Medicaid Managed-Care External Quality Review (EQR) Findings: a national 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 U.S. state Medicaid/CHIP managed-care External Quality Review (EQR) annual technical reports (42 CFR 438.350-364): validated performance measures, performance improvement projects (PIPs), and compliance findings. This release (v0.4.0) expands coverage from 32 to 43 states: 25,190 records from 53 source documents across 43 states/jurisdictions: 11,733 quality measures, 10,804 compliance findings, 2,653 performance improvement projects. Every record carries full provenance (source-document SHA-256, page, model, confidence) and a quality/trust score; nothing is imputed or dropped — hard constraint violations are flagged (qa_status=fail), not removed. These records are AI-extracted. A human-validation study (field accuracy, inter-rater agreement, calibration) is underway; a validated release will follow as a new version. Treat as preliminary 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.