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CARE QUALITY · ISSUE 048
cms-nursing-home-compareOriginal Research

Nursing Home Survey Results: How Fast Deficiencies Get Fixed

Across 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.

BY FONTEUM LLC · JUNE 4, 2026 · 14 MIN READREVIEWED BY DR. JENNIFER MONTECILLO, MDSNAPSHOT 2026-05-25 · LAST UPDATED JUNE 4, 2026
CMS Nursing Home Compare · 2026-05-25
Mean correction time by CMS severity levelcms-nursing-home-compare · 2026-05-25
A–C
32.4
D–F
32.6
G–I
30
J–L
26.2
Built on CMS Nursing Home Compare · snapshot 2026-05-25 · reproducible · re-derive the figures yourself
Key findings
days mean correction time across all severities (410,723 corrected citations with a valid span)
cms-nursing-home-compare · CMS
days mean time-to-correction for harm-level Severity G+ citations (20,297 citations)
cms-nursing-home-compare · CMS
days — immediate-jeopardy citations close fastest of all severity bands
cms-nursing-home-compare · CMS
days — mean correction time ranges from 16.5 in Texas (fastest) to 64.5 in Washington, D.C. (slowest), a near four-fold spread across states
cms-nursing-home-compare · CMS

When a federal surveyor walks into a nursing home and writes a deficiency, a clock starts. The facility has to submit a plan of correction, fix the underlying problem, and document the date the correction was complete. That correction date is recorded in the same public file that lists the citation itself. Lay the two dates side by side across every citation in the country and you get something the star rating never shows you: not whether a facility was cited, but how long it took to make the problem go away.

We measured that lead time for every corrected health deficiency in the CMS Care Compare Nursing Home Health Deficiencies file — 410,723 citations that carry a survey date, a documented correction date, and a valid non-negative correction span, drawn from a snapshot frozen on 25 May 2026. The headline is simple. Across all severities, the mean correction time is 32.4 days. For the citations that matter most — the harm-level findings at CMS Severity G and above — it is 28.5 days, with a median of 26. And the single most dangerous category, immediate jeopardy, is corrected fastest of all, in 26 days on average.

The 55-word version. Across 410,723 corrected CMS nursing home health deficiencies (snapshot May 2026), the mean correction time is 32.4 days. Harm-level violations — CMS Severity G and above — close faster, in 28.5 days on average, with a median of 26. The most severe immediate-jeopardy citations close fastest, at 26 days. Texas and Illinois correct quickest; Washington, D.C. slowest.

The severity inversion

The intuitive story is that harder problems take longer to fix. A leaking roof takes longer than a missing sign; a systemic infection-control failure takes longer than a single mislabeled medication cart. So you would expect the most severe deficiencies — the ones CMS scores as causing actual harm or immediate jeopardy — to sit open the longest. The data says the opposite.

Sort the four CMS harm bands by mean correction time and they line up almost perfectly in reverse order of severity. No-harm citations (Severity A–C) take 32.4 days on average. Minimal-harm citations (D–F), the overwhelming bulk of the file at 380,846 corrected rows, take 32.6 days. Actual-harm citations (G–I) drop to 30.0 days. And immediate-jeopardy citations (J–L) — the findings CMS defines as posing an immediate threat to resident life or safety — close in 26.2 days, more than six days faster than the least serious band.

This is not a quirk of a few outliers. The pattern holds at the median (30 days for the two least-severe bands, 27 for actual harm, 25 for immediate jeopardy) and it holds across more than twenty thousand harm-level citations. The inversion is real, and the explanation is regulatory, not clinical.

Under the federal enforcement framework at 42 CFR Part 488, a facility cited for immediate jeopardy is given almost no slack. Survey teams flag immediate jeopardy in real time, and the facility must remove the jeopardy — bring the situation below the immediate-threat threshold — on a compressed timeline, typically measured in days, or face the most severe remedies CMS can impose: denial of payment for new admissions, civil money penalties that accrue daily, and ultimately termination from Medicare and Medicaid. The clock on a J, K, or L citation is short by design. A facility that lets one sit open is not risking a slow administrative letter; it is risking its participation in the programs that pay most of its bills.

