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FINANCIAL DISTRESS · ISSUE 068
cms-open-paymentsOriginal Research

The 1% of doctors who get two-thirds of industry money

In 2024 the top 1% of physicians — 9,792 of the 979,136 who received any industry money — captured 66% of every general-payment dollar tied to a recipient, $1.74 billion of $2.64 billion. The bottom half split 1.2%. Measured across recipients, the Gini coefficient is 0.927, far above the ~0.41 of US household income.

BY FONTEUM RESEARCH BUREAU · JUNE 14, 2026 · 11 MIN READ · ASSERTED VIA SLSA L3REVIEWED BY DR. JENNIFER MONTECILLO, MDSNAPSHOT 2026-01-23 · DOI 10.5072/fonteum/open-payments-recipient-concentration-2024 · LAST UPDATED JUNE 14, 2026
CMS Open Payments · 2026-01-23
Reviewed by Dr. Jennifer Montecillo, MD, non-practicing medical reviewer. Gullas College of Medicine, 2019. Non-practicing medical reviewer focused on source interpretation, terminology, and limitations language. About our reviewers →
Reproduce this study →
Cumulative share of industry money by top band of recipients, 2024cms-open-payments · 2026-01-23
Top 0.1% (980)
33.9
Top 1% (9,792)
66.1
Top 5% (48,957)
85.2
Top 10% (97,914)
90
Top 50% (489,568)
98.8
Built on CMS Open Payments · snapshot 2026-01-23 · reproducible · re-derive the figures yourself
Key findings
66.1%
of all NPI-attributed general-payment dollars — $1.74 billion — went to the top 1% of physician recipients (9,792 of 979,136) in 2024
cms-open-payments · CMS
0.927
Gini coefficient of industry money across recipients — far above the ~0.41 of US household income, and one of the most concentrated distributions in public health-finance data
cms-open-payments · CMS
$164 vs $2,694
the median recipient took $164 — a meal or two — while the mean was $2,694, a sixteen-fold gap pulled up by a few multi-million-dollar royalty recipients
cms-open-payments · CMS
1.2%
the share left for the bottom half of recipients — 489,568 physicians split about one-fiftieth of the money
cms-open-payments · CMS
On this page
Two-thirds of the money goes to the top 1%The whole distribution, band by bandMore concentrated than incomeWho sits in the top 1%Two economies in one fileWhat one record actually isMethodologyLimitationsSources

The federal Open Payments program publishes, once a year, every consulting fee, speaking honorarium, royalty, meal, and travel reimbursement that drug and medical-device companies give to American physicians. The companion studies in this series ask who pays (device makers, not pharma) and which fields are paid (orthopedic surgery, by far). This one asks a different question of the same file: once the money reaches doctors, how evenly is it spread? The answer is that it is barely spread at all.

Two-thirds of the money goes to the top 1%

Group the 2024 general (non-research) payments by recipient and rank physicians from highest-paid to lowest. The top 1% of recipients — 9,792 of the 979,136 who received anything — captured 66.1% of every NPI-attributed general-payment dollar, about $1.74 billion of $2.64 billion. Narrow further: the top 0.1%, just 980 physicians, took 33.9% — a third of the entire pool flows to fewer than a thousand people. Widen out, and the curve is just as steep at the bottom: the bottom half of recipients, 489,568 physicians, split 1.2% between them.

A physician needed at least $42,735 for the year to sit in the top 1%, and at least $277,347 to reach the top 0.1%. Below the line, the numbers collapse fast: the median recipient received $164, and 363,598 recipients — more than a third of everyone in the file — received under $100 for the entire year.

Industry money to physicians is more unequally distributed than US household income — the top 1% of recipients take two-thirds of it, and the bottom half splits about one-fiftieth.

The whole distribution, band by band

The clearest way to see the concentration is the cumulative share at each cut of the ranking. Each row is the same pool of dollars, read from the top down.

BandRecipientsCumulative dollarsCumulative shareMinimum to qualify
Top 0.1%980$894,344,65233.9%$277,347
Top 1%9,792$1,742,445,93566.1%$42,735
Top 5%48,957$2,247,880,67485.2%$3,932
Top 10%97,914$2,374,842,73990.0%$1,837
Top 25%244,784$2,528,256,13095.8%$583
Top 50%489,568$2,606,597,40498.8%$164
All recipients979,136$2,638,034,613100.0%—

Source: CMS Open Payments PY2024, general payments with a recipient NPI, via the per-recipient rollup in the reproducibility block.

