Effect of e-Health on Medical Expenditures of Outpatients with Lifestyle-Related Diseases
Effect of e-Health on Medical Expenditures of Outpatients with Lifestyle-Related Diseases
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DOI：
10.1089/tmj.2011.0019
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发表时间：
2011-10-01
影响因子：
4.7
通讯作者：
Tsuji, Masatsugu
中科院分区：
文献类型：
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作者：
Minetaki, Kazunori;Akematsu, Yuji;Tsuji, Masatsugu
本文利用2002 - 2006年福岛县西会镇约400名居民的医疗支出面板数据，分析了电子医疗对医疗支出的影响。西会镇系统于1994年推出，目前仍在成功运行，是日本运行时间最长的电子卫生系统之一。镇办事处保存一份国家健康保险系统支付的医疗支出收据登记册，并提供电子保健用户的数据，以便区分电子保健的用户和非用户及其各自的费用。在这里，我们专注于患有生活方式相关疾病的患者，如高血压、糖尿病、中风、心力衰竭等。本文假设电子保健通过两种机制减少医疗支出，即减少差旅费用和防止症状恶化。前者意味着电子卫生监测允许患者在家访问医疗机构的频率降低，后者意味着电子卫生用户所经历的症状比非用户所经历的症状要轻。我们分别称之为旅行成本效应和机会成本效应。慢性疾病往往不会单独发生，许多患者不止一种；例如，患有高血压或糖尿病的患者也可能同时患有心脏病。这种条件的多样性阻碍了成本分析。在方法学问题中，最近的一些实证卫生分析侧重于解释变量的内生问题。在这里，我们使用广义方法矩（GMM）系统解决了这一问题，该系统不仅可以处理解释变量的内生问题，还可以处理变量之间的动态关系，这些变量是由于生活方式相关疾病对患者的慢性时间滞后效应而产生的。我们还研究了第二个重要的方法学问题，即门诊医疗支出与电子医疗之间的反向相关性，并考虑了抽样偏差。我们的结论是，通过系统GMM对内生性的控制证实了门诊医疗支出与电子医疗之间的关系显示出因果关系，而不是简单的相关性，并且电子医疗使用、电子医疗使用的持续时间和电子医疗使用的频率可以减少门诊因生活方式相关疾病的医疗支出。
We analyzed the effect of e-health on medical expenditures in Nishi-aizu Town, Fukushima Prefecture, Japan, using panel data of medical expenditures for about 400 residents from 2002 to 2006. The Nishi-aizu Town system was introduced in 1994 and is still successfully operating as one of the longest running implementations of e-health in Japan. The town office maintains a register of receipts for medical expenditures paid by the National Health Insurance system and provides data on e-health users, allowing users and nonusers of e-health and their respective costs to be distinguished. Here, we focus on patients with lifestyle-related diseases such as high blood pressure, diabetes, stroke, heart failure, etc. This article postulates that e-health reduces medical expenditures via two mechanisms, decreasing travel expenses and preventing symptoms from worsening. The former implies that e-health monitoring allows patients at home to visit medical institutions less frequently, and the latter that the symptoms experienced by e-health users are less severe than those experienced by nonusers. We termed these the travel cost effect and opportunity cost effect, respectively. Chronic conditions tend not to occur singly, and many patients have more than one; for example, patients with high blood pressure or diabetes also likely have heart disease at the same time. This multiplicity of conditions hampers cost analysis. Among methodological issues, a number of recent empirical health analyses have focused on the endogenous problem of explanatory variables. Here, we solved this problem using the generalized method moments (GMM) system, which allows treatment of not only the endogenous problem of explanatory variables but also the dynamic relationship among variables, which arise due to the chronic time-lagged effect of lifestyle-related diseases on patients. We also examined a second important methodological problem related to reverse correlation between the medical expenditures of an outpatient and e-health and took sampling biases into consideration. We concluded that this control of endogeneity through system GMM confirms that the relationship between the medical expenditures of an outpatient and e-health shows causation rather than simple correlation and that e-health use, duration of e-health use, and frequency of e-health use can reduce outpatient medical expenditures for lifestyle-related diseases.