The LEIE Database: Provider Exclusion Trends in 2026

The Office of Inspector General's List of Excluded Individuals and Entities (LEIE) is the federal registry of providers barred from participating in Medicare, Medicaid, and other federal health programs. It is a punitive, trailing record — an exclusion lands long after the conduct that prompted it. That lag is precisely what makes the list useful as a cohort signal rather than a case signal.

Through April 2026, new additions to the LEIE were essentially flat — down 2.4% against the same period in 2025, and down 18.7% across full-year 2024 (3,134 additions) to 2025 (2,549). The raw count is not the story, and it is not climbing; exclusion volume fluctuates with enforcement capacity, statutory deadlines, and a reporting lag that depresses the most recent months. The structure is the story.

Figure withdrawn · 2026-07-14. The monthly series cannot be re-derived from the current production database and is not presented as a current finding. The original values remain recorded in the claim-level audit ledger.

Joining exclusions to enrollment

We linked each new exclusion to its PECOS enrollment record where one existed, then to the cost-report markers we use as a rough proxy for financial distress — negative operating margin across two consecutive cost-report periods, and a current ratio below one. The match rate was 63%; the unmatched remainder skews toward individual practitioners who never carried an institutional enrollment.

Among the matched, institutional exclusions were 2.4 times more likely to be attached to an operator already flagged as distressed than to a financially stable one. The direction is intuitive — distress and misconduct share common upstream causes — but the magnitude is larger than we expected, and it has widened since 2024.

Why the clustering matters

If exclusions were randomly distributed across the provider base, the list would be a poor early-warning instrument — by the time a provider is excluded, the damage is done and the signal is spent. But because new exclusions cluster around operators that the cost reports had already flagged, the two datasets together do something neither does alone: the cost report flags the cohort early, and the exclusion list later confirms which members of that cohort actually failed.

This is the same pattern we found in the SNF quality study: the public, frozen-snapshot data carries a leading signal that the official, trailing designation only later ratifies.

The moat is not any single dataset. It is the join — the cost report flags the cohort early, and the exclusion list later confirms which members of that cohort actually failed.

The corollary is that the predictive value decays the moment either source is read alone. The distress proxy on its own over-flags: plenty of thin-margin operators never cross into misconduct. The exclusion list on its own is too late to act on. Read together, across frozen monthly snapshots, the false positives from the first source are pruned by the confirmations in the second, and what remains is a short, defensible watch-list rather than a long, speculative one.

Methodology

All figures are aggregations over the OIG LEIE snapshot oig-leie/2026-05 (stored under the 2026-05-01 monthly key from the OIG file captured 2026-05-08; the committed cms-leie-2026-05-08.json contains 83,001 records). Year and year-to-date trends count new additions by excl_date. The structural finding joins each new exclusion to its PECOS enrollment by NPI, then to an HCRIS-derived financial-distress proxy: an operator is "distressed" when it posts a negative operating margin across two consecutive cost-report periods and a current ratio below one. The relative-risk figure compares, among matched institutional (entity) exclusions, how many sit at a distressed operator versus a stable one. The match rate to PECOS was 63%; the unmatched remainder skews toward individual practitioners who never carried an institutional enrollment. Every count resolves to a specific row in a specific frozen snapshot; the exact SQL is in the reproducibility block below. Methodology version: distress-proxy/v1. The source-provenance contract is documented in the provenance methodology.

Limitations

• A coarse, public-data distress proxy. "Distressed" is built entirely from HCRIS cost reports and is meant to be reproducible, not authoritative. It is not an audit, credit rating, or solvency opinion, and it over-flags: thin-margin operators frequently never cross into misconduct.
• A trailing record. An exclusion lands long after the conduct that prompted it, and the most recent months are depressed by reporting lag. The study reads the list as a cohort signal, not a real-time one.
• Partial match rate. Only 63% of new exclusions matched a PECOS enrollment; the clustering finding is stated over matched institutional exclusions, not the whole list.
• Aggregate only. Every figure is a count or ratio at the file, year, or institutional/distress-status level. No individual provider is named, ranked, or scored.
• Corrected framing (2026-06-01). The original publication framed exclusions as rising; that figure was not reproducible from the committed snapshot and was corrected to the flat-to-declining series. The append-only corrigendum is in the reproducibility block.

Sources

• HHS-OIG — List of Excluded Individuals and Entities (LEIE) — the federal exclusion registry behind every count in this study.
• CMS — Medicare provider enrollment (PECOS) — the enrollment records joined to each exclusion by NPI.
• CMS HCRIS Hospital Cost Reports — the cost-report source for the financial-distress proxy.

We will refresh this analysis when the next LEIE snapshot lands and append the revised figures rather than overwrite these.