大型语言模型为有效的心理健康平价监督
1Kennedy Forum, Brigantine, NJ.
Psychiatric services (Washington, D.C.)
|December 11, 2025
概括
大型语言模型 (LLM) 在识别保险文件中的心理健康平价和成公平法案 (MHPAEA) 违规时的准确性有限. 然而,LLMs可以通过标记潜在问题进行进一步调查来加快审查过程.
科学领域:
- 卫生法 法律 卫生法
- 人工智能的人工智能
- 医疗保险条例 医疗保险条例
背景情况:
- 心理健康平等和成公平法案 (MHPAEA) 要求对心理健康和物质使用障碍的平等覆盖.
- 在医疗保险计划文件中确保遵守MHPAEA是复杂且资源密集的.
- 人工智能,特别是大型语言模型 (LLM) 的潜力,以协助监管合规是一个新兴的研究领域.
研究的目的:
- 评估大型语言模型 (LLM) 在医疗保险计划文件中识别潜在的违反心理健康平等和成公平法案 (MHPAEA) 的有效性.
- 评估LLM在检测MHPAEA违规行为的积极预测值 (PPV).
- 确定LLM是否可以帮助优先考虑人类审查的潜在不合规领域.
主要方法:
- 对2026年基本健康福利基准计划的初级文件分析.
- 一个特定的LLM提示符的验证,旨在识别MHPAEA不合规.
- 应用Anthropic的克劳德3.5索内特法学士学位来评估潜在的违规行为.
- 计算LLM的积极预测值 (PPV) 对于已确定的不合规领域.
主要成果:
- 该LLM平均每份文件发现3.8个MHPAEA不合规的潜在领域.
- 对于LLM识别不合规的平均积极预测值 (PPV) 为49%.
- 该法学士课程成功地优先考虑了那些精确识别的十大违规领域.
结论:
- 当前的LLM在MHPAEA合规检查等监管监督任务中显示出相对较低的积极预测值 (PPV).
- 通过快速识别潜在的不合规行为,LLM在提高监管机构的效率方面表现有前途.
- 通过LLM生成的见解可以帮助优先考虑复杂文档中的领域,这些领域需要对MHPAEA遵守进行更密切的人类检查.
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