30天住院再入院的预测分析

IF 1.3 Q3 COMPUTER SCIENCE, THEORY & METHODS
Lu Xiong, Tingting Sun, Randall G. Green
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引用次数: 0

摘要

30天再入院率是指最后一次出院后30天内再入院的患者的百分比。再入院率高的医院将不得不向医疗保险和医疗补助服务中心(CMS)支付罚款。预测再入院率可以帮助医院更好地配置资源,降低再入院率。在这项研究中,我们使用了北卡罗来纳州一家医院2011年至2016年的数据集,其中包括71724例住院病例。我们的目标是提供一个预测模型,可以帮助相关实体,包括医院,健康保险精算师和医疗保险,以降低成本,提高医疗保健系统的临床结果。我们使用R语言处理数据,并应用聚类、广义线性模型(GLM)和LASSO回归来预测30天的再入院情况。结果表明,患者的年龄是影响再入院的最重要因素。这项研究可以帮助医院和CMS减少昂贵的再入院费用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive analytics for 30-day hospital readmissions
The 30-day hospital readmission rate is the percentage of patients who are readmitted within 30 days after the last hospital discharge. Hospitals with high readmission rates would have to pay penalties to the Centers for Medicare & Medicaid Services (CMS). Predicting the readmissions can help the hospital better allocate its resources to reduce the readmission rate. In this research, we use a data set from a hospital in North Carolina during the years from 2011 to 2016, including 71724 hospital admissions. We aim to provide a predictive model that can be helpful for related entities including hospitals, health insurance actuaries, and Medicare to reduce the cost and improve the clinical outcome of the healthcare system. We used R to process data and applied clustering, generalized linear model (GLM) and LASSO regressions to predict the 30-day readmissions. It turns out that the patient's age is the most important factor impacting hospital readmission. This research can help hospitals and CMS reduce costly readmissions.
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CiteScore
1.50
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