Hospitals have the richest public data of any vertical due to CMS reporting requirements. HCAHPS scores, quality ratings, readmission rates, infection data, and cost reports are all publicly available and machine-readable. Here is every data point AI looks for when evaluating a hospital, where that data lives, and what it can already find.
When an AI system decides which Hospital company to recommend, it assembles evidence across every category below. The more complete and verifiable the data, the more confident the recommendation.
The core operational data that defines hospital scale, efficiency, and financial health. While CMS publishes quality and cost data, internal operating metrics like admissions volume, occupancy, and payer mix are rarely available in structured form outside the hospital itself.
CMS publishes extensive hospital quality data through Care Compare, including patient experience, readmissions, infections, and mortality. These are among the most structured, machine-readable datasets available for any business vertical. AI systems already ingest this data directly.
Hospitals vary widely in the clinical services they offer. A query like "Level I trauma center near me" or "hospital with NICU in Austin" requires precise service line data that a generic hospital listing cannot answer. AI needs structured service line information to match patient needs to facility capabilities.
Where a hospital actually draws patients from is measurable through discharge data and patient origin studies. AI systems cross-reference claimed service areas against CMS data and referral patterns to understand true community reach.
Hospital accreditation is a prerequisite for Medicare participation and a foundational quality signal. Beyond base accreditation, disease-specific and program-specific certifications indicate clinical depth. All accreditation statuses are publicly verifiable.
The composition, size, and qualifications of a hospital's medical staff are key indicators of clinical capability. Teaching hospital status, specialty distribution, and nursing ratios all affect care quality and are increasingly available to AI systems.
Hospital financial data is more publicly available than in almost any other industry due to CMS reporting requirements and price transparency mandates. AI systems can access cost reports, chargemasters, and network participation data directly.
Hospitals have more structured reputation data than most verticals due to national ranking programs, safety grades, and CMS quality ratings. AI systems use these alongside patient reviews to build a multi-dimensional reputation picture.
Foundational identity and structural data that AI uses to classify, compare, and locate hospitals. Hospital type, ownership, system affiliation, and bed count are among the most important classification fields for AI evaluation.
The performance and customer experience data AI values most already exists in software these businesses use every day. It is locked inside these platforms and not published anywhere AI can access it.
Without access to a business's own systems, this is all AI has to work with. These are the public sources it checks, grouped by type.
A TrustRecord connects to your systems of record, extracts verified data that proves your performance, experience, and credibility, and publishes it in a format AI systems can read, verify, and cite.