用于分析和识别医疗保险不当支付的生成AI,按提供者类型和HCPC代码划分
1Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-ku, Tokyo 135-8181, Japan.
Exploratory research in clinical and social pharmacy
|December 29, 2023
概括
生成型人工智能有助于根据提供者类型和HCPC代码识别医疗保险不当支付. 这种分析有助于改进系统,以减少80.57亿美元的支付错误.
科学领域:
- 卫生经济学 卫生经济学
- 医疗保健中的人工智能
- 医疗保健服务研究 医疗服务研究
背景情况:
- 医疗保险服务费 (FFS) 面临着由于不当支付而造成的重大财务损失.
- 2022年报告确定了805.7亿美元的不当支付,错误率为15.62%.
- 确定这些错误的具体驱动因素对于有效的干预至关重要.
研究的目的:
- 为了利用生成人工智能来分析医疗保险FFS不当支付.
- 为了确定提供者类型和医疗保健共同程序编码系统 (HCPC) 代码与支付错误最相关.
- 为研究人员开发可访问的工具来分析医疗保险支付数据.
主要方法:
- 利用生成性AI创建用于数据分析的Python代码.
- 开发了医疗保险不当支付 (2010-2022) 的时间序列趋势图.
- 根据提供者类型和HCPC代码计算的支付错误,使用合并的数据集.
主要成果:
- 确定了与大量不当医疗保险支付相关的特定提供者类型和HCPC代码.
- 为初学者程序员生成了用户友好的Python代码,用于分析复杂的医疗保健数据集.
- 提供了对导致支付错误的系统弱点的见解.
结论:
- 生成型人工智能为分析医疗保险不当支付提供了一种强大,易于使用的方法.
- 调查结果表明,需要有针对性的系统改进,以减少财务浪费.
- 赋予非程序员人工智能工具的能力可以增强未来的医疗数据研究.
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