Tamang Suzanne, Milstein Arnold, Sørensen Henrik Toft, Pedersen Lars, Mackey Lester, Betterton Jean-Raymond, Janson Lucas, Shah Nigam
Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark.
BMJ Open. 2017 Jan 11;7(1):e011580. doi: 10.1136/bmjopen-2016-011580.
To compare the ability of standard versus enhanced models to predict future high-cost patients, especially those who move from a lower to the upper decile of per capita healthcare expenditures within 1 year-that is, 'cost bloomers'.
We developed alternative models to predict being in the upper decile of healthcare expenditures in year 2 of a sample, based on data from year 1. Our 6 alternative models ranged from a standard cost-prediction model with 4 variables (ie, traditional model features), to our largest enhanced model with 1053 non-traditional model features. To quantify any increases in predictive power that enhanced models achieved over standard tools, we compared the prospective predictive performance of each model.
We used the population of Western Denmark between 2004 and 2011 (2 146 801 individuals) to predict future high-cost patients and characterise high-cost patient subgroups. Using the most recent 2-year period (2010-2011) for model evaluation, our whole-population model used a cohort of 1 557 950 individuals with a full year of active residency in year 1 (2010). Our cost-bloom model excluded the 155 795 individuals who were already high cost at the population level in year 1, resulting in 1 402 155 individuals for prediction of cost bloomers in year 2 (2011).
Using unseen data from a future year, we evaluated each model's prospective predictive performance by calculating the ratio of predicted high-cost patient expenditures to the actual high-cost patient expenditures in Year 2-that is, cost capture.
Our best enhanced model achieved a 21% and 30% improvement in cost capture over a standard diagnosis-based model for predicting population-level high-cost patients and cost bloomers, respectively.
In combination with modern statistical learning methods for analysing large data sets, models enhanced with a large and diverse set of features led to better performance-especially for predicting future cost bloomers.
比较标准模型与增强模型预测未来高成本患者的能力，尤其是那些在1年内从人均医疗保健支出的下十分位数跃升至 上十分位数的患者，即“成本激增者”。
我们基于第1年的数据开发了替代模型，以预测样本第2年医疗保健支出处于上十分位数的情况。我们的6个替代模型范围从具有4个变量的标准成本预测模型（即传统模型特征）到具有1053个非传统模型特征的最大增强模型。为了量化增强模型相对于标准工具在预测能力上的任何提升，我们比较了每个模型的前瞻性预测性能。
我们使用了2004年至2011年丹麦西部的人口（2146801人）来预测未来的高成本患者并描述高成本患者亚组。使用最近的2年期间（2010 - 2011年）进行模型评估，我们的全人群模型使用了1557950名在第1年（2010年）有全年活跃居住记录的个体。我们的成本激增模型排除了在第1年人群层面已经是高成本的155795名个体，从而得到1402155名个体用于预测第2年（2011年）的成本激增者。
使用未来一年的未见过的数据，我们通过计算第2年预测的高成本患者支出与实际高成本患者支出的比率来评估每个模型的前瞻性预测性能，即成本捕获。
我们最好的增强模型在预测人群层面高成本患者和成本激增者方面，相对于基于标准诊断的模型，成本捕获分别提高了21%和30%。
结合用于分析大型数据集的现代统计学习方法，具有大量多样特征增强的模型表现更佳，尤其是在预测未来成本激增者方面。