By the top decile the pool is effectively exhausted: the top 10% of physicians hold 90% of the money, leaving the remaining 881,000 recipients to share the last tenth. The distance between the 90% line at the top decile and the 1.2% line at the bottom half is the whole story — industry money does not taper off toward the median, it falls off a cliff just past the top few percent.

More concentrated than income

To put the steepness on a familiar scale, compute the Gini coefficient across recipients — the standard 0-to-1 measure of inequality, where 0 is everyone receiving the same amount and 1 is a single recipient taking everything. For industry payments to physicians in 2024, the Gini is 0.927.

That is an extreme figure. US household income carries a Gini of roughly 0.41; US wealth, far more skewed than income, sits near 0.85. At 0.927, the distribution of industry money across doctors is more concentrated than the American wealth distribution — among the most unequal distributions found in any public health-finance dataset.

A Gini of 0.927 does not describe how much any doctor earns — only how unevenly industry payments are spread across those who receive them. The vast majority of physicians take a few meals; a small group takes royalties. The coefficient measures that gap, not clinical income or influence.

The mean-versus-median gap says the same thing in dollars. The average recipient received $2,694; the median received $164. Whenever a mean sits sixteen times above its median, a small set of very large values is doing the lifting — here, the 156 physicians who each received $1 million or more, and above them a single recipient tied to the $91.1 million dental-device acquisition transfer that is the largest general payment in the entire file.

Who sits in the top 1%

The concentration is not random across medicine — it tracks the specialties where industry pays royalties rather than for meals. Break the 9,792 top-1% recipients down by their primary specialty and the operating room dominates.

SpecialtyRecipients in top 1%Top-1% dollarsShare of top-1% pool
Orthopaedic Surgery767$329,637,64218.9%
Endodontics27$101,248,4965.8%
Neurology433$66,929,8593.8%
Neurological Surgery287$66,169,2603.8%
Orthopaedic Surgery of the Spine249$57,584,5833.3%
Psychiatry321$56,761,6473.3%
Dermatology272$49,419,6782.8%
Adult Reconstructive Orthopaedic Surgery140$47,240,6582.7%

Source: CMS Open Payments PY2024, top 1% of NPI-identified general-payment recipients, most-specific taxonomy segment.

Orthopedic surgery alone supplies 767 of the top-1% recipients and 18.9% of their dollars; fold in the spine and adult-reconstructive subspecialties and the orthopedic family reaches 1,156 recipients and 24.9% of all top-1% money — a quarter of the most-concentrated tier in one surgical field. The pattern is structural, not behavioral: device makers owe royalties on implants and instruments, those royalties are large and accrue to a small number of high-volume surgeons, and the same firms that lead the list of largest payers — Stryker, Medtronic, Arthrex, Zimmer Biomet — are the ones putting these surgeons at the top of the recipient distribution. Endodontics is the lone outlier, third on the list almost entirely because of one acquisition-related transfer rather than a broad royalty stream.

Two economies in one file

The concentration resolves the file into two activities that share a statute but little else. At the top, a few thousand surgeons receive device royalties — large, recurring, individually negotiated transfers that account for two-thirds of the money. At the bottom, hundreds of thousands of clinicians receive food, beverage, and speaker payments — the broad, shallow footprint of pharmaceutical marketing, averaging tens of dollars and summing, across the entire bottom half, to barely one-fiftieth of the total. The companion study on the kinds of payments industry makes takes that split apart by payment nature; the recipient distribution is the same divide seen from the doctor's side of the ledger.

This is why a single number like "$2.64 billion to 979,136 physicians" misleads. It implies an average of roughly $2,700 a doctor, when the typical doctor sees $164 and a few see eight figures. The aggregate is real; the average is a fiction that no individual recipient experiences.

What one record actually is

Each row in cms_open_payments is one reported transfer of value to one covered recipient, carrying the recipient's NPI and primary specialty. Rolling those rows up by recipient_npi gives one total per physician — the unit of this study. A recipient who took 200 catered lunches and one who took a single $1 million royalty each become one observation; the concentration figures rank those observations and measure how the dollars distribute across them. Because the unit is the recipient, no count or share in this study names, ranks, or surfaces an individual physician.

