论文标题
使用电子保健记录对医疗保健成本进行建模的高通量方法
High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records
论文作者
论文摘要
准确估计医疗保健费用对于医疗保健系统至关重要，即与保险公司就患者护理费用的覆盖范围进行计划和有效谈判。估算医疗保健成本的更高准确性将为卫生系统和保险公司提供互惠互利，这些保险公司通过更好地使支付模式与患者护理成本保持一致。这项研究介绍了一种可推广的机器学习方法的结果，该方法可预测从40年的数据中构建的医疗事件，从威斯康星州的Marshfield Clinic提供，> 860,000名患者> 6,700种处方药。已经发现，与预测单个药物医师处方的类似研究相比，使用这种方法构建的模型表现良好。除了为大型医疗保健系统中的所有药物提供全面的预测模型外，该研究中采用的方法从潜在的适用性到其他各种医疗事件都受益。
Accurate estimation of healthcare costs is crucial for healthcare systems to plan and effectively negotiate with insurance companies regarding the coverage of patient-care costs. Greater accuracy in estimating healthcare costs would provide mutual benefit for both health systems and the insurers that support these systems by better aligning payment models with patient-care costs. This study presents the results of a generalizable machine learning approach to predicting medical events built from 40 years of data from >860,000 patients pertaining to >6,700 prescription medications, courtesy of Marshfield Clinic in Wisconsin. It was found that models built using this approach performed well when compared to similar studies predicting physician prescriptions of individual medications. In addition to providing a comprehensive predictive model for all drugs in a large healthcare system, the approach taken in this research benefits from potential applicability to a wide variety of other medical events.
