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

TL;DR

This paper introduces a new corpus of legal and medical texts related to U.S. health insurance, along with an outcome prediction task and benchmark to improve understanding and access to justice using NLP.

Contribution

It provides the first comprehensive corpus with context for health insurance appeals and a new prediction task with benchmark models to aid legal and medical decision-making.

Findings

Benchmark models show promising results on health insurance outcome prediction.

The corpus enables better NLP understanding of complex legal and medical texts.

The approach supports improved access to justice and healthcare decisions.

Abstract

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.