{ "study": { "slug": "hospital-ownership-changes-2026", "title": "Who buys America's hospitals: a fragmented market with no roll-up, 2026", "standfirst": "Across the 755 hospital ownership-change transactions on CMS's published file, 2016–2025, the ten most active buyers account for just 11.5% of them and 59.9% of all 461 buyers appear exactly once. America's hospitals change hands constantly — but no one is rolling them up.", "desk": "financial-distress", "article_type": "Original Research", "published": "2026-06-17", "issue": 86, "doi": "10.5072/fonteum/hospital-ownership-changes-2026", "url": "https://fonteum.com/research/hospital-ownership-changes-2026", "methodology_version": "cms-hospital-chow/v1" }, "data_as_of": "2026-06-17", "datasets": [ { "slug": "cms-provider-data-catalog", "name": "CMS Provider Data Catalog", "publisher": "CMS — Provider Data Catalog", "upstream_url": null } ], "key_findings": [ { "number": "755", "finding": "hospital ownership-change transactions sit on CMS's published Change of Ownership file, 2016–2025, spread across 461 distinct buyers, 491 distinct sellers, and 742 distinct hospitals. The unit of analysis is the transaction, not the hospital — a hospital can change hands more than once across the period", "dataset": "cms-provider-data-catalog" }, { "number": "11.5%", "finding": "of all 755 changes go to the ten most active buyers combined — just 87 transactions. The single most active buyer accounts for 12 (1.6%), and the average buyer appears 1.64 times. There is no national roll-up visible on this file", "dataset": "cms-provider-data-catalog" }, { "number": "59.9%", "finding": "of the 461 buyers appear exactly once. The 185 repeat buyers — those with two or more changes — drive 63.4% of all transactions, at 2.59 each on average. Consolidation runs through many regional health systems, not a handful of national chains", "dataset": "cms-provider-data-catalog" }, { "number": "85.6%", "finding": "of the changes (646 of 755) are coded as a plain CHANGE OF OWNERSHIP; only 14.4% (109) are coded ACQUISITION/MERGER. Most hospital ownership change on the file is a quiet reassignment of the Medicare billing entity, not a headline merger", "dataset": "cms-provider-data-catalog" }, { "number": "12", "finding": "hospitals — the largest single cluster on the file — move from Dignity Health to Dignity Community Care, all effective the same day in early 2019, an intra-system reassignment rather than an outside purchase. Every figure here is a count at the transaction, buyer, type, year, or state level; no individual is named and no inference about price, motive, or care quality is drawn", "dataset": "cms-provider-data-catalog" } ], "faqs": [ { "q": "What is a hospital change of ownership (CHOW)?", "a": "A CHOW is the formal filing a hospital makes with CMS when the entity that holds its Medicare billing number changes hands. It records a buyer, a seller, an effective date, and a type — either a plain change of ownership or an acquisition/merger. The hospital's Medicare certification (its CCN) carries over to the new owner. The filing marks who is now responsible for the Medicare provider agreement; it is an enrollment record, not a price or a deal valuation." }, { "q": "Who buys the most hospitals in this data?", "a": "No one buys many. The most active single buyer on the file accounts for 12 of the 755 changes (1.6%), and that cluster is one health system reorganizing its own hospitals. The ten most active buyers together hold only 87 changes, 11.5% of the file. The average buyer appears 1.64 times, and 59.9% of all 461 buyers appear exactly once." }, { "q": "Is the hospital market being rolled up by a few big chains?", "a": "Not on this file. The buyer side is highly fragmented: the top ten buyers hold 11.5% of all changes and most buyers appear once. What concentration exists runs through repeat