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A Platform based on Multiple Regression to Estimate the Effect of in-Hospital Events on Total Charges

机译:基于多元回归的平台,估计住院事件对总费用的影响

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Recently hospitals struggle to control the cost of care while maintaining optimal outcomes. To respond to this challenge, we developed an interactive web platform which utilizes a multiple linear regression model. The user can create and furthermore alter a clinical scenario, during a patient hospitalization to see the dynamic prediction of total charges, via interactive sessions. The R2 value of our model is 0.655 and the standard error of the estimate is 38,732. Predictors with high coefficient scores include the cardioverter implantation, mechanical ventilation, implant of pulsation balloon and hospital-acquired conditions such as staphylococcus aureus septicemia. Our findings indicate that (a) integration of predictive models into clinical decision support systems is feasible and use of regression methods provide direct feedback on the effect of any clinical practice to the in-hospital charges (b) medical claims data can provide a useful estimation of the in-hospital charges (c) hospital acquired conditions have significant impact on the in-hospital charges.
机译:最近,医院在保持最佳结果的同时努力控制护理成本。为了应对这一挑战,我们开发了一个利用多元线性回归模型的交互式Web平台。用户可以在患者住院期间创建并进一步更改临床方案,以通过交互会话查看总费用的动态预测。我们模型的R2值为0.655,估计的标准误为38,732。具有高系数得分的预测因子包括心脏复律器植入,机械通气,搏动球囊植入和医院获得性疾病,例如金黄色葡萄球菌败血症。我们的发现表明(a)将预测模型集成到临床决策支持系统中是可行的,并且使用回归方法可以直接反馈任何临床实践对住院费用的影响(b)医疗索赔数据可以提供有用的估计(c)医院获得的条件对医院收费产生重大影响。

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