Methodology

All figures are aggregations over the cms_open_payments table, populated from the CMS Open Payments program-year-2024 release (PGYR2024, published 2026-01-23, RLS Pattern B — public read). The table holds 16,146,544 records. "General payments" means records with record type general, excluding research ($8.49B) and ownership ($147.8M). The concentration universe is general payments carrying a recipient NPI: 979,136 recipients holding $2,638,034,613, which is 79.6% of the all-recipient general total of $3.31 billion; the remainder has no NPI and is about 98.5% teaching-hospital payments, excluded because a concentration measure needs an individual-recipient identity.

Per-recipient totals are computed as sum(total_amount_usd) GROUP BY recipient_npi — one recipient, one observation. Cumulative shares rank recipients descending and sum each top band as a fraction of the NPI-attributed total. The Gini coefficient uses the rank formula 2·Σ(i·amount)/(n·Σamount) − (n+1)/n over ascending ranks. Because a count-distinct plus percentile over the 15.4-million-row general slice exceeds PostgREST's statement timeout, the figures are point-in-time to the frozen 2026-01-23 snapshot; the exact SQL is in the reproducibility block below and on the Open Payments dataset page. Methodology version: open-payments/v1.

Limitations

  • Recipient-NPI universe only. Concentration is measured across the 979,136 general-payment recipients with an NPI ($2.64B). Teaching-hospital and other non-NPI payments ($675.8M) are excluded because they have no individual recipient; the all-recipient general total is $3.31 billion.
  • Snapshot, not a trend. Figures reflect the 2026-01-23 PY2024 release. CMS restates prior years and publishes a new file annually, so the bands shift between releases.
  • One-off transfers steepen the top. A single acquisition or large royalty (as with endodontics, or the $91.1 million top recipient) lifts the very top of the distribution. The concentration is real either way, but the extreme tail is sensitive to a handful of records.
  • Primary specialty only. Each recipient carries one primary specialty; multi-role clinicians are counted once. Subspecialties are reported separately from their parent field.
  • Reported identity, not resolved. Recipients are grouped by the NPI as reported; no cross-NPI entity resolution is applied, so a clinician reported under two NPIs would appear as two recipients.
  • Disclosure, not influence, and aggregate-only. Concentration measures how industry money is distributed, never its effect on care. No individual physician is named, ranked, or surfaced.

Sources

  • CMS — Open Payments (openpaymentsdata.cms.gov) — the federal disclosure database behind every figure in this study.
  • CMS — Open Payments data dictionary and methodology — recipient-NPI, recipient-type, payment-nature codes, and reporting rules.
  • Physician Payments Sunshine Act — 42 U.S.C. §1320a-7h — the statute requiring manufacturer disclosure.
  • U.S. Census Bureau — Gini index of income inequality — the reference definition and US household-income benchmark used for comparison.

The companion dataset page for CMS Open Payments lists the full schema and refresh cadence; the payer-side counterpart sits in which companies pay U.S. doctors the most, and the specialty cut in which specialties take the most industry money.

