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Kapi'olani Medical Specialists at a glanceAlso receives grants: funding profile →

· Public charity

Kapi'olani Medical Specialists

Kapi'olani Medical Specialists (dba hawai'I pacific health medical group) is a multispecialty provider group dedicated to providing world-class care for adults and children across hawai'I and the pacific region working in partnership with kapi'olani medical center for women and children (kapi'olani), pali momi medical center (pali momi),…

$15k
Granted FY2025still arriving
1
Grants FY2025still arriving
1
States reached
$10k
Largest
01What it fundsRead
02The needRead
03Its fieldRead
01What it funds
01FY2025 · 1 grant

Where the money goes

Grants by size, and the largest recipients.

How this is measured
The 1 grants below total $10,000, the rows itemized in this filing. The $15,200 headline is the total grant expense on the return; the other $5,200 is giving the schedule does not break out (grants under the $5,000 itemization floor, grants to individuals, grants on other schedules). Every figure below describes the itemized rows only.
$10,000
Median grant
1
States reached
$86M
Total assets
Largest grants
RecipientAmount
YWCA OF OAHU$10,000
02The need
02

Does the money reach high-need areas?

Each grant placed by the hardship in its grantee’s ZIP, against the area’s typical level.

show:

Its human-services grants (FY25–25, $10k) land where the poverty rate runs at 9%, against an area that typically sits at 7%. 100% of those dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.

area typical 7%YWCA OF OAHU: $10k → 9%0%20%40%50%more need →
grant to an above-average-need area below average· circle size = grant amount
How this is measured
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty line in the grantee’s ZIP, averaged from the tracts in that ZIP.
How this is measured
US grants placed by each grantee’s ZIP, which can differ from where services are delivered. Need from public data (U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA), shown as context about the area, never attributed to the funder’s giving.
03Its field
03the grantee network

1 grantees tracked through their own filings, 2017–2025.

Each grant beside the grantee’s revenue from every source, year by year.

How this is measured

Counted here: distinct organizations funded across 2017 to 2025, not grant rows in a single year, so this will not match the grant count on the cover.

Each grantee is resolved to its own IRS returns. Association, dated; never a causal claim.

0
Load-bearing (≥25% of a budget)
0
Early backer (in before they grew)
1/1
Grantees still filing
1/1
Grew since first funded

Each grantee by its size and the funder’s share of it; top left is load-bearing

25%50%75%100%grantee revenue →↑ funder's share of their budget
Grantee by grantee: a decade per organization, before and after funding

A decade per grantee: revenue shaded, grants as bars, all rows on one timeline.

How much its grantees rely on government

Each dot is one organization Kapi'olani Medical Specialists funds: left to right is the share of its income from government, bottom to top the share from Kapi'olani Medical Specialists. Bigger dots raise more.

2024
’20’21’22’23’24
no gov · 00%0%1%3%5%funder's share of their income ↑0%23%45%68%90%share of the org’s income from government →
    no gov moneyreceives it· size = income
    0get no government money at all
    0rely on government for over half their income
    ⤢ axis zoomed · 0–90%
    typical government reliance, FY20–24
    How this is measured
    Government income is each org’s traced federal awards (USASpending grants and contracts) plus state payments (open checkbooks) as a share of its total revenue (IRS Form 990); the self-reported government-grant line (990 line 1e) is carried for cross-check: a grantee that reports government grants we could not trace to a source is left off the chart rather than shown as receiving none. An association, not a claim that a grant caused the public funding. Coverage is precision-first, a floor rather than a census; state records exist for 9 states, so a grantee outside them shows no state figure (dimmed) rather than a false zero. Federal award amounts are obligations, which can span years, so a single-year share is indicative. 990 filings lag 12 to 24 months.

    On method. Every financial figure here is read from IRS e-file XML, the funder’s own 990/990-PF, and from the multi-year returns of the 1 grantees we resolved, each linked to its source. Grantee outcomes are each organization’s own program-service reporting (Form 990, Part III), read as association with funding, never as caused by it. Low-confidence name matches are suppressed rather than guessed. How we build these →

    How this is measured
    Each figure carries one of six labels. Filed is what the funder reported on its return. Official is another government record about an organization, such as a federal award or a charity register, joined by name where no shared identifier exists. Resolved is an identity we worked out where the filing named a recipient without an EIN, kept only above a measured confidence threshold. Computed is arithmetic over those, like themes, portfolio clusters and co-funder strength. Context is a statistic about a place rather than about an organization, which is what the need overlay is: it describes the area a grantee’s address sits in, not where its work lands. Inferred is drawn by a model from text, like the partnerships read out of public news and organization websites. Each is labeled where it appears.

    Generated from the IRS Form 990 e-file return for fiscal year 2025, released 2025. Filings run roughly 12 to 24 months behind; figures are dated accordingly.

    Source object · view filing

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    More from the funding graph

    The unnamed billions
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    The regranting layer
    51% of grant dollars land in the regranting layer
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    $9.7B circulates between donor-advised funds
    The endowment market
    who holds foundation money, and what just moved
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    funders cluster by geography, not ideology
    By state
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