The least-severe citations carry no such pressure. A Severity D finding — the most common single category in the entire file — is an isolated deficiency with the potential for, but no actual, more-than-minimal harm. The plan-of-correction process still applies, but the consequence of a longer correction window is mild. So the least urgent findings drift, and the most urgent ones are closed under the threat of the heaviest penalties in the federal toolkit. The result is the inversion you see in the table below.

The pattern runs against intuition. The deficiencies that put residents in immediate jeopardy are corrected fastest of all — not because they are easier to fix, but because federal rules give the facility almost no time to leave them open.

Correction time by harm band

The figures below are computed from the corrected-citation universe: every row with both a survey date and a documented correction date, with negative spans (a CMS data artifact discussed in the limitations) removed. p90 is the 90th percentile — the value below which nine in ten corrections land.

Harm bandCMS codesCorrected citationsMean daysMedian daysp90 days
No harmA–C9,58032.43051
Minimal harmD–F380,84632.63052
Actual harmG–I12,22330.02754
Immediate jeopardyJ–L8,07426.22551
Harm-level (G+)G–L20,29728.52653
All correctedA–L410,72332.43052

A few things are worth drawing out. First, the harm-level row — actual harm plus immediate jeopardy combined — is the study's anchor: 20,297 corrected citations where a surveyor documented real harm to a resident, closed in a mean of 28.5 days. That is the number to remember. Second, the p90 column tells a quieter story than the means. Even though immediate-jeopardy citations have the lowest mean, their p90 (51 days) is close to every other band. The fast average is driven by the bulk of cases being closed quickly under regulatory pressure; the slow tail still exists. Roughly one in ten harm-level citations stays open for more than 53 days. Third, the actual-harm band (G–I) has the highest p90 of any band at 54 days — these are serious findings that did not rise to immediate jeopardy, and without the compressed jeopardy clock, their long tail runs the longest of all.

The all-corrected mean of 32.4 days and median of 30 days are the right summary statistics for the file as a whole, and they are dominated by the minimal-harm band simply because that band is 93 percent of the corrected universe. When a facility, a journalist, or a regulator talks about how long it takes nursing homes to fix problems, the honest single number depends on which problems they mean. For everything, it is about a month. For the harmful findings specifically, it is closer to four weeks — and falling as severity rises.

State by state: a four-fold spread

Correction time is not evenly distributed across the country. Limiting the ranking to states with at least 500 corrected citations (to keep small-sample territories from dominating the extremes), the mean correction time ranges from 16.5 days in Texas to 64.5 days in Washington, D.C. — a near four-fold spread, and a three-to-one gap even between the second-fastest and second-slowest jurisdictions (51.9 days in New York versus 16.8 in Illinois).

The ten fastest correctors (mean days, all corrected citations):

RankStateCorrected citationsMean daysMedian days
1Texas30,43216.516
2Illinois24,95616.815
3Iowa7,99623.723
4Rhode Island2,06525.428
5Indiana11,74326.525
6California57,62927.125
7Arkansas4,33427.729
8South Carolina2,35928.228
9Wisconsin8,70128.228
10North Carolina7,89128.227

The ten slowest correctors:

RankStateCorrected citationsMean daysMedian days
1Washington, D.C.1,00964.562
2New York11,59651.954
3Maryland11,09149.545
4Utah2,64446.646
5Delaware1,64446.546
6Virginia10,85544.841
7New Mexico3,28544.542
8Arizona3,18944.443
9Georgia5,87943.645
10Alaska51142.745

Texas and Illinois are in a category of their own. Both close more than 24,000 citations apiece, so this is not a small-sample mirage — these are two of the largest long-term-care markets in the country, each resolving the typical deficiency in about two and a half weeks. California, the single largest market in the file at 57,629 corrected citations, sits sixth at 27.1 days. At the other end, Washington, D.C. takes nine weeks on average and New York close to eight.

