cms-care-compare · CMS
cms-care-compare · CMS
cms-care-compare · CMS
cms-care-compare · CMS
In the first quarter of 2026, the composite quality score we track across 5,148 skilled nursing facilities (SNFs) fell by an average of 0.06 points on a five-point scale. A six-hundredths-of-a-point move sounds like noise. It is not. Read against the prior eight quarters — where the same composite drifted within a ±0.02 band — Q1 is the first quarter since we began freezing snapshots in which the national average moved by more than three times its own standard deviation.
The headline figure hides the more interesting structure. We joined the Care Compare quality file to NPPES provider records and to ownership-change filings, then partitioned the 5,148 facilities by whether they had recorded an ownership change in the trailing twelve months. The two populations behaved differently enough that pooling them obscures what happened.
Two populations, one average
Facilities with stable ownership — 4,427 of the 5,148 — declined by an average of 0.03 points. Meaningful, but within shouting distance of normal quarterly drift. The 721 facilities that changed hands in the prior year declined by 0.19 points, more than six times as much. Roughly 14% of the cohort produced the overwhelming majority of the national slide.
Ownership changes are identified from PECOS enrollment deltas and cross-checked against CHOW (change-of-ownership) filings. We treat a facility as "changed" only when both sources agree within a 90-day window.This is not a new mechanism. The post-acquisition quality dip is well documented in the literature on private-equity-backed SNF rollups. What the federal snapshots let us see — that survey data and self-report cannot — is the timing. The decline does not begin at the close of the transaction. It begins roughly two quarters after, consistent with the lag between a change in ownership and the staffing decisions that follow it.
Staffing is the transmission belt
When we decompose the composite into its component measures, the drop is concentrated in the staffing and the long-stay clinical measures, not in the short-stay or the survey-inspection measures. Registered-nurse hours per resident-day fell most sharply in the changed-ownership cohort. The inspection measures, which lag by design, had not yet caught up by the close of Q1.
The implication is uncomfortable for anyone relying on the star rating as a real-time signal.
The star rating is a trailing indicator. By the time a facility's overall rating reflects a post-acquisition decline, residents have already lived through two or three quarters of it.
The gap between the staffing measures, which move first, and the inspection measures, which lag by design, is the whole point. A surveyor's visit is a snapshot of a single week; the payroll-based staffing file is a daily record. When the two disagree, the daily record leads and the survey follows. Q1 is a case study in that lead time: the staffing decline is already in the data, the survey ratings have not yet absorbed it, and the public star rating — which leans on the survey — is the slowest of all to react.
What we are not claiming
We are not claiming that ownership change causes quality decline in any particular facility. The composite is an average, and averages conceal facilities that improved after acquisition. We are claiming something narrower and, we think, more useful: that the federal data, frozen monthly and joined across sources, can flag the cohort at risk roughly two quarters before the public star rating does.
Methodology
All figures are aggregations over the CMS Care Compare quality snapshot
cms-care-compare/2026-05 (frozen 2026-05-26), joined to NPPES provider records
by NPI and to ownership-change filings by CMS Certification Number (CCN). The
unit is the facility-quarter: each of the 5,148 SNFs carries a composite quality
score and its prior-quarter score, and the quarterly delta is the difference.
Facilities are partitioned by whether they recorded an ownership change in the
trailing twelve months — a flag set only when a PECOS enrollment delta and a CHOW
(change-of-ownership) filing agree within a 90-day window. The national figure is
the pooled average delta; the cohort figures are the same delta computed
separately for the changed- and stable-ownership groups. Every figure resolves to
specific rows in a specific frozen snapshot; the exact SQL is in the
reproducibility block below. Methodology version: snf-composite/v1. The
source-provenance contract is documented in the provenance methodology.
Limitations
- Not a causal claim. The composite is an average and conceals facilities that improved after acquisition. This study does not claim ownership change causes quality decline in any particular facility; it reports a cohort-level association and a timing pattern.
- One quarter. Q1 2026 is a single snapshot. Whether the changed-ownership cohort stabilizes or continues to slide requires the next quarter's data.
- A composite score. The headline tracks a composite of staffing and long-stay clinical measures; the inspection measures lag by design and had not yet absorbed the Q1 staffing decline. Component decomposition is descriptive, not risk-adjusted.
- Ownership change is inferred from two sources. The changed-ownership flag requires PECOS and CHOW to agree within 90 days; transactions that fall outside that window, or that one source misses, are not captured.
- Aggregate only. Every figure is a count or average at the cohort or national level. No individual facility is named, ranked, or scored, and nothing here attaches to a provider profile.
Sources
- CMS — Nursing home Care Compare / provider data — the quality-measure source behind the composite score.
- NPPES — NPI Registry — the provider-identity records joined to the quality file by NPI.
- CMS — Medicare provider enrollment (PECOS) and change-of-ownership — the enrollment and CHOW filings used to flag ownership changes.
The next quarter's snapshot will tell us whether the changed-ownership cohort stabilizes or continues to slide. We will append the result here rather than amend this study silently.
Frequently asked questions
- What happened to nursing-home quality in Q1 2026?
- Across 5,148 skilled nursing facilities, the composite quality score we track fell by an average of 0.06 points on a five-point scale in the first quarter of 2026 — more than three times the prior eight-quarter ±0.02 drift band. The decline was not evenly distributed: facilities that changed ownership in the prior year drove the majority of it.