Frequently asked questions

What share of industry money goes to the top 1% of doctors?
In 2024, the top 1% of physician recipients — 9,792 of the 979,136 who received any general (non-research) industry payment — captured 66.1% of all NPI-attributed general-payment dollars, about $1.74 billion of $2.64 billion. A physician needed at least $42,735 for the year to sit in that top 1%.
How unequal is the distribution compared with income?
More unequal than income. The Gini coefficient of industry money across recipients is 0.927, where 0 is perfect equality and 1 is one recipient taking everything. US household income carries a Gini of roughly 0.41. Industry money to physicians is among the most concentrated distributions found in public health-finance data.
How much does a typical doctor receive?
Very little. The median recipient received $164 in 2024 — the value of a meal or two — while the mean was $2,694. That sixteen-fold gap between median and mean is the signature of a skewed distribution: a small number of recipients taking royalties in the hundreds of thousands or millions pull the average far above what the typical doctor sees.
Who is in the top 1%?
Procedural specialists, overwhelmingly. Orthopedic surgeons alone are 767 of the 9,792 top-1% recipients and 18.9% of all top-1% dollars; adding spine and adult-reconstructive subspecialties brings the orthopedic family to 1,156 recipients and 24.9% of the top-1% money. Neurology, neurosurgery, psychiatry and dermatology fill out the rest. The concentration is driven by device royalties, which are large and paid to a small number of high-volume surgeons.
Does the bottom half of doctors get anything?
Almost nothing in aggregate. The bottom 50% of recipients — 489,568 physicians — split about 1.2% of all NPI-attributed general-payment dollars between them. For most doctors who appear in Open Payments at all, the record is a handful of catered lunches: 363,598 recipients received under $100 for the entire year.
Why exclude teaching-hospital and non-NPI payments?
A concentration measure needs a recipient identity. Of the $3.31 billion in 2024 general payments, $2.64 billion (79.6%) carries an individual recipient NPI; the $675.8 million remainder has no NPI and is about 98.5% teaching-hospital payments, which flow to institutions rather than to a named individual. This study measures concentration across the 979,136 NPI-identified recipients only, and says so wherever the figure appears.
Does a large payment mean a doctor did something wrong?
No. These are lawful, federally disclosed payments, and the largest are typically royalties on invented devices or paid consulting. The data shows scale and structure, not wrongdoing or clinical influence. Open Payments exists so patients, journalists and researchers can see the financial relationships; concentration describes how the money lands, not whether any payment changed any decision.
Can I reproduce these concentration figures?
Yes. Every figure aggregates the cms_open_payments table (16,146,544 records, program year 2024) over the recipient-NPI universe of general payments. The exact SQL — per-recipient rollup, percentile bands, Gini, and the universe reconciliation — is published in the reproducibility block below. No individual physician is named or ranked.

Datasets used

CMS Open Payments→

Reproducibility

Every claim, reproducible

The SQL+
open-payments-recipient-concentration-2024.sql
-- Open Payments recipient CONCENTRATION — fully reproducible query.
--
-- Question: among the physicians who receive industry money, how concentrated
-- is it? We measure, over the recipient-NPI universe, what share of all
-- general-payment dollars flows to the top 0.1% / 1% / 5% / 10% / 50% of
-- recipients, the Gini coefficient across recipients, and the median-vs-mean
-- gap. This is the RECIPIENT-side companion to the manufacturer study
-- (payer-side: top 25 of 1,763 companies = 52%) — here the axis is how the
-- money lands on doctors, not which companies send it.
--
-- Source:
--   public.cms_open_payments — CMS Open Payments program-year-2024 release
--     (PGYR2024, published 2026-01-23). One row per reported transfer of value.
--     16,146,544 records total; RLS Pattern B — public read.
--     License: US-Government-Works (17 U.S.C. §105).
--
-- Scope: general (non-research) payments only (record_type = 'general'), the
--   $3.31B consulting / speaking / royalty / food / travel bucket. Research
--   ($8.49B) and ownership ($147.8M) are excluded.
--
-- Recipient universe: records carrying a recipient NPI (an individual-recipient
--   identity). 15,336,988 of 15,385,047 general records carry one; the
--   $675.8M remainder has no NPI and is ~98.5% teaching-hospital payments —
--   excluded because a concentration measure needs a recipient identity.
--   NPI-attributed general dollars = $2,638,034,613 across 979,136 recipients.
--
-- These aggregates run server-side (direct SQL) — a count-distinct + percentile
-- over the 15.4M-row general slice exceeds PostgREST's 8s statement timeout,
-- which is why the page renders point-in-time figures from this frozen
-- 2026-01-23 snapshot rather than reading the table at request time.

-- Per-recipient rollup reused by every query below.
WITH recip AS (
  SELECT recipient_npi,
         sum(total_amount_usd)::numeric AS amt,
         max(recipient_specialty)        AS spec
  FROM public.cms_open_payments
  WHERE record_type = 'general'
    AND program_year = 2024
    AND recipient_npi IS NOT NULL
  GROUP BY recipient_npi
)