It is tempting to read fast correction as good and slow correction as bad, but the metric does not support that reading on its own. A correction date is the date the facility documents that it fixed the problem; it is a measure of administrative throughput and regulatory cadence, not of care quality. State survey agencies differ in how they schedule revisits, how they accept plans of correction, and how they record completion. A state that requires an on-site revisit before recording a correction will mechanically show longer lead times than one that accepts documented self-attestation. The spread is real and worth attention, but it measures the speed of the correction process, not the goodness of the underlying care.

The Illinois paradox

The state-level results become genuinely interesting when read alongside our earlier work on the same dataset. In our companion analysis of nursing-home harm rates, Illinois had the highest rate of harm-level (Severity G+) citations per facility cited of any state — 4.57, against a national pattern far below it. Yet here, Illinois corrects faster than every state but Texas, closing the typical deficiency in 16.8 days.

High citation intensity and fast correction are not a contradiction; together they describe a particular enforcement posture. Illinois runs an aggressive survey regime that documents a large volume of serious findings, and it pairs that with a correction process that closes them quickly. New York shows the mirror image on the correction axis — a large market that takes nearly eight weeks to close the average citation. Neither number, on its own, tells you whether a resident in Chicago or Buffalo is safer. What the two studies together demonstrate is that the federal data, frozen and joined, can separate enforcement intensity from enforcement speed — two things that the public star rating blends into a single, lagging summary.

Why this is not in the star rating

The Five-Star Quality Rating that consumers see on Care Compare folds health inspections, staffing, and clinical quality measures into one number. The health inspection component is built from the weighted count and severity of deficiencies over the most recent survey cycles. It is, by construction, a backward-looking measure of how many problems a facility had and how bad they were. It says nothing about how quickly the facility responded.

Correction speed is a different signal entirely. It is the operational reflex of a facility under regulatory pressure — the gap between being told something is wrong and documenting that it is fixed. Two facilities with identical citation records can behave completely differently here: one closes its harm-level findings in two weeks, the other lets them sit for two months. The star rating treats them the same. The correction-date field, sitting unused in the same public file, distinguishes them cleanly.

This is the recurring theme of our care-quality desk. The federal data carries operational signals that the consumer-facing ratings discard. We found the same thing when we partitioned the SNF quality decline by ownership change in our analysis of the Q1 2026 quality drop, and again in the timing of provider deactivations. The ratings are a trailing summary; the underlying files are a daily record. When the two disagree, the underlying file leads.

Methodology

The analysis aggregates the CMS Care Compare Nursing Home Health Deficiencies file (table nh_health_deficiencies), a frozen snapshot taken on 25 May 2026. The file contains 418,148 citations across 14,635 nursing homes, with survey dates ranging from March 2017 to March 2026.

The correction lead time for a citation is correction_date − survey_date, expressed in whole days. Of the 418,148 citations, 415,849 carry both a survey date and a correction date; the remaining 2,299 have no correction date recorded and are treated as open and excluded. A further 5,126 citations record a correction date earlier than the survey date — a data-entry artifact in the source file — and are excluded outright rather than clamped to zero, leaving 410,723 citations with a valid non-negative span.

Severity bands follow the CMS scope-and-severity scale: no harm (A–C), minimal harm (D–F), actual harm (G–I), and immediate jeopardy (J–L). "Harm-level" throughout this study means Severity G and above (G–L). For each band we report the count, the arithmetic mean, the median (50th percentile), and the 90th percentile, computed with continuous-percentile interpolation so the figures reconcile with the percentile_cont aggregate in the published query. State rankings use the same correction-time definition, restricted to states with at least 500 corrected citations.

The exact query is reproduced below and embedded in the reproducibility block at the foot of this page. It runs unmodified against the nh_health_deficiencies table and returns the five result sets behind every figure in this study — the harm-band table, the harm-level headline, the state ranking, the all-citation percentiles, and the file reconciliation.

