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Health Insurance Coverage Rule Interpretation Corpus: Law, Policy, and Medical Guidance for Health Insurance Coverage Understanding

arXiv 2508.03718 ↗First posted 28 Jul 2025cs.CY cs.AI cs.CLcited by 0

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Abstract (arXiv)

U.S. health insurance is complex, and inadequate understanding and limited access to justice have dire implications for the most vulnerable. Advances in natural language processing present an opportunity to support efficient, case-specific understanding, and to improve access to justice and healthcare. Yet existing corpora lack context necessary for assessing even simple cases. We collect and release a corpus of reputable legal and medical text related to U.S. health insurance. We also introduce an outcome prediction task for health insurance appeals designed to support regulatory and patient self-help applications, and release a labeled benchmark for our task, and models trained on it.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 28 Jul 2025.

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9 of 17 references.

References · 9

  1. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  2. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
  3. 2021CUAD: An Expert-Annotated NLP Dataset for Legal Contract Reviewcited by 236
  4. 2023LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domaincited by 68
  5. 2023MultiLegalPile: A 689GB Multilingual Legal Corpuscited by 60
  6. 2019BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understandingcited by 16
  7. 2022BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?cited by 13
  8. 2023Legal Extractive Summarization of U.S. Court Opinionscited by 9
  9. 2022LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehensioncited by 7

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