-- ============================================================================
-- (1) Headline: cumulative share of NPI-attributed dollars by top band.
--     The top 1% share (66.1%) is the lead figure.
-- ============================================================================
SELECT b.label,
       ceil(max(n) * b.frac)                                           AS recipients_in_band,
       round(sum(amt) FILTER (WHERE rk <= ceil(n * b.frac)))           AS cum_dollars,
       round(sum(amt) FILTER (WHERE rk <= ceil(n * b.frac))
             / max(tot) * 100, 1)                                      AS cum_share_pct,
       round(min(amt) FILTER (WHERE rk <= ceil(n * b.frac)))           AS min_amount_in_band
FROM (
  SELECT amt,
         row_number() OVER (ORDER BY amt DESC) AS rk,
         count(*)     OVER ()                  AS n,
         sum(amt)     OVER ()                  AS tot
  FROM recip
) ranked
CROSS JOIN (VALUES ('Top 0.1%', 0.001), ('Top 1%', 0.01), ('Top 5%', 0.05),
                   ('Top 10%', 0.10), ('Top 25%', 0.25), ('Top 50%', 0.50),
                   ('All 100%', 1.0)) AS b(label, frac)
GROUP BY b.label, b.frac
ORDER BY b.frac;
--  Top 0.1%     980      894,344,652  33.9   277,347
--  Top 1%     9,792    1,742,445,935  66.1    42,735
--  Top 5%    48,957    2,247,880,674  85.2     3,932
--  Top 10%   97,914    2,374,842,739  90.0     1,837
--  Top 25%  244,784    2,528,256,130  95.8       583
--  Top 50%  489,568    2,606,597,404  98.8       164
--  All 100% 979,136    2,638,034,613 100.0         0
--  (bottom 50% therefore split 100.0 - 98.8 = 1.2% of the dollars.)

-- ============================================================================
-- (2) Distribution shape — median vs mean, thresholds, and the long tail.
-- ============================================================================
SELECT
  count(*)                                                        AS recipients,
  round(avg(amt))                                                 AS mean_recipient,
  round(percentile_cont(0.5)  WITHIN GROUP (ORDER BY amt))        AS median_recipient,
  round(percentile_cont(0.9)  WITHIN GROUP (ORDER BY amt))        AS p90_recipient,
  round(percentile_cont(0.99) WITHIN GROUP (ORDER BY amt))        AS p99_recipient,
  round(max(amt))                                                 AS max_recipient,
  count(*) FILTER (WHERE amt >= 1000000)                          AS recipients_ge_1m,
  count(*) FILTER (WHERE amt >= 100000)                           AS recipients_ge_100k,
  count(*) FILTER (WHERE amt <  100)                              AS recipients_lt_100
FROM recip;
--  recipients 979,136 · mean $2,694 · median $164 · p90 $1,837 · p99 $42,731
--  max_recipient $91,082,706 (one recipient = the single largest general
--    payment in the file, a dental-device acquisition transfer)
--  >=$1M 156 · >=$100k 4,027 · <$100 363,598

-- ============================================================================
-- (3) Gini coefficient across recipients + bottom-50% share.
--     Gini = (2 * sum(i * amt) / (n * sum(amt))) - (n + 1) / n,
--     with i = ascending rank. 0 = perfectly equal, 1 = one recipient takes all.
-- ============================================================================
WITH ord AS (
  SELECT amt, row_number() OVER (ORDER BY amt) AS i FROM recip
),
agg AS (
  SELECT count(*)::numeric AS n,
         sum(amt)          AS tot,
         sum(i * amt)      AS wsum,
         sum(amt) FILTER (WHERE i <= (SELECT count(*) FROM recip) * 0.5) AS bottom50
  FROM ord
)
SELECT round((2.0 * wsum / (n * tot) - (n + 1.0) / n)::numeric, 3) AS gini,
       round((bottom50 / tot * 100)::numeric, 2)                  AS bottom50_share_pct
FROM agg;
--  gini 0.927 · bottom50_share 1.19   (US household-income Gini ≈ 0.41)

-- ============================================================================
-- (4) Who sits in the top 1% — specialty mix of the 9,792 highest-paid
--     recipients (most-specific CMS taxonomy segment). No recipient named.
-- ============================================================================
WITH ranked AS (
  SELECT amt, spec,
         row_number() OVER (ORDER BY amt DESC) AS rk,
         count(*)     OVER ()                  AS n
  FROM recip
),
top1 AS (
  SELECT amt,
         trim(split_part(spec, '|', array_length(string_to_array(spec, '|'), 1))) AS leaf
  FROM ranked WHERE rk <= ceil(n * 0.01)
)
SELECT leaf AS specialty,
       count(*)                                              AS recipients_in_top1pct,
       round(sum(amt))                                       AS dollars,
       round(sum(amt) / (SELECT sum(amt) FROM top1) * 100, 1) AS pct_of_top1_dollars
FROM top1
GROUP BY leaf
ORDER BY dollars DESC
LIMIT 8;
--  Orthopaedic Surgery                        767  329,637,642  18.9
--  Endodontics                                 27  101,248,496   5.8
--  Neurology                                  433   66,929,859   3.8
--  Neurological Surgery                       287   66,169,260   3.8
--  Orthopaedic Surgery of the Spine           249   57,584,583   3.3
--  Psychiatry                                 321   56,761,647   3.3
--  Dermatology                                272   49,419,678   2.8
--  Adult Reconstructive Orthopaedic Surgery   140   47,240,658   2.7
--  (orthopedic family — surgery + spine + adult-reconstructive — = 1,156
--   recipients and 24.9% of all top-1% dollars.)