-- Correction time by CMS harm band (no harm A-C, minimal D-F,
-- actual harm G-I, immediate jeopardy J-L). Open citations and
-- negative spans excluded.
with corrected as (
  select
    (correction_date - survey_date) as days,
    upper(left(scope_severity_code, 1)) as sev
  from nh_health_deficiencies
  where survey_date is not null
    and correction_date is not null
    and (correction_date - survey_date) >= 0
)
select
  case
    when sev in ('A','B','C') then '1 no harm (A-C)'
    when sev in ('D','E','F') then '2 minimal harm (D-F)'
    when sev in ('G','H','I') then '3 actual harm (G-I)'
    when sev in ('J','K','L') then '4 immediate jeopardy (J-L)'
  end as harm_band,
  count(*) as n,
  round(avg(days)::numeric, 2) as mean_days,
  round(percentile_cont(0.5) within group (order by days)::numeric, 1) as median_days,
  round(percentile_cont(0.9) within group (order by days)::numeric, 1) as p90_days
from corrected
where sev in ('A','B','C','D','E','F','G','H','I','J','K','L')
group by harm_band
order by harm_band;

The computation is also available as a typed, unit-tested helper in the Fonteum codebase (src/lib/research/time-to-correction.ts), which classifies severity, excludes invalid and negative spans, and computes the same mean/median/p90 from plain records — so the published figures can be reproduced from the raw rows without re-deriving the SQL.

Limitations

  • A correction date is an administrative event, not a clinical outcome. It records when a facility documented that a deficiency was corrected. It does not measure whether the correction held, whether residents were harmed in the interim, or whether the underlying problem recurred.
  • Open citations are excluded. The 2,299 citations with no recorded correction date — the still-open findings — are not in any average here. If the slowest corrections are disproportionately still open at snapshot time, the true correction times are slightly understated.
  • Negative spans are excluded, not corrected. 5,126 rows record a correction date before the survey date. These are source data-entry artifacts. We drop them rather than guess at the intended dates.
  • State differences reflect process, not only performance. Survey agencies differ in revisit scheduling and in how completion is recorded. A longer mean in one state can reflect a stricter revisit requirement rather than slower remediation.
  • Severity is CMS's classification, not ours. Scope-and-severity codes are assigned by CMS surveyors. We group and count them; we do not re-adjudicate any citation's severity.
  • No facility-level claims. This study reports national and state aggregates only. It does not rank, rate, endorse, or warn against any individual facility, and it attaches no citation to any provider profile.
  • Snapshot, not live. Figures reflect the 25 May 2026 snapshot. CMS updates the source file on a recurring cadence, and the numbers will move with each release.

Sources and provenance

The underlying data is a U.S. Government Work in the public domain. Every figure in this study traces to the frozen snapshot recorded in the reproducibility block below, whose SHA-256 digest is published so the result set can be checked byte-for-byte. Fonteum reports CMS-published enforcement records; it does not independently inspect or rate any facility.

Related Fonteum research


Reviewed by Jennifer Montecillo, MD, medical reviewer. Non-practicing medical reviewer.

Frequently asked questions

How fast do nursing homes fix cited deficiencies?
Across 410,723 corrected CMS nursing-home health deficiencies, the mean time from survey to documented correction is 32.4 days. Harm-level citations (Severity G and above) close faster — 28.5 days on average, with a median of 26.
Do more severe citations get fixed faster?
Yes, counter to intuition. Immediate-jeopardy citations (Severity J–L) are corrected fastest of any band, in 26.2 days on average, because federal rules give facilities almost no time to leave them open. Minimal-harm citations — about 93% of the corrected universe — take the longest, at 32.6 days.
Which states correct fastest and slowest?
Among states with at least 500 corrected citations, the mean correction time ranges from 16.5 days in Texas and 16.8 in Illinois to 64.5 days in Washington, D.C. and 51.9 in New York — a near four-fold spread.
How many citations does this cover?
The snapshot holds 418,148 health deficiencies; 415,849 carry both a survey and a correction date. After excluding 2,299 still-open citations and 5,126 with a correction date before the survey date, 410,723 have a valid non-negative span.
Is a faster correction always better?
Not necessarily. This measures the documented administrative gap between the survey and the recorded correction, not clinical outcome quality. It is a descriptive measure of process speed, not a judgment of care.

