS NOT NULL
),
per_state AS (
SELECT s.state,
count(*) AS matchable_providers,
count(*) FILTER (WHERE f.npi IS NULL) AS federally_invisible
FROM state_npi s
LEFT JOIN fed_inforce f USING (npi)
GROUP BY s.state
),
overall_npi AS ( SELECT DISTINCT npi FROM se_inforce WHERE npi IS NOT NULL ),
overall AS (
SELECT 'ALL'::text AS state,
count(*) AS matchable_providers,
count(*) FILTER (WHERE f.npi IS NULL) AS federally_invisible
FROM overall_npi o
LEFT JOIN fed_inforce f USING (npi)
),
u AS ( SELECT * FROM per_state UNION ALL SELECT * FROM overall )
SELECT
state,
matchable_providers, -- NPI-identified denominator
federally_invisible, -- NOT on the federal LEIE
(matchable_providers - federally_invisible) AS on_federal_too, -- caught by both
round(100.0 * federally_invisible / nullif(matchable_providers, 0), 1)
AS pct_invisible
FROM u
ORDER BY state;
-- state matchable invisible on_federal_too pct_invisible
-- ALL 4,851 3,117 1,734 64.3
-- NY 2,254 1,347 907 59.8
-- PA 974 569 405 58.4
-- OH 673 363 310 53.9
-- IA 313 263 50 84.0
-- MD 263 221 42 84.0
-- WA 210 178 32 84.8
-- NC 149 128 21 85.9
-- MS 138 106 32 76.8
-- MT 41 15 26 36.6 (small sample — see study)
-- GA 35 8 27 22.9 (small sample — see study)
-- Rows with NO NPI — excluded from the matchable denominator above and reported
-- separately. These in-force state exclusions cannot be matched to the federal
-- list by identifier at all, so federal NPI-based screening cannot reach them.
SELECT
coalesce(state, 'ALL') AS state,
count(*) FILTER (WHERE inforce) AS inforce_rows,
count(*) FILTER (WHERE inforce AND npi IS NULL) AS inforce_no_npi_rows,
count(DISTINCT npi) FILTER (WHERE inforce AND npi IS NOT NULL) AS inforce_distinct_npi
FROM (
SELECT state,
nullif(btrim(npi), '') AS npi,
(reinstatement_date IS NULL OR reinstatement_date > current_date) AS inforce
FROM public.state_exclusions
) se
GROUP BY ROLLUP (state)
ORDER BY state NULLS LAST;
-- state inforce_rows inforce_no_npi_rows inforce_distinct_npi
-- NY 8,904 6,633 2,254
-- PA 4,913 3,712 974
-- OH 1,980 1,248 673
-- MD 1,603 1,339 263
-- GA 1,369 1,333 35
-- IA 1,150 795 313
-- WA 244 34 210
-- MS 193 53 138
-- MT 174 132 41
-- NC 166 14 149
-- ALL 20,696 15,293 4,851The snapshot
| dataset_id | state-exclusions |
| snapshot_date | 2026-06-15 |
| sha256 | |
| doi | 10.5072/fonteum/federal-state-exclusion-gap-2026 |
| slsa_provenance_url |
The JOINs
join key: state_exclusions.npi = oig_leie_exclusions.npi -- 10-digit NPI, btrim, never a name match in_force = reinstatement_date IS NULL OR reinstatement_date > current_date -- applied to both tables matchable = distinct in-force state NPI (non-empty); rows with no NPI excluded and reported separately federally_invisible = matchable NPI with NO row in oig_leie_exclusions on the same NPI share = federally_invisible / matchable -- 3,117 / 4,851 = 64.3%
The pipeline version
| git_sha | |
| slsa_provenance | |
| methodology_version | exclusion-gap/v1 |
Reproduce this
Run the exact query against the frozen 2026-06-15.
Cite this study
Citation-ready for researchers and AI.
Check the chain
Each figure is snapshot-attested — re-derive the hash from the federal file.
state-exclusions · 2026-06-15SHA-256 a3f1c9…7e6b- FINANCIAL DISTRESS · JUN 2026The OIG exclusion list, explained: who gets barred from Medicare, and whyThe OIG List of Excluded Individuals and Entities (LEIE) holds 68,055 active exclusions spanning 1977–2026. The most common reason to be barred from Medicare is not fraud — it is losing a state license: §1128(b)(4) license actions are 41% of the list. And only 10.3% of records carry an NPI, so the list is mostly non-clinicians.
- FINANCIAL DISTRESS · MAY 2026Provider exclusions aren't rising — but they cluster around distressed operatorsNew additions to the OIG exclusion list are flat to declining — down 2.4% year-over-year through April 2026, and down 18.7% across full-year 2024 to 2025. The count is not the story. What concentrates is the composition: new exclusions cluster in facilities already showing the balance-sheet markers of financial distress.
- ACCESS · APR 2026A March spike in Medicare enrollment deactivations thinned provider supply in shortage areasMedicare enrollment deactivations in PECOS ran 28% above the trailing-twelve-month average in March 2026 — and the spike was not uniform. Deactivations in HRSA-designated shortage areas grew 41% against trend, versus 19% elsewhere. The places least able to absorb a departure lost providers fastest.
- CARE QUALITY · JUN 2026How fast do nursing homes fix what surveyors cite? 28.5 days for the harmful onesAcross 415,849 corrected CMS nursing home health deficiencies, the mean time from survey to documented correction is 32 days — but the harm-level citations, Severity G and above, close faster, in 28.5 days. The more severe the finding, the quicker the fix. Texas and Illinois correct in about two weeks; Washington, D.C. takes nine.
- WORKFORCE · JUN 2026Who is enrolled in Medicare? The nurse practitioner is now the most common clinician413,539 nurse practitioner enrollments make NPs the single most common clinician type in Medicare's provider-enrollment file — 13.9% of all 2.98 million PECOS records, nearly triple the largest physician specialty. Together, NPs and physician assistants are one in five enrollments. Advanced-practice providers now anchor the Medicare workforce.
Federal source citations
Fonteum Research · June 15, 2026 · All figures trace to the frozen federal-data snapshot cited above.