regional buyers — 185 buyers with two or more changes account for 63.4% of transactions — but no single national acquirer dominates. This is the opposite pattern from nursing-home ownership, where a smaller set of chains controls a large share of facilities." }, { "q": "What's the difference between a CHANGE OF OWNERSHIP and an ACQUISITION/MERGER here?", "a": "They are the two type codes CMS records on the filing. A plain change of ownership (code CH) is the more administrative case — the Medicare provider agreement is reassigned to a new owner — and it covers 85.6% of the file. An acquisition/merger (code AM) is the smaller block at 14.4%, and it skews more recent, with 60.6% of AM filings effective since 2020. Both are recorded as transactions; neither carries a price." }, { "q": "Does a change of ownership mean something went wrong at the hospital?", "a": "No. A CHOW filing is an enrollment transaction, not a disciplinary, financial-distress, or quality signal. Ownership changes for many ordinary reasons — system consolidation, corporate restructuring, a parent reorganizing subsidiaries. This study draws no inference about price, motive, or the quality of care at any hospital before or after a change, and names no individual." }, { "q": "How is this different from your nursing-home ownership study?", "a": "The nursing-home study measures ownership concentration in a point-in-time snapshot of who owns facilities now. This study measures ownership-change transactions — events over time, each with a buyer, a seller, and a date. One asks how concentrated current ownership is; the other asks how often, and to whom, hospitals change hands. The hospital transaction file shows a fragmented buyer side, in contrast to the more concentrated nursing-home picture." }, { "q": "Can I reproduce these figures?", "a": "Yes. Every number is a direct count over the public cms_hospital_chow table — CMS's Hospital Change of Ownership file, snapshot dated 2026-06-17 — with no modeling. The exact SQL for the type mix, the buyer concentration, the repeat-buyer share, the year-by-year effective dates, and the state breakdown is published in the reproducibility block below." } ], "citation": { "apa": "Fonteum Research. (2026, June 17). Who buys America's hospitals: a fragmented market with no roll-up, 2026. Fonteum Research, Issue 86. https://doi.org/10.5072/fonteum/hospital-ownership-changes-2026", "url": "https://fonteum.com/research/hospital-ownership-changes-2026" }, "reproducible_sql": "-- Who is buying America's hospitals — and the answer is: almost everyone, a\n-- little. Fully reproducible query.\n--\n-- Question: across the hospital Change-of-Ownership (CHOW) transactions CMS\n-- publishes, what kind of ownership change is it, who is on the buying side,\n-- when did it take effect, and where? The lead figure: the ten most active\n-- buyers account for just 87 of 755 changes (11.5%), 59.9% of all buyers\n-- appear exactly once, and the single largest cluster on the file is one\n-- health system reorganizing itself. A CHOW filing records a transaction, NOT\n-- a quality, fraud, or wrongdoing signal of any kind.\n--\n-- Source:\n-- public.cms_hospital_chow — CMS \"Hospital Change of Ownership\" public-use\n-- file, published via the CMS data catalog (data.cms.gov, Medicare\n-- provider enrollment / PECOS). 755 transaction rows across 742 distinct\n-- Medicare CCNs; snapshot 2026-06-17. Public, read-only. License:\n-- US-Government-Works (17 U.S.C. Sec. 105).\n-- methodology_version = 'cms-hospital-chow/v1'.\n--\n-- Universe: this study reads the published file AS A WHOLE — every row is one\n-- hospital ownership-change transaction CMS lists, with a buyer identity, a\n-- seller identity, a CHOW type, an effective date, and a state. The file is\n-- a single point-in-time snapshot (snapshot 2026-06-17) of the transactions\n-- on record; it is not a complete census of every U.S. hospital deal ever.