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.

Reproducibility

Every claim, reproducible

The SQL
nursing-home-deficiency-correction-time.sql
-- Time-to-Correction: CMS Nursing Home Health Deficiencies (2026).
-- Source table: nh_health_deficiencies (CMS Care Compare NH Health
-- Deficiencies; snapshot frozen 2026-05-25; US-Government-Works).
--
-- Metric: correction lead time = correction_date - survey_date, in whole days.
-- Rows excluded: open citations (correction_date IS NULL, n=2,299) and
-- negative spans where correction_date predates survey_date (n=5,126, a CMS
-- data quirk — excluded outright, never clamped to zero). 410,723 of the
-- 415,849 corrected citations carry a valid non-negative span.
--
-- CMS scope/severity bands: no harm A-C, minimal harm D-F, actual harm G-I,
-- immediate jeopardy J-L. "Harm-level" = G+ (G-L).

-- Audit observation: 2026-07-14. Source snapshot remains 2026-05-25.
-- audit-claim: harm-band-vector
with severity_bands(sev, harm_band) as (
  values
    ('A', '1 no harm (A-C)'), ('B', '1 no harm (A-C)'), ('C', '1 no harm (A-C)'),
    ('D', '2 minimal harm (D-F)'), ('E', '2 minimal harm (D-F)'), ('F', '2 minimal harm (D-F)'),
    ('G', '3 actual harm (G-I)'), ('H', '3 actual harm (G-I)'), ('I', '3 actual harm (G-I)'),
    ('J', '4 immediate jeopardy (J-L)'), ('K', '4 immediate jeopardy (J-L)'), ('L', '4 immediate jeopardy (J-L)')
), corrected as (
  select
    (correction_date - survey_date) as days,
    upper(left(scope_severity_code, 1)) as sev
  from nh_health_deficiencies
  where survey_date is not null
    and correction_date is not null
    and (correction_date - survey_date) >= 0
)
select
  severity_bands.harm_band,
  count(*) as n,
  round(avg(days)::numeric, 2) as mean_days,
  round(percentile_cont(0.5) within group (order by days)::numeric, 1) as median_days,
  round(percentile_cont(0.9) within group (order by days)::numeric, 1) as p90_days
from corrected
join severity_bands using (sev)
group by severity_bands.harm_band
order by severity_bands.harm_band;

-- audit-claim: harm-level-summary
--    Expected: n=20,297 · mean 28.48 · median 26 · p90 53.
select
  count(*) as harm_level_n,
  round(avg(correction_date - survey_date)::numeric, 2) as mean_days,
  round(percentile_cont(0.5) within group (order by (correction_date - survey_date))::numeric, 1) as median_days,
  round(percentile_cont(0.9) within group (order by (correction_date - survey_date))::numeric, 1) as p90_days
from nh_health_deficiencies
where survey_date is not null
  and correction_date is not null
  and (correction_date - survey_date) >= 0
  and upper(left(scope_severity_code, 1)) in ('G','H','I','J','K','L');

-- audit-claim: state-correction-vector
select
  state,
  count(*) as n,
  round(avg(correction_date - survey_date)::numeric, 2) as mean_days,
  round(percentile_cont(0.5) within group (order by (correction_date - survey_date))::numeric, 1) as median_days
from nh_health_deficiencies
where survey_date is not null
  and correction_date is not null
  and (correction_date - survey_date) >= 0
  and state is not null
  and length(state) = 2
group by state
having count(*) >= 500
order by mean_days asc;

-- audit-claim: all-corrected-summary
select
  count(*) as valid_corrected,
  round(avg(correction_date - survey_date)::numeric, 2) as mean_days,
  round(percentile_cont(0.5) within group (order by (correction_date - survey_date))::numeric, 1) as median_days,
  round(percentile_cont(0.9) within group (order by (correction_date - survey_date))::numeric, 1) as p90_days
from nh_health_deficiencies
where survey_date is not null
  and correction_date is not null
  and (correction_date - survey_date) >= 0;

