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The evaluation model of a country's health care system based on AHP and entropy weight method

机译:基于层次分析法和熵权法的国家卫生系统评价模型

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摘要

A fair and reasonable evaluation method of a country's health care system (HCS) is necessary. With the data provided by World Health Organization (WHO), analytic hierarchy process (AHP) was applied to establish an AHP structure including 4 modules and 13 metrics. Then, the weight of every metric was obtained by using the entropy weight method. Following the basic model, a deeper analysis was done to make the evaluation simpler and more feasible. A new model named projection pursuit principal components analysis (PPPCA) was developed to select the most important and viable metrics. Using the genetic algorithm, five major metrics including health life expectancy at birth, birth attended by midwives, nurse density, expenditure shared by social security and alcohol consumption were screened out. Next, 105 countries' HCSs were evaluated with the 5 metrics and 13 metrics got in previous model respectively. The rankings got with the two methods were almost the same. Applying our models, two pairs of countries were compared. The results showed that Japan has a more effective HCS than the United States and the United States is better than India. Finally, by computer simulations to the HCS of the United States, several practical measures were provided to improve the overall quality of the system.
机译:一个国家的卫生保健系统(HCS)的公正,合理的评估方法是必要的。根据世界卫生组织(WHO)提供的数据,应用层次分析法(AHP)建立包括4个模块和13个指标的AHP结构。然后,使用熵权重法获得每个度量的权重。根据基本模型,进行了更深入的分析,以使评估更简单,更可行。开发了一种名为投影追踪主成分分析(PPPCA)的新模型,以选择最重要和可行的指标。使用遗传算法,筛选出了五个主要指标,包括出生时的健康预期寿命,助产士的分娩,护士密度,社会保障分担的支出和饮酒。接下来,分别使用先前模型中获得的5个指标和13个指标对105个国家的HCS进行了评估。两种方法获得的排名几乎相同。应用我们的模型,比较了两个国家。结果表明,日本的HCS比美国更有效,美国比印度更好。最后,通过对美国HCS的计算机模拟,提供了一些实际措施来提高系统的整体质量。

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