{
  "study": {
    "slug": "hospital-acquired-condition-penalties-2026",
    "title": "The 1% penalty: which hospitals lose Medicare pay for hospital-acquired conditions, FY2026",
    "standfirst": "In CMS's FY2026 Hospital-Acquired Condition Reduction Program, 719 of 3,055 hospitals — the worst-performing quartile by total HAC score — lose 1% of every Medicare payment for the year. The cut is graded on a curve: most hospitals beat the national infection baseline, yet a fixed quartile is penalized regardless.",
    "desk": "care-quality",
    "article_type": "Original Research",
    "published": "2026-06-17",
    "issue": 84,
    "doi": "10.5072/fonteum/hospital-acquired-condition-penalties-2026",
    "url": "https://fonteum.com/research/hospital-acquired-condition-penalties-2026",
    "methodology_version": "cms-hac-reduction-program/v1"
  },
  "data_as_of": "2026-06-03",
  "datasets": [
    {
      "slug": "cms-hospital-compare",
      "name": "CMS Hospital Compare",
      "publisher": "CMS — Hospital quality (Care Compare)",
      "upstream_url": null
    }
  ],
  "key_findings": [
    {
      "number": "719",
      "finding": "of the 3,055 hospitals in CMS's FY2026 Hospital-Acquired Condition Reduction Program — 23.5% — lose 1% of every Medicare fee-for-service payment for the year. By statute the program penalizes the worst-performing quartile by total HAC score, so roughly one in four hospitals is cut every year by design",
      "dataset": "cms-hospital-compare"
    },
    {
      "number": "35.6%",
      "finding": "of hospitals reporting surgical-site infections sit above the national baseline (a standardized infection ratio over 1.0) — the worst of the five tracked infections — against just 4.0% for C. difficile, the best. The single composite penalty hides a tenfold spread in how controlled each harm is",
      "dataset": "cms-hospital-compare"
    },
    {
      "number": "0 of 43",
      "finding": "Maryland hospitals carry a payment reduction — none — because the state's all-payer global-budget waiver exempts its hospitals from the program. Among states with 25 or more hospitals the rate runs from that 0% to 53.3% in Iowa (16 of 30) and 48.0% in West Virginia",
      "dataset": "cms-hospital-compare"
    },
    {
      "number": "1%",
      "finding": "is the flat penalty every cut hospital takes, regardless of how far above or below the worst-quartile line it fell. The program ranks hospitals against each other, so the cut is relative: median scores show most hospitals beating the national baseline (median PSI-90 composite 0.96, median CLABSI ratio 0.56) while a fixed 25% is still penalized",
      "dataset": "cms-hospital-compare"
    },
    {
      "number": "3,055",
      "finding": "hospitals across 51 states and territories make up the FY2026 program file, snapshot dated 2026-06-03. Every figure is a count or percentile over published records — no hospital is named, ranked, or scored, and no inference about care quality or conduct is drawn",
      "dataset": "cms-hospital-compare"
    }
  ],
  "faqs": [
    {
      "q": "What is the Hospital-Acquired Condition Reduction Program?",
      "a": "It is a Medicare pay-for-performance program created by the Affordable Care Act (§3008). Each fiscal year CMS scores acute-care hospitals on a set of patient-safety and healthcare-associated-infection measures, ranks them by a single total HAC score, and cuts 1% from every Medicare fee-for-service payment to the worst-performing quartile. The penalty applies to the whole fiscal year."
    },
    {
      "q": "How many hospitals are penalized, and is that number fixed?",
      "a": "In FY2026, 719 of the 3,055 hospitals in the program — 23.5% — receive the 1% payment reduction. The share is roughly constant year to year because the penalty is defined as the worst-performing quartile by total HAC score, not as a fixed quality threshold. About one in four hospitals is cut every year by the program's own design."
    },
    {
      "q": "Which hospital-acquired infection is the hardest to control?",
      "a": "Surgical-site infections. Among hospitals reporting it, 35.6% sit above the national baseline — a standardized infection ratio greater than 1.0 — the highest of the five tracked infections. C. difficile is the most controlled, with only 4.0% of hospitals above baseline. The single composite HAC score masks this wide variation across harm types."
