Hospital Community Health Needs Assessment (CHNA) Findings: an open dataset (Dark Health Data)

Dark Health Data turns buried public-record health documents (PDFs) into research-ready, provenance-stamped datasets via large language model extraction (Anthropic Claude claude-haiku-4-5) with a verification layer: source grounding, symbolic + Logical Neural Network-inspired neurosymbolic constraints, a heterogeneous ensemble, and a conformal selective-acceptance gate. Source documents are U.S. non-profit hospital Community Health Needs Assessments (CHNAs), required triennially under IRC 501(r)(3), which identify and prioritize community health needs and set implementation strategies. This release (v0.4.0) is a national crawl expanding coverage from ~96 to thousands of hospitals: 277,273 records from 694 source documents naming 3,410 hospitals across the United States (a system CHNA often names several member hospitals): 207,771 identified community health needs, 69,502 implementation strategies. Every record carries full provenance (source-document SHA-256, page, model, confidence) and a quality/trust score; nothing is imputed or dropped. These records are AI-extracted and not yet independently validated — treat as preliminary, filter on the trust score, and verify values against the linked source pages. Note: the free-text state field is as-extracted and not yet normalized. Derived entirely from public-record documents; no protected health information. Code (Apache-2.0): github.com/sanjaybasu/dark-health-data. Data license: CC0-1.0.