数字时代的安全消息远程医疗计费:超越基于时间的指标
Dong-Gil Ko1, Umberto Tachinardi2,3, Eric J Warm4
1Department of Operations, Business Analytics & Information Systems, Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 26, 2024
概括
安全消息 (SM) 远程医疗服务的计费可以通过在电子健康记录 (EHR) 数据上训练的机器学习模型来改进. 这些模型根据临床复杂性预测了计费,超越了简单的基于时间的指标.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 远程医疗服务 远程医疗服务
背景情况:
- 对于安全消息传递 (SM) 远程医疗服务的当前计费模式通常依赖于基于时间的指标.
- 这种方法可能不能准确地反映患者护理所涉及的临床复杂性和专业知识.
- 需要更复杂的计费方法.
研究的目的:
- 建议和评估中小企业远程医疗服务的替代计费模式.
- 利用机器学习 (ML) 和电子健康记录 (EHR) 审计日志数据来提高计费准确性.
- 将重点从基于时间的指标转移到临床复杂性和专业知识.
主要方法:
- 训练了八个分类ML模型,使用来自患者发起的非紧急消息的EHR审计日志数据.
- 利用混合效应建模 (MEM) 来分析计费中的各种因素的重要性.
- 使用准确度和接收器操作特征曲线 (AUC) 下的面积得分的验证模型性能.
主要成果:
- ML模型表现出强大的性能,精度和AUC得分通常超过0.85.
- MEM分析显示,知识领域显著影响了SM计费,占差异的近40%.
- 这些发现支持了ML模型对SM远程医疗计费的预测能力.
结论:
- 使用EHR审计日志数据的ML模型可以增强和预测SM远程医疗服务的计费.
- 该研究支持采用准确反映临床复杂性和专业知识的计费模型.
- 电子健康数据为开发计费系统提供了宝贵的资源,可以捕捉到远程医疗中临床工作的真正价值.
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