    },
    {
      "q": "Why are no Maryland hospitals penalized?",
      "a": "Maryland operates under a CMS all-payer global-budget waiver that exempts its hospitals from many Medicare pay-for-performance programs, including the HAC Reduction Program. Maryland hospitals still appear in the published file and report their measures, but 0 of the 43 carry a payment reduction. The exemption is a structural policy artifact, not a quality signal."
    },
    {
      "q": "Does a HAC penalty mean a hospital is unsafe?",
      "a": "No. The penalty is a relative ranking, not an absolute safety verdict. Because hospitals are scored against one another, most penalized hospitals still beat the national infection baseline on the underlying measures — the median PSI-90 composite is 0.96 and the median central-line-infection ratio is 0.56. A hospital can improve its own rates and still be cut if its peers improve faster. This study draws no inference about any hospital's care."
    },
    {
      "q": "Can I reproduce these figures?",
      "a": "Yes. Every number is a direct count or percentile over the public hac_reduction_program table — CMS's Hospital-Acquired Condition Reduction Program file, snapshot dated 2026-06-03 — with no modeling. The exact SQL for the penalty count, the per-infection baseline shares, the median ratios, and the state breakdown is published in the reproducibility block below."
    }
  ],
  "citation": {
    "apa": "Fonteum Research. (2026, June 17). The 1% penalty: which hospitals lose Medicare pay for hospital-acquired conditions, FY2026. Fonteum Research, Issue 84. https://doi.org/10.5072/fonteum/hospital-acquired-condition-penalties-2026",
    "url": "https://fonteum.com/research/hospital-acquired-condition-penalties-2026"
  },
  "reproducible_sql": "-- Which hospitals lose Medicare pay for hospital-acquired conditions, and how\n-- the single composite penalty hides very different infection problems.\n-- Fully reproducible query.\n--\n-- Question: Medicare's Hospital-Acquired Condition (HAC) Reduction Program\n-- (ACA Sec. 3008) cuts 1% of every Medicare fee-for-service payment from the\n-- worst-performing quartile of acute-care hospitals, ranked by a single total\n-- HAC score. For FY2026: how many hospitals are cut, which hospital-acquired\n-- infections are least controlled, and where do penalties land? The lead\n-- figure: 719 of 3,055 hospitals (23.5%) take the 1% reduction. A HAC penalty\n-- is a RELATIVE ranking against the field, NOT an absolute safety verdict or a\n-- statement about any hospital's care.\n--\n-- Source:\n--   public.hac_reduction_program — CMS \"Hospital-Acquired Condition Reduction\n--     Program\" public-use file, published via the CMS provider data catalog as\n--     part of Hospital Care Compare. 3,055 hospitals, fiscal year 2026;\n--     source_release_date 2026-06-03. Public, read-only.\n--     License: US-Government-Works (17 U.S.C. Sec. 105).\n--     methodology_version = 'cms-hac-reduction-program/v1'.\n--\n-- Universe: this study reads the FY2026 program file AS A WHOLE — every row is\n--   one acute-care hospital scored for the fiscal year. The file is the current\n--   program year, not a longitudinal history, so figures are counts/percentiles\n--   within one cycle and are not modeled as a trend.\n--\n-- Measure note: the total HAC score folds in the PSI-90 patient-safety\n--   composite plus five standardized infection ratios (SIRs) from CDC NHSN —\n--   CLABSI, CAUTI, SSI, CDI, MRSA. An SIR > 1.0 means more infections than the\n--   national baseline predicts; < 1.0 means fewer. Each infection is reported\n--   by a different subset of hospitals (small hospitals are exempt below a case\n--   threshold), so each measure carries its own denominator below. The CMS\n--   facility id is a CCN; the CCN-to-entity-graph link is deferred and no\n--   individual hospital is named in the study.