Data.gov — Health Insurance Search

It is the widest aperture available on American health-insurance data: a single query across the United States' federated government catalog, which held 552,271 indexed datasets at the August 2026 review, returning hundreds of matches on plan enrollment, exchange operation, coverage statistics and access to care.

The range is the point. Federal publishers sit beside state, county, city, tribal and university ones in one result list. The Centers for Medicare & Medicaid Services publish Health Insurance Exchanges Monthly Effectuated Enrollment and its annual companion; the Department of Labor contributes Auxiliary Health Insurance Data; NIH appears alongside King County, Washington's decade-long coverage series (2009-2019), New York State insurance-related indicators and New York City's Equitable Health Systems enrollment files. Every hit arrives as one DCAT-US record - title, description, publishing organization wearing a government-level badge, keywords, themes, issue and modification dates, distributions and harvest provenance - described identically enough to join into a single base.

Sorting runs on relevance, popularity and date; filters narrow by keyword and organization; a geographic box search scopes results spatially; and a downloadable-file-only toggle removes records without a directly retrievable file. Get a sample of this dataset cut to your states and plans before anything else.

Five leading results from the query, in relevance order:

# rank 1
title       : King County Health Insurance data (2009-2019)
publisher   : data.kingcounty.gov
org type    : County Government
theme       : Health & Wellness
has_download: true
harvested   : 2026-07-31T20:59:49

# rank 2
title       : Health Insurance Exchanges Monthly Effectuated Enrollment
publisher   : Centers for Medicare & Medicaid Services
org type    : Federal Government
has_download: true

# rank 3
title       : Health Insurance Exchanges Annual Effectuated Enrollment
publisher   : Centers for Medicare & Medicaid Services
org type    : Federal Government
has_download: true

# rank 4
title       : Equitable Health Systems - Health Insurance Enrollment
publisher   : City of New York
org type    : City Government
has_download: true

# rank 5
title       : Auxiliary Health Insurance Data
publisher   : Department of Labor
org type    : Federal Government
has_download: true

# one row per catalog record; ranks follow the August 2026 review pass

Read together they show the span one query covers: a county's decade-long coverage panel, two federal exchange-enrollment series at monthly and annual grain, a municipal enrollment file and a Department of Labor table - four government levels in five rows. Every row shown carries has_download set to true, and the county record also shows the harvest stamp from July 31, 2026 that lets you separate fresh records from stale ones at a glance. Ranks reflect the August 2026 review pass; a fresh pull arrives current to the day it ships.

Fifteen verified DCAT-US fields complete the core schema, confirmed against live catalog output during the August 2026 review. They identify the dataset, qualify its publisher down to government level, timestamp both publication and harvest, and flag whether a file comes attached. Record-level extras fold under additional fields on request rather than padding every delivery.

Geography - the United States at every administrative level at once: federal agencies such as HHS, CMS and the Department of Labor beside participating state, county, city, tribal and university publishers. The organization filter sizes each publisher before you commit, and the map search cuts the result list by bounding box.

Temporal - per record, because each publisher sets its own clock. King County's coverage table closes out 2009-2019 as a fixed historical panel; CMS effectuated enrollment posts month by month; last-harvested stamps on results ran into late July 2026 during the review. One result list can hold both ends.

Granularity - dataset-level catalog records, with the underlying grain varying by publisher: national aggregates in one entry, state or county series in the next, plan-month detail in a third. Plan analysis around the specific datasets the query surfaces, not the catalog layer itself.

API, files, or your warehouse. Daily, weekly, or hourly.

Pick the channel your team already runs and set the cadence - change either when the project changes. Deliveries arrive typed and keyed rather than as raw catalog dumps: the field dictionary attached, dates parsed, government-level tags normalized, and a sample cut to your health-insurance scope shipped first so validation takes minutes instead of days.

Discovery earns its keep on specific jobs:

• Enrollment analytics - assemble the CMS monthly and annual effectuated-enrollment series beside state and county coverage tables into one enrollment base; see market researchers use cases.
• Access-to-care research - pair coverage statistics with the access-to-care filings the same query surfaces, county by county.
• Uninsured-rate trend work - King County's 2009-2019 panel makes the decade-long method study trivial before scaling nationally.
• Pipeline scoping - turn a query into an ingestion roadmap: which agencies own which enrollment tables and in what formats; see developers builders use cases.
• Citation-grade sourcing - anchor stories, theses and actuarial memos to named agency publications; see journalists academics use cases.
• Diligence breadth checks - confirm what enrollment evidence exists before trusting any single vendor's version of it; see investors quants use cases.

Market researchers and consultants get the widest aperture in the industry: every publisher level searchable at once instead of portal by portal. Developers building data products get a stable index to scope ingestion roadmaps against. Journalists, academics and students get named agencies, government-level badges and citable provenance on every number. Sales and growth teams get a jurisdiction map of who publishes usable enrollment data. Investors and quant researchers get a diligence layer showing where an enrollment thesis can be checked against primary records, and competitive intelligence and product teams watch which agencies register new coverage tables first - a read on where the category is moving.

Four things worth knowing upfront.

First, this is an index, not a warehouse. Records describe datasets living on their original publishers' portals, so structure and completeness vary by publisher; the search guarantees you can find them, not that any two will match.

Second, metadata completeness is uneven. A rich entry arrives with themes, dates, distributions and harvest stamps; a lean one may carry little beyond a title and a publisher. Plan validation around the thin rows, not just the rich ones.

Third, the frontier overlaps narrower slices. Medicaid managed care queries return their own deeper shelf inside the same catalog, so route program-specific questions to the dedicated searches and keep this one for the whole-insurance sweep.

Fourth, counts drift. Hundreds of matches were observed at the August 2026 review and the number moves as agencies publish and retire records - size publishers through the organization facet rather than quoting totals. Name your jurisdictions and plans when requesting a sample and the extract arrives cut to exactly that shape.

Because a query only pays off once it is normalized, watched and routed. Datadory flattens every record to one schema, keeps all fifteen fields verified against live output, tracks harvest stamps so you learn when a publisher moves, and joins each record through to the deeper dataset it describes when you ask - instead of leaving you to reconcile seven file formats and four levels of government by hand.

Start with a sample scoped to the states, plans and publishers you care about, then browse the rest of the industry on the managed health care data hub or the best managed health care datasets ranking.