Definitive Healthcare Hospital, IDN and Post-Acute Facility Intelligence

It is the broadest single view of the US care continuum in this catalog, and the only one pairing claims depth with named human beings. Eight universes share one keyed structure: more than 9,000 hospital and health system profiles through HospitalView, 100K+ post-acute organizations (nursing, assisted living, home health, hospice) through LongTermCareView, 22K+ imaging centers, 14K+ ambulatory surgery centers, 60K+ clinics and FQHCs, 130K+ physician groups, 4.5K+ ACOs, HIEs and payers, with billions of de-identified all-payor claims underneath.

Hospital profiles span nine institution types - short-term acute care, long-term acute care, critical access, VA, pediatric, Department of Defense, psychiatric, rehabilitation and religious non-medical - and carry the org chart and the operating reality on the same record: affiliations and parent/child system structure, financial performance, staffing, physicians, quality metrics, technologies installed by category, vendor and product, purchasing information, referrals, episodes of care, study activity, population data, Medicare-certified bed counts and operating room square footage. Medicare claims ride along broken out by DRG, diagnoses, procedures, revenue centers, cost and readmission measures, and claim origination/destination.

Two things separate this from a directory. Claims enrichment turns each facility into measurable volume, and every profile names its executives - department, title, email, phone, LinkedIn - scored for likelihood of technology investment. Universe counts drift between marketing pages (9,000+ versus 9,300+ hospitals); figures here were read at the August 2026 review. Get a sample of this dataset cut to the segments you sell into.

Shapes first, cells second. The vendor publishes no public sample profiles, and Datadory does not print numbers it cannot stand behind - so below is the documented anatomy every record carries, with placeholders that resolve when your sample is cut:

# hospital / IDN profile -- one row per facility or health system
facility_profile_id           : <atlas profile identifier>
affiliations_and_partnerships : <system components; parent/child
                                relationships across the network>
medicare_certified_bed_count  : <certified beds on the profile>
operating_room_square_footage : <OR square footage>
technology_installations      : <category > vendor > product>
drg_claims_volume             : <inpatient Medicare claims, split by
                                DRG, diagnoses and procedures>
quality_metrics               : <quality measures on the profile>
predicted_buyer_score         : <likelihood-of-investment score>

# executive_contacts -- carried on the same profile
<name> | <department> | <title> | <position level>
<email> | <phone number> | <linkedin profile>

Read it as two joined layers. The profile layer answers scale and structure: how many certified beds, how much operating room square footage, which system owns whom, which technologies sit installed at vendor and product level, and how much inpatient Medicare volume flows through each DRG. The executive layer answers the human question: who runs the place, at what level, reachable where.

Multiply the pair across 9,000-plus hospitals, 100K-plus post-acute organizations and the adjacent universes, and the same file works as a prospecting panel, a referral-network map or a market-sizing denominator - the grouping key decides.

Nine attribute groups define the core profile. Definitions below are reconstructed from the platform's own feature documentation rather than transcribed from a published schema document - the vendor ships no public data dictionary - so treat them as verified structure carrying inferred labels until a sample confirms cell-level typing.

Everything deeper folds under additional fields on request rather than being promised blind: the financial, staffing and physician detail on each profile, the Medicare revenue-center, readmission and origination/destination splits beneath the DRG breakouts, purchasing and news activity, population context, study activity, and the six adjacent universes from post-acute to physician groups. Name the attributes you benchmark against when you request the sample and the extract arrives carrying exactly those columns.

Geography - United States throughout, across every tracked universe: hospitals and IDNs, post-acute organizations, imaging centers, ambulatory surgery centers, clinics and FQHCs, physician groupes and ACO/HIE/payer profiles. Claims products extend the footprint into prescription detail.

Temporal - maintained current-state profiles rather than an archived time series. Each record describes the facility as the platform holds it at delivery, with no deep backfile of earlier profile states. Universe counts quoted anywhere should carry an observation date; the figures on this page were read at the August 2026 review.

Granularity - one profile per facility or organization at the top, with network, member and episode-level elements beneath for episodic care. The analysis unit is therefore configurable: roll profiles up through parent/child affiliations to read a whole IDN or system, or drill to episode elements for care-utilization questions.

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

You pick the channel and set the cadence, and change either without re-integrating. Every delivery ships typed rows keyed so profiles join cleanly to their executive records, the full field dictionary attached, and sample rows for validation before anything recurring starts. Name the universes, the geography and the profile attributes you need when you request the sample, and the first cut arrives already filtered to them.

• Provider prospecting and lead enrichment - sales teams build account lists across hospitals, IDNs, post-acute organizations and surgery centers with named executives attached, then carve territories on system affiliation rather than raw address. See lead enrichment use cases and the sales growth teams use cases page.
• Market sizing for market-entry work - researchers size the post-acute, imaging-center and ASC landscape from documented universes (100K+ post-acute orgs, 22K+ imaging centers, 14K+ ASCs) instead of estimating them. Worked examples live on our market sizing use case page.
• Referral network mapping - discharge flows become mappable once the post-acute universe is keyed: 100K-plus organizations and 14K-plus surgery centers give the destination layer behind every discharging hospital.
• Private-market diligence on provider rollups - investors map an operator's facilities, affiliations and executive bench before pricing a deal; see investors and quants use cases.
• Competitive install tracking - watch which systems rivals partner with by following technology installations and affiliation changes across their footprints; see competitor tracking use cases.
• Provider master-data enrichment - claims-enriched profiles serve as a reference layer for entity resolution and internal CRM hygiene; see data scientists use cases.

Sales and growth teams and market researchers and consultants rate it maximum relevance - three out of three, the top of this industry's eight tagged personas. One group monetizes the executive records and affiliation maps directly; the other leans on the documented universe counts as defensible denominators.

Investors and quant researchers and competitive intelligence and product teams rate it two: diligence mapping on provider rollups, and tracking rival facilities' technology installs and partnerships - see competitive intel product teams use cases.

Data scientists and ML engineers and journalists, academics and students rate it one - a reference layer for provider master data, and background context rather than a citable primary figure. Developers and data-product builders carry no strong tag on this slice: the payoff lands after delivery, when typed profile rows power CRM enrichment and internal tools - see developers builders use cases.

Four things, stated upfront.

First, the dictionary is reconstructed, not published. The vendor releases no public data dictionary, so the nine core definitions come from its own feature documentation and stay labeled inferred until a sample confirms cell-level typing.

Second, universe counts move. The hospital figure reads 9,000+ on some pages and 9,300+ on others; every count on this page was read at the August 2026 review, and your sample states its own totals.

Third, this is a current-state view, not an archive. Profiles describe facilities as held at delivery, so longitudinal designs need successive captures - precisely what a scheduled cadence builds from day one.

Fourth, Datadory scores it 5/10 against a 7.81 catalog average: docked for undocumented fields and absent public sample rows, credited for breadth nothing else in this industry matches - eight universes, DRG-level claims and named executives under one keyed roof.