\n--\n-- Counting note: the unit of analysis is the TRANSACTION (one row), not the\n-- hospital. A hospital (CCN) can appear in more than one transaction across\n-- the period. Buyer / seller names are the organizational entities recorded\n-- on the filing; no individual person is named in the study, and names are\n-- used only as the factual aggregate unit of a count.\n\n-- ============================================================================\n-- (1) Universe reconciliation — the published file at a glance.\n-- ============================================================================\nSELECT\n count(*) AS events,\n count(DISTINCT ccn) AS distinct_ccn,\n count(DISTINCT buyer_name) AS distinct_buyers,\n count(DISTINCT seller_name) AS distinct_sellers,\n count(*) FILTER (WHERE buyer_name IS NULL OR buyer_name = '') AS null_buyer,\n count(*) FILTER (WHERE seller_name IS NULL OR seller_name = '') AS null_seller,\n count(*) FILTER (WHERE state IS NULL OR state = '') AS null_state,\n min(chow_effective_date) AS earliest,\n max(chow_effective_date) AS latest,\n max(snapshot_date) AS snapshot\nFROM public.cms_hospital_chow;\n-- events 755 · distinct_ccn 742 · distinct_buyers 461 · distinct_sellers 491\n-- null_buyer 0 · null_seller 0 · null_state 0\n-- earliest 2016-01-01 · latest 2025-12-28 · snapshot 2026-06-17\n\n-- ============================================================================\n-- (2) WHAT KIND of change. 85.6% are plain CHANGE OF OWNERSHIP filings; only\n-- 14.4% are coded ACQUISITION/MERGER. Most hospital ownership change is a\n-- quiet reassignment of the Medicare billing entity, not a headline merger.\n-- ============================================================================\nSELECT\n chow_type_code,\n chow_type,\n count(*) AS events,\n round(100.0 * count(*) / sum(count(*)) OVER (), 1) AS pct_of_all\nFROM public.cms_hospital_chow\nGROUP BY chow_type_code, chow_type\nORDER BY events DESC;\n-- CH CHANGE OF OWNERSHIP 646 85.6%\n-- AM ACQUISITION/MERGER 109 14.4%\n\n-- ============================================================================\n-- (3) HEADLINE: buyer-side concentration. The ten most active buyers hold just\n-- 87 of 755 changes (11.5%); the single most active buyer took 12 (1.6%).\n-- There is no national roll-up here.\n-- ============================================================================\nSELECT\n buyer_name,\n count(*) AS acquisitions,\n count(DISTINCT seller_name) AS distinct_sellers,\n count(DISTINCT state) AS states\nFROM public.cms_hospital_chow\nGROUP BY buyer_name\nORDER BY acquisitions DESC\nLIMIT 10;\n-- DIGNITY COMMUNITY CARE 12 · 1 seller · 2 states\n-- MEDICAL UNIVERSITY HOSPITAL AUTHORITY 10 · 5 sellers · 1 state\n-- PRISMA HEALTH-UPSTATE 10 · 2 sellers · 1 state\n-- HMH HOSPITALS CORPORATION 10 · 4 sellers · 1 state\n-- SUTTER BAY HOSPITALS 10 · 4 sellers · 1 state\n-- CATHOLIC HEALTH INITIATIVES COLORADO 8 · 4 sellers · 2 states\n-- UNIVERSITY OF CALIFORNIA IRVINE 7 · 5 sellers · 1 state\n-- BOWLING GREEN-WARREN COUNTY COMMUNITY .. 7 · 4 sellers · 1 state\n-- ST JOSEPH HEALTH NORTHERN CALIFORNIA LLC 7 · 4 sellers · 1 state\n-- OSF HEALTHCARE SYSTEM 6 · 4 sellers · 1 state\n-- (top 10 buyers = 87 of 755 changes = 11.5% of the file.)\n\n-- ============================================================================\n-- (4) The concentration, computed directly. 59.9% of buyers appear exactly\n-- once; the 185 repeat buyers (2+ changes) drive 63.4% of all changes at\n-- 2.59 each on average. Consolidation runs through many regional systems,\n-- not a handful of national chains.