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The Strategic Value of Big Data Analytics in Health Care Policy-Making

机译:大数据分析在医疗保健政策制定中的战略价值

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This article describes how the metrics that are used to gauge acceptable versus inadequate care have spurred debates among health care administrators and scholars. Specifically, they argue that the use of readmissions as a quality-of-care metric may reduce patients' safety. Consequently, the new well-intended policies may prove ineffective, or even worse, yield disappointing results. While the discussions over the advantages and disadvantages of the new policies are based more on conjectures rather than on evidence, analytics provides a vehicle to measure the effectiveness of such overarching strategies. In this effort, the authors analyze large volumes of hospital encounters data before and after the implementation of the Patient Protection and Affordable Care Act (PPACA) to show how overlooking some aspects of a problem may lead to unexpected outcomes. The authors conclude that the feedback provided by big data analytics can be used by the government and organization policymakers to obtain a better understanding of loopholes and to propose more effective policies in prospective endeavors.
机译:本文介绍了用于衡量可接受的医疗服务与不足的医疗服务的指标如何引发了医疗保健管理人员和学者之间的争论。他们特别指出,将再入院率用作医疗质量指标可能会降低患者的安全性。因此,新的良好政策可能会导致无效或什至更糟的结果令人失望。尽管有关新政策的利弊的讨论更多地基于推测而不是证据,但分析提供了一种工具来衡量此类总体策略的有效性。在这项工作中,作者分析了《患者保护和负担得起的医疗法案》(PPACA)实施前后的大量医院遭遇数据,以表明忽视问题的某些方面可能如何导致意外结果。作者得出的结论是,政府和组织决策者可以使用大数据分析提供的反馈来更好地了解漏洞并在预期的工作中提出更有效的政策。

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