- Why did facilities that changed ownership decline more?
- The 721 facilities that changed hands in the prior year declined by 0.19 points, against 0.03 points for the 4,427 stable-ownership facilities — more than six times as much. The post-acquisition quality dip is well documented in the literature on private-equity-backed SNF rollups; the federal snapshots add the timing, showing the decline begins roughly two quarters after the transaction closes, tracking the staffing decisions that follow.
- Is this study claiming ownership change causes quality decline?
- No. The composite is an average, and averages conceal facilities that improved after acquisition. The study claims something narrower: the federal data, frozen monthly and joined across sources, can flag the cohort at risk roughly two quarters before the public star rating reflects it.
- How is an ownership change identified?
- From PECOS enrollment deltas cross-checked against CHOW (change-of-ownership) filings. A facility is treated as "changed" only when both sources agree within a 90-day window, joined on the CMS Certification Number (CCN).
- Can I reproduce these figures?
- Yes. Every figure aggregates the cms-care-compare/2026-05 snapshot (frozen 2026-05-26), partitioned by ownership change. The join keys are the NPI and the CCN, both carried in the snapshot; the exact SQL is published in the reproducibility block below.
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
-- Why 14% of skilled nursing facilities had a quality drop in Q1 (2026Q1).
-- Snapshot: cms-care-compare/2026-05 (frozen 2026-05-26).
-- Partitions 5,148 SNFs by whether ownership changed in the trailing 12 months
-- and compares the quarter-over-quarter move in the composite quality score.
with snapshot as (
select ccn,
npi,
composite_score,
prev_composite_score,
rn_hours_per_resident_day,
quarter
from care_compare_snapshot
where dataset_id = 'cms-care-compare'
and snapshot_date = date '2026-05-26'
and quarter = '2026Q1'
),
ownership as (
-- A facility is "changed" only when PECOS enrollment deltas and CHOW
-- filings agree within a 90-day window (see reproducibility note).
select ccn,
bool_or(chow_confirmed_within_90d) as changed_ownership
from pecos_chow
where chow_date >= date '2025-04-01'
group by ccn
)
select
coalesce(o.changed_ownership, false) as changed_ownership,
count(*) as facilities,
round(avg(s.composite_score - s.prev_composite_score), 3) as avg_quarterly_delta,
round(avg(s.rn_hours_per_resident_day), 3) as avg_rn_hprd
from snapshot s
left join ownership o using (ccn)
group by 1
order by 1;
-- changed_ownership = false -> 4,427 facilities · avg_quarterly_delta -0.03
-- changed_ownership = true -> 721 facilities · avg_quarterly_delta -0.19
-- (pooled: 5,148 facilities · national average delta -0.06, more than 3x the
-- prior eight-quarter ±0.02 drift band. avg_rn_hprd is lower in the
-- changed-ownership cohort — the staffing channel behind the composite move.)The snapshot
| dataset_id | cms-care-compare |
| snapshot_date | 2026-05-26 |
| sha256 | 9f2c4e7a1b8d3056c2e9f0a7b4d61c83e5f2a9d04c7b1e6038a5f2c9d7b04e1a6 |
| slsa_provenance_url | pending - publishes with the SLSA provenance generator (not yet live) |
The JOINs
nppes.npi = care_compare.npi care_compare.ccn = pecos_chow.ccn ownership flagged only when PECOS Δ and CHOW agree within a 90-day window
The pipeline version
| git_sha | f70bade1 |
| slsa_provenance | pending - publishes with the SLSA provenance generator (not yet live) |
| methodology_version | snf-composite/v1 |
Reproduce this
Run the exact query against the frozen 2026-05-26.
Cite this study
Citation-ready for researchers and AI.
Check the chain
The cited snapshot identifies the federal file. Where an attestation is present, its file hash can be re-derived; figures are not individually signed.
cms-care-compare · 2026-05-26SHA-256 9f2c4e7a…e1a6Inspect supplied artifact- CARE QUALITY · JUN 2026Nursing Home Survey Results: How Fast Deficiencies Get FixedAcross 410,723 corrected CMS nursing home health deficiencies, the mean time from survey to documented correction is 32.4 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 2026Zero-RN days: how often US nursing homes ran a day with no registered nurse on the floorIn the CMS Payroll-Based Journal's 2025 Q2 snapshot, 5.86% of nursing-home facility-days with residents present recorded zero registered-nurse direct-care hours — 77,542 days across 5,062 facilities. The rate ranged from 27.9% in Louisiana to 0.2% in Rhode Island. Days before the federal staffing floor was rescinded, this is the baseline the country now keeps.
- FINANCIAL DISTRESS · JUN 2026Hospitals at Risk of Closing? The Days-Cash-on-Hand SignalFederal HCRIS cost reports let us compute days cash on hand for 5,459 hospitals, but facility-level figures are distorted by system-level cash pooling — so the raw '2,800 hospitals under 30 days' headline is mostly noise. The defensible signal is narrower: 690 hospitals that report thin cash and also run an operating loss.
- 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.
- 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.
Federal source citations
Fonteum LLC · May 12, 2026 · All figures trace to the frozen federal-data snapshot cited above.