-- audit-claim: file-reconciliation
select
  count(*) as total_citations,
  count(*) filter (where survey_date is not null and correction_date is not null) as both_dates,
  count(*) filter (where correction_date is null) as open_without_correction,
  count(*) filter (
    where survey_date is not null
      and correction_date is not null
      and correction_date < survey_date
  ) as negative_spans,
  count(*) filter (
    where survey_date is not null
      and correction_date is not null
      and correction_date >= survey_date
  ) as valid_nonnegative_spans,
  count(distinct ccn) as facilities,
  min(survey_date) as earliest_survey_date,
  max(survey_date) as latest_survey_date
from nh_health_deficiencies;
The snapshot
dataset_idcms-nursing-home-compare
snapshot_date2026-05-25
sha2565ec5cfb8d6658b98ec3aa7f84ccec9920a403a29055bbb1ca0a8b124382730f2
The JOINs
Single-table aggregate on nh_health_deficiencies — no cross-source join.
correction lead time = correction_date − survey_date (whole days).
Excluded: correction_date IS NULL (2,299 open citations); negative spans where correction_date predates survey_date (5,126 rows).
The pipeline version
git_sha48d4a3b
methodology_versionnh-correction-time/v1

Reproduce this

Run the exact query against the frozen 2026-05-25.

-- Time-to-Correction: CMS Nursing Home Health Deficiencies (2026). -- Source table: nh_health_deficiencies (CMS Care Compare NH Health -- Deficiencies; snapshot frozen 2026-05-25; US-Government-Works). -- -- Metric: correction lead time = correction_date - survey_date, in whole days. -- Rows excluded: open citations (correction_date IS NULL, n=2,299) and -- negative spans where correction_date predates survey_date (n=5,126, a CMS -- data quirk — excluded outright, never clamped to zero). 410,723 of the -- 415,849 corrected citations carry a valid non-negative span. -- -- CMS scope/severity bands: no harm A-C, minimal harm D-F, actual harm G-I, -- immediate jeopardy J-L. "Harm-level" = G+ (G-L). -- Audit observation: 2026-07-14. Source snapshot remains 2026-05-25. -- audit-claim: harm-band-vector with severity_bands(sev, harm_band) as ( values ('A', '1 no harm (A-C)'), ('B', '1 no harm (A-C)'), ('C', '1 no harm (A-C)'), ('D', '2 minimal harm (D-F)'), ('E', '2 minimal harm (D-F)'), ('F', '2 minimal harm (D-F)'), ('G', '3 actual harm (G-I)'), ('H', '3 actual harm (G-I)'), ('I', '3 actual harm (G-I)'), ('J', '4 immediate jeopardy (J-L)'), ('K', '4 immediate jeopardy (J-L)'), ('L', '4 immediate jeopardy (J-L)') ), corrected as ( select (correction_date - survey_date) as days, upper(left(scope_severity_code, 1)) as sev from nh_health_deficiencies where survey_date is not null and correction_date is not null and (correction_date - survey_date) >= 0 ) select severity_bands.harm_band, count(*) as n, round(avg(days)::numeric, 2) as mean_days, round(percentile_cont(0.5) within group (order by days)::numeric, 1) as median_days, round(percentile_cont(0.9) within group (order by days)::numeric, 1) as p90_days from corrected join severity_bands using (sev) group by severity_bands.harm_band order by severity_bands.harm_band; -- audit-claim: harm-level-summary -- Expected: n=20,297 · mean 28.48 · median 26 · p90 53. select count(*) as harm_level_n, round(avg(correction_date - survey_date)::numeric, 2) as mean_days, round(percentile_cont(0.5) within group (order by (correction_date - survey_date))::numeric, 1) as median_days, round(percentile_cont(0.9) within group (order by (correction_date - survey_date))::numeric, 1) as p90_days from nh_health_deficiencies where survey_date is not null and correction_date is not null and (correction_date - survey_date) >= 0 and upper(left(scope_severity_code, 1)) in ('G','H','I','J','K','L'); -- audit-claim: state-correction-vector select state, count(*) as n, round(avg(correction_date - survey_date)::numeric, 2) as mean_days, round(percentile_cont(0.5) within group (order by (correction_date - survey_date))::numeric, 1) as median_days from nh_health_deficiencies where survey_date is not null and correction_date is not null and (correction_date - survey_date) >= 0 and state is not null and length(state) = 2 group by state having count(*) >= 500 order by mean_days asc; -- audit-claim: all-corrected-summary select count(*) as valid_corrected, round(avg(correction_date - survey_date)::numeric, 2) as mean_days, round(percentile_cont(0.5) within group (order by (correction_date - survey_date))::numeric, 1) as median_days, round(percentile_cont(0.9) within group (order by (correction_date - survey_date))::numeric, 1) as p90_days from nh_health_deficiencies where survey_date is not null and correction_date is not null and (correction_date - survey_date) >= 0; -- audit-claim: file-reconciliation select count(*) as total_citations, count(*) filter (where survey_date is not null and correction_date is not null) as both_dates, count(*) filter (where correction_date is null) as open_without_correction, count(*) filter ( where survey_date is not null and correction_date is not null and correction_date < survey_date ) as negative_spans, count(*) filter ( where survey_date is not null and correction_date is not null and correction_date >= survey_date ) as valid_nonnegative_spans, count(distinct ccn) as facilities, min(survey_date) as earliest_survey_date, max(survey_date) as latest_survey_date from nh_health_deficiencies;

