烟草治疗实时自动计费:开发和验证可扩展的机器学习方法
Derek J Baughman1, Layth Qassem1, Lina Sulieman1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
JAMIA open
|June 13, 2025
概括
一个自动化医疗计费原型CigStopper使用机器学习准确识别烟草戒烟护理代码. 该系统简化了计费,减少了管理任务,并提高了医疗保健实践的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 手动医疗计费用于戒烟咨询是行政繁重的.
- 账单中的不准确性可能会阻碍提供必要的戒烟服务.
研究的目的:
- 开发CigStopper,用于自动识别符合条件的戒烟护理代码的原型.
- 减少管理工作量,提高医疗保健中的计费准确性.
主要方法:
- 使用ChatGPT提示工程生成合成临床笔记,用于CPT代码99406/99407.
- 在临床医生注释的数据上训练了机器学习模型,包括决策树和随机森林.
- 使用PRC AUC和F1分数评估模型性能,在未识别的笔记上进行概括性测试.
主要成果:
- 基于树的模型,特别是决策树和随机森林,表现出卓越的性能.
- 这些模型的平均PRC AUC为0.857,F1得分为0.835.
- 一般化测试证实了基于树的模型在真实数据上的有效性.
结论:
- CigStopper显示了优化计费流程和支持戒烟护理的潜力.
- 机器学习方法为自动化计费任务提供了基础,提高了效率.
- 自动化这些任务可以简化医疗保健实践操作,提高财务可持续性.
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