\n-- ============================================================================\nWITH b AS (\n SELECT buyer_name, count(*) AS n\n FROM public.cms_hospital_chow\n GROUP BY buyer_name\n)\nSELECT\n count(*) AS distinct_buyers,\n count(*) FILTER (WHERE n = 1) AS one_time_buyers,\n round(100.0 * count(*) FILTER (WHERE n = 1) / count(*), 1) AS one_time_pct,\n count(*) FILTER (WHERE n >= 2) AS repeat_buyers,\n sum(n) FILTER (WHERE n >= 2) AS events_by_repeat,\n round(100.0 * sum(n) FILTER (WHERE n >= 2) / sum(n), 1) AS repeat_event_pct,\n round(avg(n), 2) AS avg_per_buyer,\n round(avg(n) FILTER (WHERE n >= 2), 2) AS avg_per_repeat,\n max(n) AS max_acquisitions\nFROM b;\n-- distinct_buyers 461 · one_time_buyers 276 (59.9%) · repeat_buyers 185\n-- events_by_repeat 479 (63.4%) · avg_per_buyer 1.64 · avg_per_repeat 2.59\n-- max_acquisitions 12\n\n-- ============================================================================\n-- (5) The largest single cluster is internal. DIGNITY COMMUNITY CARE's 12\n-- acquisitions are all from DIGNITY HEALTH, all effective 2019-02-01, all\n-- coded CH (change of ownership) — an intra-system reorganization (the\n-- Dignity Health / CommonSpirit transition), not an outside acquisition.\n-- ============================================================================\nSELECT\n buyer_name,\n seller_name,\n chow_type_code,\n count(*) AS events,\n min(chow_effective_date) AS first_effective,\n max(chow_effective_date) AS last_effective\nFROM public.cms_hospital_chow\nWHERE buyer_name ILIKE '%DIGNITY%' OR seller_name ILIKE '%DIGNITY%'\nGROUP BY buyer_name, seller_name, chow_type_code\nORDER BY events DESC\nLIMIT 5;\n-- DIGNITY COMMUNITY CARE <- DIGNITY HEALTH CH 12 2019-02-01 .. 2019-02-01\n-- PORT CITY OPERATING COMPANY LLC <- DIGNITY HEALTH CH 2 2016-06-01\n-- DIGNITY HEALTH <- DIGNITY HEALTH AM 1 2020-01-31\n\n-- ============================================================================\n-- (6) WHEN — first-effective year of each change, 2016 onward. Volume runs in\n-- waves (2019 = 127, 2024 = 101) rather than rising steadily; 2025 is a\n-- partial / still-settling year as late filings arrive.\n-- ============================================================================\nSELECT\n extract(year FROM chow_effective_date)::int AS effective_year,\n count(*) AS events,\n count(*) FILTER (WHERE chow_type_code = 'AM') AS acquisition_merger\nFROM public.cms_hospital_chow\nGROUP BY effective_year\nORDER BY effective_year;\n-- 2016 60 · 2017 59 · 2018 95 · 2019 127 · 2020 73\n-- 2021 89 · 2022 38 · 2023 74 · 2024 101 · 2025 39 (partial)\n\n-- ============================================================================\n-- (7) WHERE — the states with the most hospital ownership change. California\n-- (74) and Texas (64) lead; no state holds even 10% of the file, the same\n-- fragmentation visible on the buyer side.\n-- ============================================================================\nSELECT\n state,\n count(*) AS events,\n round(100.0 * count(*) / sum(count(*)) OVER (), 1) AS pct_of_all\nFROM public.cms_hospital_chow\nWHERE state IS NOT NULL AND state <> ''\nGROUP BY state\nORDER BY events DESC\nLIMIT 10;\n-- CA 74 9.8% · TX 64 8.5% · IL 38 5.0% · OK 34 4.5% · NC 32 4.2%\n-- FL 31 4.1% · LA 31 4.1% · SC 31 4.1% · GA 29 3.8% · AR 24 3.2%\n\n-- ============================================================================\n-- (8) Provider-type mix — every row is a hospital, but a fifth are rural\n-- critical-access hospitals (167 of 755 = 22.1%), a reminder that ownership\n-- change is not just a big-city, big-system phenomenon.\n-- ============================================================================\nSELECT\n provider_type,\n count(*) AS events,\n round(100.0 * count(*) / sum(count(*)) OVER (), 1) AS pct_of_all\nFROM public.cms_hospital_chow\nGROUP BY provider_type\nORDER BY events DESC;\n-- PART A PROVIDER - HOSPITAL 587 77.7%\n-- PART A PROVIDER - CRITICAL ACCESS HOSPITAL 167 22.1%\n-- PART A PROVIDER - RURAL EMERGENCY HOSPITAL 1 0.1%", "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." }