· Private foundation
National Foundation of Emergency Medicine Inc
To encourage, support and facilitate clinical research in academic emergency medicine; to improve research capability in emergency medicine through the scholar-mentor program; to facilitate multi-institutional collaborative research efforts; to seek out grants and contracts for research solely dedicated to emergency medicine; to promote…
What you funded, over time
Every grant placed by its stated purpose and the recipient’s mission, by year — across FY2017–2024.
Where the money goes
Your grants by size, and where they go.
| Recipient | Amount |
|---|---|
| UNIVERSITY OF CALIFORNIA SAN DIEGO SCHOOL OF MEDICINE | $30,000 |
| THE MOUNT SINAI HOSPITAL | $30,000 |
| THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL SCHOOL OF MEDICINE | $30,000 |
| UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE DEPARTMENT OF EMERGENCY MEDICINE | $30,000 |
| UCSF SCHOOL OF MEDICINE | $30,000 |
| HARVARD MEDICAL SCHOOL | $30,000 |
| EMORY UNIVERSITY DEPARTMENT OF EMERGENCY MEDICINE | $30,000 |
| PENN STATE COLLEGE OF MEDICINE | $30,000 |
| WAKE FOREST UNIVERSITY HEALTH SCIENCES | $30,000 |
| THE UNIVERSITY OF ARIZONA DEPARTMENT OF EMERGENCY MEDICINE | $30,000 |
| WEIL INSTITUTE FOR CRITICAL CARE RESEARCH AND INNOVATION | $20,000 |
Do your dollars go where the need is?
Each grant placed by the hardship in its grantee’s ZIP, then read against the area’s typical level.
Dollar for dollar, your grants (FY17–24) land where the poverty rate runs at 15%, against an area that typically sits at 10%. 94% of your dollars go to grantees based in above-average-need neighborhoods. Most of your grants land in higher-need ZIPs.
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty linein the grantee’s ZIP, averaged from the tracts in that ZIP; grantee ZIP can differ from where services are delivered.
Which US states your grants reach
US grants placed by each grantee’s ZIP, which can differ from where services are actually delivered. Need from public data — U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA — shown as context about the area, never attributed to your giving.
Who you back again
Which organizations you funded more than once, and which you funded a single time. Each grantee is matched to its own filings.
17 repeat relationships — 9 still active in FY2024, 8 since wound down; 2 grantees were first funded in FY2024 (too recent to call).
How the two cohorts compare
Organizations
Total granted
Median revenue growth · since first grant
Still filing today
New vs renewed · share of each year
In FY2024, 84% of grant dollars renewed an existing relationship; $50k went to new ones.
Where new relationships form · theme of each grantee’s first grant
First-time = a grantee’s first year in your filing window; renewed = funded in an earlier year too. As a portfolio matures the renewed share naturally climbs — once funded, an org stays “renewable” — so the signal is the years that buck it (a new-grantee intake wave). The earliest year is left-censored: relationships that predate the data read as “new.” Theme is the grantee’s IRS cause.
Backed again, and grew
- BIBETH ISRAEL DEACONESS MEDICAL CENTER INC2× · 2017–2018 · $444k · revenue +82%
- TMTHE MOUNT SINAI HOSPITAL2× · 2022–2024 · $90k · revenue +25%
- USUC SAN DIEGO FOUNDATION2× · 2017–2018 · $50k · revenue +78%
Funded once
- EIEUREKA INSTITUTE INCgraduatedone grant, 2021 · $24k · revenue +80% · 42% of their budget
Repeat = funded in two or more distinct years; growth and survival are read from each grantee’s own subsequent IRS filings.
6 grantees tracked through their own filings, 2017–2025.
Each one resolved to its own IRS returns and tracked year by year — your grant beside their revenue from every source. Association, dated; never a causal claim.
Counted here: distinct organizations you funded across 2017–2025, not grant rows in a single year — so this will not match the grant count on the cover. 6 of the 21 grantees resolved in that span have returns of their own we could reconcile; the rest are funded organizations whose filings we could not track year by year.
Where your money sits — by cause, then by grantee
Each org by its size and your share of it — top-left is where you’re load-bearing
Go grantee by grantee — a decade per org, and how each moved after you funded them
A decade per grantee — revenue shaded, your grants as bars, all rows on one timeline.
Government reliance of your grantees
Every dot is one organization National Foundation of Emergency Medicine Inc funds. Left–right is the share of its income from government; up–down is the share from you. Bigger dots raise more. Filter to federal, a single department, or state — and drag the year to watch it move.
- University of Maryland Emergency Medicine Associates Pa — 24% of income from government
- Beth Israel Deaconess Medical Center Inc — 6% of income from government
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 your grant caused the public funding. Coverage is precision-first — a floor, not 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 12–24 months.
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How do I get to National Foundation of Emergency Medicine Inc?
Find your warmest path to National Foundation of Emergency Medicine Inc through trustees and officers whose names appear on both boards. Search for your organization and Plinth traces the shortest route it can evidence.
Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.