-- ============================================================================
-- (5) Universe reconciliation — where the non-NPI general dollars go.
-- ============================================================================
SELECT
  round(sum(total_amount_usd) FILTER (WHERE recipient_npi IS NOT NULL))                       AS npi_dollars,
  round(sum(total_amount_usd) FILTER (WHERE recipient_npi IS NULL))                           AS non_npi_dollars,
  round(sum(total_amount_usd) FILTER (WHERE recipient_npi IS NULL
        AND recipient_type ILIKE '%Teaching Hospital%'))                                      AS teaching_hospital_dollars,
  round(sum(total_amount_usd))                                                                AS general_total
FROM public.cms_open_payments
WHERE record_type = 'general' AND program_year = 2024;
--  npi_dollars 2,638,034,613 · non_npi 675,767,124 · teaching_hosp 665,901,119
--  general_total 3,313,801,737  (NPI-attributed = 79.6% of the all-recipient total)
The snapshot+
dataset_idcms-open-payments
snapshot_date2026-01-23
sha256
doi10.5072/fonteum/open-payments-recipient-concentration-2024
slsa_provenance_url
The JOINs+
recipient universe: record_type='general' AND program_year=2024 AND recipient_npi IS NOT NULL  -- 979,136 recipients
npi_attributed_value = sum(total_amount_usd) over that universe                                -- $2,638,034,613
per-recipient amt    = sum(total_amount_usd) GROUP BY recipient_npi                            -- one recipient = one observation
top1pct_share        = sum(amt where rank <= ceil(0.01*n)) / total                             -- $1,742,445,935 / $2,638,034,613 = 66.1%
gini = 2*sum(i*amt)/(n*sum(amt)) - (n+1)/n, i = ascending rank                                 -- 0.927
median_recipient / mean_recipient = percentile_cont(0.5) / avg(amt)                            -- $164 / $2,694
The pipeline version+
git_sha
slsa_provenance
methodology_versionopen-payments/v1

Reproduce this

Run the exact query against the frozen 2026-01-23.

