Medicaid Section 1115 Demonstration Evaluation Findings: an open dataset (Hidden Health Data)

Hidden 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 independent evaluations of Medicaid Section 1115 demonstration waivers (delivery-system reform, health-related social needs, SUD/behavioral health, family planning, and more), which report evaluation findings and recommendations. This release (v0.4.0) expands coverage from 7 to 46 states: 43,443 records from 53 source documents across 46 states: 36,201 evaluation findings, 7,242 recommendations. 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. 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.