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Improved prediction of medical expenditures and health care utilization using an updated chronic disease score and claims data

机译:使用更新的慢性病评分和理赔数据,改善对医疗支出和医疗保健利用的预测

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Objective: To predict future medical expenditures, health care utilization, and mortality in Switzerland using an updated chronic disease score (CDS), a chronic morbidity measure based on pharmacy data. Study Design and Setting: We performed a cohort study using medical claims data from insured persons enrolled in 2009 and 2010. Patient's characteristics, chronic conditions, and health care costs from baseline were used to calculate each patient's disease score. Two-part regression models were fit to predict health care expenditures, utilization, and mortality during the following year using the score's baseline values. We calculated the proportion of explained variation for each regression model to assess their performance. Results: The CDS model, controlled for sociodemographics and health insurance plan, showed a significant improvement in explained variance of health care costs, outpatient costs, and outpatient visits in 2010. Future outpatient visits were predicted best with an R 2 of 29.2% (age group: 18-65 years) and 22.9% (65 years), and models predicted future mortality with a c-statistic of 0.8. Conclusion: The CDS showed reasonable predictive validity of future health care utilization and medical expenditure based on pharmacy dispensing data, which may support health care decision makers in the planning delivery systems and resource allocation.
机译:目的:使用更新的慢性疾病评分(CDS)来预测瑞士未来的医疗支出,医疗保健利用和死亡率,该评分基于药房数据进行的慢性发病率测量。研究设计和设置:我们使用来自2009年和2010年参加参保的人的医疗索赔数据进行了一项队列研究。使用患者的特征,慢性病和基线医疗费用来计算每个患者的疾病评分。分为两部分的回归模型适用于使用得分的基准值来预测下一年的医疗保健支出,利用率和死亡率。我们为每个回归模型计算了解释差异的比例,以评估其性能。结果:受社会人口统计学和健康保险计划控制的CDS模型显示,2010年医疗保健费用,门诊费用和门诊病人的解释差异显着改善。预测未来的门诊病人最佳R 2为29.2%(年龄)组:18-65岁)和22.9%(> 65岁),模型预测未来死亡率,c统计量为0.8。结论:CDS根据药房分配数据显示了未来医疗保健利用和医疗支出的合理预测有效性,这可以为医疗保健决策者提供计划交付系统和资源分配方面的支持。

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