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Large Language Models for Efficient Mental Health Parity Oversight.

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Large language models (LLMs) show limited accuracy in identifying Mental Health Parity and Addiction Equity Act (MHPAEA) noncompliance in insurance documents. However, LLMs can expedite the review process by flagging potential issues for further investigation.

Keywords:
Computer TechnologyHealth Care ReformHealth LawLarge Language ModelsMental Health PolicyPublic Policy Issues

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Area of Science:

  • Health Law
  • Artificial Intelligence
  • Health Insurance Regulation

Background:

  • The Mental Health Parity and Addiction Equity Act (MHPAEA) mandates equal coverage for mental health and substance use disorders.
  • Ensuring compliance with MHPAEA in health insurance plan documents is complex and resource-intensive.
  • The potential of artificial intelligence, specifically large language models (LLMs), to assist in regulatory compliance is an emerging area of research.

Purpose of the Study:

  • To evaluate the efficacy of a large language model (LLM) in identifying potential noncompliance with the Mental Health Parity and Addiction Equity Act (MHPAEA) within health insurance plan documents.
  • To assess the positive predictive value (PPV) of an LLM in detecting MHPAEA violations.
  • To determine if LLMs can aid in prioritizing areas of potential noncompliance for human review.

Main Methods:

  • Analysis of primary documentation for Essential Health Benefits benchmark plans for 2026.
  • Validation of a specific LLM prompt designed to identify MHPAEA noncompliance.
  • Application of Anthropic's Claude 3.5 Sonnet LLM to assess potential violations.
  • Calculation of the LLM's positive predictive value (PPV) for identified noncompliance areas.

Main Results:

  • The LLM identified an average of 3.8 potential areas of MHPAEA noncompliance per document.
  • The average positive predictive value (PPV) for the LLM's identification of noncompliance was 49%.
  • The LLM successfully prioritized the top 10 areas of noncompliance among those accurately identified.

Conclusions:

  • Current LLMs demonstrate a relatively low positive predictive value (PPV) for regulatory oversight tasks like MHPAEA compliance checking.
  • LLMs show promise in enhancing efficiency for regulatory bodies by rapidly identifying potential noncompliance.
  • LLM-generated insights can help prioritize areas within complex documents that require closer human examination for MHPAEA adherence.