-- Open Payments recipient CONCENTRATION — fully reproducible query. -- -- Question: among the physicians who receive industry money, how concentrated -- is it? We measure, over the recipient-NPI universe, what share of all -- general-payment dollars flows to the top 0.1% / 1% / 5% / 10% / 50% of -- recipients, the Gini coefficient across recipients, and the median-vs-mean -- gap. This is the RECIPIENT-side companion to the manufacturer study -- (payer-side: top 25 of 1,763 companies = 52%) — here the axis is how the -- money lands on doctors, not which companies send it. -- -- Source: -- public.cms_open_payments — CMS Open Payments program-year-2024 release -- (PGYR2024, published 2026-01-23). One row per reported transfer of value. -- 16,146,544 records total; RLS Pattern B — public read. -- License: US-Government-Works (17 U.S.C. §105). -- -- Scope: general (non-research) payments only (record_type = 'general'), the -- $3.31B consulting / speaking / royalty / food / travel bucket. Research -- ($8.49B) and ownership ($147.8M) are excluded. -- -- Recipient universe: records carrying a recipient NPI (an individual-recipient -- identity). 15,336,988 of 15,385,047 general records carry one; the -- $675.8M remainder has no NPI and is ~98.5% teaching-hospital payments — -- excluded because a concentration measure needs a recipient identity. -- NPI-attributed general dollars = $2,638,034,613 across 979,136 recipients. -- -- These aggregates run server-side (direct SQL) — a count-distinct + percentile -- over the 15.4M-row general slice exceeds PostgREST's 8s statement timeout, -- which is why the page renders point-in-time figures from this frozen -- 2026-01-23 snapshot rather than reading the table at request time. -- Per-recipient rollup reused by every query below. WITH recip AS ( SELECT recipient_npi, sum(total_amount_usd)::numeric AS amt, max(recipient_specialty) AS spec FROM public.cms_open_payments WHERE record_type = 'general' AND program_year = 2024 AND recipient_npi IS NOT NULL GROUP BY recipient_npi ) -- ============================================================================ -- (1) Headline: cumulative share of NPI-attributed dollars by top band. -- The top 1% share (66.1%) is the lead figure. -- ============================================================================ SELECT b.label, ceil(max(n) * b.frac) AS recipients_in_band, round(sum(amt) FILTER (WHERE rk <= ceil(n * b.frac))) AS cum_dollars, round(sum(amt) FILTER (WHERE rk <= ceil(n * b.frac)) / max(tot) * 100, 1) AS cum_share_pct, round(min(amt) FILTER (WHERE rk <= ceil(n * b.frac))) AS min_amount_in_band FROM ( SELECT amt, row_number() OVER (ORDER BY amt DESC) AS rk, count(*) OVER () AS n, sum(amt) OVER () AS tot FROM recip ) ranked CROSS JOIN (VALUES ('Top 0.1%', 0.001), ('Top 1%', 0.01), ('Top 5%', 0.05), ('Top 10%', 0.10), ('Top 25%', 0.25), ('Top 50%', 0.50), ('All 100%', 1.0)) AS b(label, frac) GROUP BY b.label, b.frac ORDER BY b.frac; -- Top 0.1% 980 894,344,652 33.9 277,347 -- Top 1% 9,792 1,742,445,935 66.1 42,735 -- Top 5% 48,957 2,247,880,674 85.2 3,932 -- Top 10% 97,914 2,374,842,739 90.0 1,837 -- Top 25% 244,784 2,528,256,130 95.8 583 -- Top 50% 489,568 2,606,597,404 98.8 164 -- All 100% 979,136 2,638,034,613 100.0 0 -- (bottom 50% therefore split 100.0 - 98.8 = 1.2% of the dollars.) -- ============================================================================ -- (2) Distribution shape — median vs mean, thresholds, and the long tail. -- ============================================================================ SELECT count(*) AS recipients, round(avg(amt)) AS mean_recipient, round(percentile_cont(0.5) WITHIN GROUP (ORDER BY amt)) AS median_recipient, round(percentile_cont(0.9) WITHIN GROUP (ORDER BY amt)) AS p90_recipient, round(percentile_cont(0.99) WITHIN GROUP (ORDER BY amt)) AS p99_recipient, round(max(amt)) AS max_recipient, count(*) FILTER (WHERE amt >= 1000000) AS recipients_ge_1m, count(*) FILTER (WHERE amt >= 100000) AS recipients_ge_100k, count(*) FILTER (WHERE amt < 100) AS recipients_lt_100 FROM recip; -- recipients 979,136 · mean $2,694 · median $164 · p90 $1,837 · p99 $42,731 -- max_recipient $91,082,706 (one recipient = the single largest general -- payment in the file, a dental-device acquisition transfer) -- >=$1M 156 · >=$100k 4,027 · <$100 363,598 -- ============================================================================ -- (3) Gini coefficient across recipients + bottom-50% share. -- Gini = (2 * sum(i * amt) / (n * sum(amt))) - (n + 1) / n, -- with i = ascending rank. 0 = perfectly equal, 1 = one recipient takes all. -- ============================================================================ WITH ord AS ( SELECT amt, row_number() OVER (ORDER BY amt) AS i FROM recip ), agg AS ( SELECT count(*)::numeric AS n, sum(amt) AS tot, sum(i * amt) AS wsum, sum(amt) FILTER (WHERE i <= (SELECT count(*) FROM recip) * 0.5) AS bottom50 FROM ord ) SELECT round((2.0 * wsum / (n * tot) - (n + 1.0) / n)::numeric, 3) AS gini, round((bottom50 / tot * 100)::numeric, 2) AS bottom50_share_pct FROM agg; -- gini 0.927 · bottom50_share 1.19 (US household-income Gini ≈ 0.41) -- ============================================================================ -- (4) Who sits in the top 1% — specialty mix of the 9,792 highest-paid -- recipients (most-specific CMS taxonomy segment). No recipient named. -- ============================================================================ WITH ranked AS ( SELECT amt, spec, row_number() OVER (ORDER BY amt DESC) AS rk, count(*) OVER () AS n FROM recip ), top1 AS ( SELECT amt, trim(split_part(spec, '|', array_length(string_to_array(spec, '|'), 1))) AS leaf FROM ranked WHERE rk <= ceil(n * 0.01) ) SELECT leaf AS specialty, count(*) AS recipients_in_top1pct, round(sum(amt)) AS dollars, round(sum(amt) / (SELECT sum(amt) FROM top1) * 100, 1) AS pct_of_top1_dollars FROM top1 GROUP BY leaf ORDER BY dollars DESC LIMIT 8; -- Orthopaedic Surgery 767 329,637,642 18.9 -- Endodontics 27 101,248,496 5.8 -- Neurology 433 66,929,859 3.8 -- Neurological Surgery 287 66,169,260 3.8 -- Orthopaedic Surgery of the Spine 249 57,584,583 3.3 -- Psychiatry 321 56,761,647 3.3 -- Dermatology 272 49,419,678 2.8 -- Adult Reconstructive Orthopaedic Surgery 140 47,240,658 2.7 -- (orthopedic family — surgery + spine + adult-reconstructive — = 1,156 -- recipients and 24.9% of all top-1% dollars.) -- ============================================================================ -- (5) Universe reconciliation — where the non-NPI general dollars go. -- ============================================================================ SELECT round(sum(total_amount_usd) FILTER (WHERE recipient_npi IS NOT NULL)) AS npi_dollars, round(sum(total_amount_usd) FILTER (WHERE recipient_npi IS NULL)) AS non_npi_dollars, round(sum(total_amount_usd) FILTER (WHERE recipient_npi IS NULL AND recipient_type ILIKE '%Teaching Hospital%')) AS teaching_hospital_dollars, round(sum(total_amount_usd)) AS general_total FROM public.cms_open_payments WHERE record_type = 'general' AND program_year = 2024; -- npi_dollars 2,638,034,613 · non_npi 675,767,124 · teaching_hosp 665,901,119 -- general_total 3,313,801,737 (NPI-attributed = 79.6% of the all-recipient total)