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Cite this study

Citation-ready for researchers and AI.

Fonteum Research Bureau (2026). Nursing Home Survey Results: How Fast Deficiencies Get Fixed. CMS Nursing Home Compare, snapshot 2026-05-25. https://fonteum.com/research/nursing-home-deficiency-correction-time

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.

1
Snapshot
cms-nursing-home-compare · 2026-05-25
2
Supplied file hash
SHA-256 5ec5cfb8…30f2
3
Snapshot attestation
Inspect supplied artifact
Figures are not individually signed · check it in Attest →

Federal source citations

  1. CMS Nursing Home Compare · snapshot 2026-05-25 · federal source family · US-Government-Works

Fonteum LLC · June 4, 2026 · All figures trace to the frozen federal-data snapshot cited above.

What’s on file, by the numbers

Platform snapshot · 2026-07-19

13.4Mproviders & companiesProviders, organizations, owners, and facilities on file
26.2Msource-linked factsSource-linked field facts in the dated platform snapshot
86sources with dataDistinct snapshot source IDs with at least one positive record count
75fresh sourcesDistinct source IDs whose latest positive-data snapshot falls within the preceding 45 days
111sources integratedActive registry rows; integration does not establish a load
13state Medicaid jurisdictionsDistinct states represented in the state-exclusions serving table

Integrated, with-data, and fresh-observation counts are separate. No platform-wide source-completeness count is published. Completeness is source-specific and must be evaluated against the named source's expected scope. State coverage is a separate jurisdiction measure.

Source authority is record-specific

Use the issuer named on the record.

Fonteum spans federal, state, and global public publishers. A source page or returned record identifies its issuer and dataset where that metadata is available. A platform registry count does not assign every page to one authority or establish loaded, fresh, or complete coverage.

Browse source records and their stated limitations →

Reproducible by design

Inspect the evidence each published figure actually supplies.

Source and date

Research pages expose the named public file and observation date where those fields are available. Source-file SHA-256 coverage is separate; facts do not currently link deterministically to signatures.

Available derivation

Studies with a retained release and committed derivation link the SQL or method used. Other studies state the evidence and reproduction limits they actually have.

Daily observations

Dated table row-count observations can detect local drift. They do not imply that an upstream publisher released or Fonteum ingested new data that day.

Named medical review

Reviewed by Jennifer Montecillo, MD, medical reviewer. Non-practicing medical reviewer.

Read the full provenance and attestation methodology →

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