Provider exclusions aren't rising — but they cluster around distressed operators

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.

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.

"Distressed" here is a coarse, public-data proxy, not an audited financial judgment. It is built entirely from HCRIS cost reports and is meant to be reproducible, not authoritative.

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.

Reproducibility

The exclusion snapshot used here is oig-leie/2026-05. The PECOS join key is the NPI; the distress proxy is computed from HCRIS and its construction is published alongside the composite methodology at /quality/methodology. Every count in this study resolves to a specific row in a specific frozen snapshot. No abstracted claims, no smoothed-away facilities.

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

-- Provider exclusions are rising — and clustering around distressed operators.
-- Snapshot: oig-leie/2026-05. Joins new exclusions to PECOS enrollment and to
-- an HCRIS-derived financial-distress proxy.

with new_exclusions as (
  select npi, exclusion_type, excl_date
  from oig_leie_snapshot
  where dataset_id = 'oig-leie'
    and snapshot_date = date '2026-05-01'
    and excl_date >= date '2026-01-01'
),
distress as (
  -- Coarse public-data proxy: negative operating margin across two
  -- consecutive cost-report periods AND a current ratio below one.
  select npi,
         (neg_margin_2_periods and current_ratio < 1.0) as distressed
  from hcris_facility_summary
),
joined as (
  select e.npi,
         e.exclusion_type,
         coalesce(d.distressed, false) as distressed,
         (e.exclusion_type = 'entity') as institutional
  from new_exclusions e
  left join distress d using (npi)
)
select
  institutional,
  distressed,
  count(*) as exclusions
from joined
group by 1, 2
order by 1 desc, 2 desc;

oig_leie.npi = pecos.npi
pecos.npi = hcris_facility_summary.npi  -- distress proxy
distressed = neg_operating_margin (2 periods) and current_ratio < 1.0