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Fonteum Research Bureau (2026). The 1% of doctors who get two-thirds of industry money. CMS Open Payments, snapshot 2026-01-23. https://fonteum.com/research/open-payments-recipient-concentration-2024

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Related studies

  • FINANCIAL DISTRESS · JUN 2026Which companies pay U.S. doctors the most? Device makers, not pharmaIn 2024, drug and device companies disclosed $3.31 billion in general payments to U.S. physicians under the Sunshine Act — and the largest payers are device makers, not pharma. BioNTech led at $180.6 million from just 164 royalty payments; the top 25 of 1,763 reporting companies account for 52% of every general-payment dollar.
  • FINANCIAL DISTRESS · JUN 2026Which medical specialties take the most industry money?In 2024, U.S. orthopedic surgeons received $381.4 million in general industry payments — more than any other specialty and over three times the second-place field. Counting spine, joint and sports-medicine subspecialties, orthopedics drew $531.8 million, about 16% of the $3.31 billion total. The average orthopedic payment was $1,711; the average internal-medicine payment was $96.
  • FINANCIAL DISTRESS · JUN 2026Industry payments to physicians by state: where the money landsIndustry's $3.31 billion in 2024 general payments to physicians spread across 59 U.S. jurisdictions, but not in proportion to population. California led at $334.5 million, yet Pennsylvania ranked third and Massachusetts fourth on far fewer payments — Massachusetts averaged $1,031 per payment against Texas's $153. Where royalty recipients live, not where patients are, shapes the map.
  • FINANCIAL DISTRESS · JUN 2026What pharma actually buys: food, travel, consulting and royaltiesIndustry made 15.4 million general payments to U.S. physicians in 2024, worth $3.31 billion — but the two halves barely overlap. Royalties, speaking and consulting are 2.9% of payments yet 63% of the dollars; food and beverage is 91.7% of payments but 12.4% of the money. The average meal was $29; the average royalty, $56,258.
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Federal source citations

  1. [1]CMS Open Payments · snapshot 2026-01-23 · federal source family · US-Government-Works
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Fonteum Research · June 14, 2026 · All figures trace to the frozen federal-data snapshot cited above.

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