\n\n-- ============================================================================\n-- (1) Universe reconciliation — the FY2026 program file at a glance.\n-- ============================================================================\nSELECT\n  count(*)                                                          AS hospitals,\n  count(DISTINCT facility_id)                                       AS distinct_facilities,\n  count(DISTINCT state)                                             AS states,\n  count(*) FILTER (WHERE total_hac_score IS NULL)                   AS no_total_score,\n  min(fiscal_year)                                                  AS fy,\n  max(source_release_date)                                          AS snapshot\nFROM public.hac_reduction_program;\n--  hospitals 3,055 · distinct_facilities 3,055 · states 51 · no_total_score 126\n--  fy 2026 · snapshot 2026-06-03\n\n-- ============================================================================\n-- (2) HEADLINE: how many hospitals are penalized. The 1% cut falls on the\n--     worst-performing quartile by total HAC score, so payment_reduction = 'Yes'\n--     is exactly the in_worst_quartile set (the two fields agree). ~1 in 4\n--     hospitals is cut every year by the program's own design.\n-- ============================================================================\nSELECT\n  payment_reduction,\n  count(*)                                                          AS hospitals,\n  round(100.0 * count(*) / sum(count(*)) OVER (), 1)                AS pct_of_all,\n  count(*) FILTER (WHERE in_worst_quartile)                         AS also_worst_quartile\nFROM public.hac_reduction_program\nGROUP BY payment_reduction\nORDER BY hospitals DESC;\n--  No   2,293  75.1%   (worst_quartile 0)\n--  Yes    719  23.5%   (worst_quartile 719)  <- the 1% penalty cohort\n--  NULL    43   1.4%   (worst_quartile 0)\n\n-- ============================================================================\n-- (3) The composite hides the infections. For each of the five tracked\n--     hospital-acquired infections: how many reporting hospitals sit ABOVE the\n--     national baseline (SIR > 1.0), and the median SIR. Surgical-site\n--     infections are the laggard (35.6% above baseline); C. difficile the best\n--     controlled (4.0%). Every median is below 1.0 — the typical hospital beats\n--     the baseline on every measure, yet a fixed quartile is still penalized.\n-- ============================================================================\nSELECT 'SSI'    AS measure, count(ssi_sir)    AS reporting,\n       round(percentile_cont(0.5) WITHIN GROUP (ORDER BY ssi_sir)::numeric, 3)    AS median_sir,\n       count(*) FILTER (WHERE ssi_sir    > 1) AS above_baseline,\n       round(100.0 * count(*) FILTER (WHERE ssi_sir    > 1) / count(ssi_sir),    1) AS pct_above\n  FROM public.hac_reduction_program\nUNION ALL\nSELECT 'MRSA',  count(mrsa_sir),\n       round(percentile_cont(0.5) WITHIN GROUP (ORDER BY mrsa_sir)::numeric, 3),\n       count(*) FILTER (WHERE mrsa_sir   > 1),\n       round(100.0 * count(*) FILTER (WHERE mrsa_sir   > 1) / count(mrsa_sir),   1)\n  FROM public.hac_reduction_program\nUNION ALL\nSELECT 'CLABSI',count(clabsi_sir),\n       round(percentile_cont(0.5) WITHIN GROUP (ORDER BY clabsi_sir)::numeric, 3),\n       count(*) FILTER (WHERE clabsi_sir > 1),\n       round(100.0 * count(*) FILTER (WHERE clabsi_sir > 1) / count(clabsi_sir), 1)\n  FROM public.hac_reduction_program\nUNION ALL\nSELECT 'CAUTI', count(cauti_sir),\n       round(percentile_cont(0.5) WITHIN GROUP (ORDER BY cauti_sir)::numeric, 3),\n       count(*) FILTER (WHERE cauti_sir  > 1),\n       round(100.0 * count(*) FILTER (WHERE cauti_sir  > 1) / count(cauti_sir),  1)\n  FROM public.hac_reduction_program\nUNION ALL\nSELECT 'CDI',   count(cdi_sir),\n       round(percentile_cont(0.5) WITHIN GROUP (ORDER BY cdi_sir)::numeric, 3),\n       count(*) FILTER (WHERE cdi_sir    > 1),\n       round(100.0 * count(*) FILTER (WHERE cdi_sir    > 1) / count(cdi_sir),    1)\n  FROM public.hac_reduction_program\nORDER BY pct_above DESC;\n--  SSI    reporting 2,270 · median 0.795 · above 809 · 35.6%   <- worst measure\n--  MRSA   reporting 2,068 · median 0.641 · above 476 · 23.0%\n--  CLABSI reporting 2,182 · median 0.559 · above 389 · 17.8%\n--  CAUTI  reporting 2,355 · median 0.489 · above 332 · 14.1%\n--  CDI    reporting 2,767 · median 0.346 · above 111 ·  4.0%   <- best controlled\n\n-- ============================================================================\n-- (4) The penalty is RELATIVE, not absolute. The median PSI-90 patient-safety\n--     composite is at/just under the value the measure treats as expected, yet\n--     23.5% of hospitals are penalized — because the cut targets a fixed\n--     worst-performing quartile, not a quality threshold. A hospital can beat\n--     the baseline and still be cut if peers improve faster.\n-- ============================================================================\nSELECT\n  round(percentile_cont(0.5) WITHIN GROUP (ORDER BY psi_90_composite_value)::numeric, 3) AS median_psi90,\n  count(psi_90_composite_value)                                     AS psi90_reporting,\n  round(percentile_cont(0.5) WITHIN GROUP (ORDER BY clabsi_sir)::numeric, 3) AS median_clabsi_sir\nFROM public.hac_reduction_program;\n--  median_psi90 0.962 · psi90_reporting 2,809 · median_clabsi_sir 0.559\n\n-- ============================================================================\n-- (5) WHERE penalties land — penalty rate by state, restricted to states with\n--     >= 25 hospitals so a single facility cannot swing the rate. Maryland sits\n--     at 0% (its all-payer global-budget waiver exempts its hospitals from the\n--     program); Iowa and West Virginia penalize close to half their hospitals.\n-- ============================================================================\nSELECT\n  state,\n  count(*)                                                          AS hospitals,\n  count(*) FILTER (WHERE payment_reduction ILIKE 'Y%')              AS penalized,\n  round(100.0 * count(*) FILTER (WHERE payment_reduction ILIKE 'Y%')\n        / count(*), 1)                                              AS penalty_rate\nFROM public.hac_reduction_program\nGROUP BY state\nHAVING count(*) >= 25\nORDER BY penalty_rate DESC;\n--  high: IA 53.3% (16/30) · WV 48.0% (12/25) · NM 42.3% (11/26) · OR 40.6% (13/32)\n--        AL 37.7% (29/77) · MA 36.4% (20/55) · NY 34.9% (45/129)\n--  large states: CA 27.4% (76/277) · PA 27.1% (36/133) · TX 11.3% (32/284) · FL 10.7% (18/169)\n--  low:  CT 11.1% (3/27) · NJ 8.2% (5/61) · VA 7.0% (5/71) · UT 3.1% (1/32) · MD 0.0% (0/43)\n\n-- ============================================================================\n-- (6) The Maryland exemption, made explicit. Its hospitals report measures but\n--     carry no payment reduction — a structural policy artifact of the state's\n--     all-payer waiver, NOT a quality result.\n-- ============================================================================\nSELECT\n  count(*)                                                          AS md_hospitals,\n  count(*) FILTER (WHERE payment_reduction ILIKE 'Y%')              AS md_penalized,\n  count(*) FILTER (WHERE total_hac_score IS NOT NULL)               AS md_with_score\nFROM public.hac_reduction_program\nWHERE state = 'MD';\n--  md_hospitals 43 · md_penalized 0 · md_with_score (hospitals still reporting)",
  "license": "U.S. Government Works (federal sources; 17 U.S.C. §105)",
  "generated_by": "Fonteum — https://fonteum.com",
  "notes": "Aggregate, source-traced figures frozen to the snapshot above. Reproduce by running reproducible_sql against the cited federal dataset; no per-entity records are included."
}