5 public registries, reconciled

Public health data was never built to be read together.

Unicasys cross-references industry payments, device malfunction reports, and trial registries — turning millions of disconnected filings into one searchable signal.

NO SETUP REQUIRED UPDATED WITH EVERY SOURCE RELEASE
Data sources

Five registries. One record of truth.

Each source publishes on its own schedule, in its own format, with its own quirks. Unicasys normalizes all five so a physician, a device, a facility, or a sponsor reads the same everywhere.

CMS · Physician payments

OpenPayments

Track industry payments to providers and teaching hospitals — consulting fees, research funding, ownership stakes — patterned across specialties and institutions.

General & researchAnnual
FDA · Device events

MAUDE

Surface device malfunction and adverse event reports as they're filed — spotting the pattern of a recall long before the recall notice exists.

Malfunctions & injuriesRolling
NIH · Trial registry

ClinicalTrials.gov

Follow trials from registration to results — flagging enrollment gaps, missed deadlines, and sponsors who never post outcomes.

Trials & outcomesDaily
NLM · Publications

Publications

Link peer-reviewed research on PubMed back to the physicians, sponsors, and trials that produced it — citation by citation.

Literature & citationsDaily
CMS · Care Compare

Facilities

Track hospitals, surgical centers, and care facilities — ownership, quality ratings, and how they tie back to the payments and devices above.

Ownership & qualityQuarterly
How it works

From raw filing to flagged signal.

01 — INGEST

Pulled at the source

Filings are ingested directly from CMS, FDA, NIH, and NLM as they're published — no scraping delay, no stale exports.

02 — RECONCILE

Matched across registries

The same physician, device, facility, or sponsor is resolved across five inconsistent naming conventions into a single entity.

03 — FLAG

Outliers surfaced

Statistical thresholds and cross-source correlation flag what's worth a closer look — before it's news.

16M+
Payment records reconciled from OpenPayments
2.2M+
MAUDE device-event reports reconciled
596K+
Trials tracked from registration to results
9.3M+
Physicians & providers resolved across registries
2M+
Facilities tracked across ownership & ratings
15M+
Entity records linked in the resolved graph
Methodology

How records become one entity.

Matching is deterministic and reproducible end to end: every record carries a stable, content-addressed ID, and the same real-world person, device, facility, or sponsor always resolves to the same entity — no matter which registry it came from.

01 · Normalize

Canonical names

Names are lowercased, legal suffixes stripped (Inc, LLC, Corp), and punctuation removed — so "Company X Corporation" and "company x corp." collapse to the same key.

02 · Identity

Stable content-addressed IDs

A hash of the normalized name plus a type prefix (pro_, co_, fac_, dev_) yields the same entity ID every time the same record appears. No sequential keys, no drift between loads.

03 · Registry keys

NPI, NCT, CCN anchors

Providers, trials, facilities, and devices anchor on their official identifiers, so payments, enrollments, and event reports attach to the right record instead of a text guess.

04 · Cross-walks

Bridge tables

Manufacturer and taxonomy cross-walks reconcile company aliases and provider specialties across OpenPayments, MAUDE, and trial sponsors — the step that connects a device maker to a trial it funds.

05 · Deduplicate

Blocking & collapse

Records are grouped by blocking keys and deduplicated, so alias sets collapse into one canonical entity instead of a pile of near-duplicates.

06 · Search

Bloom-filter narrowing

A compact bloom index prunes candidate sets in milliseconds, so searches and link queries run across millions of resolved entities without scanning everything.

See the connections these agencies never drew for you.

Free for individual researchers and journalists. Built for teams who need it at scale.

About me

A single-person research project.

Unicasys is built and maintained by one person E. Hakim. It exists to read public U.S. government registries — CMS Open Payments, FDA MAUDE, ClinicalTrials.gov, and PubMed — as a single graph. All data comes from public sources; analysis, matching, and any conclusions are our own.

Terms of Service

Provided "as is", without warranty.

No warranty. Unicasys is provided on an "as is" and "as available" basis, without warranties of any kind, express or implied — including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, accuracy, completeness, or non-infringement.

No liability for inaccuracies. All data on this platform is produced by automated ingestion, matching, and calculation. We do not warrant that any calculation, aggregate, match, identification, or signal is correct, complete, or current, and we accept no responsibility or liability for any inaccurate calculation, mis-identification, omission, or error, or for any decision or action taken in reliance on the platform.

Your responsibility to verify. It is your full responsibility to validate, verify, and cross-check any finding or figure shown here against the original primary data source before relying on it for any purpose. The underlying records come from public U.S. government registries — CMS Open Payments, FDA MAUDE, ClinicalTrials.gov, and PubMed — and those original sources are the authoritative reference.

No professional advice. Nothing on this platform constitutes legal, medical, financial, investment, or compliance advice.