Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
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DOI：
10.1377/hlthaff.2014.0041
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发表时间：
2014-07-01
期刊：
影响因子：
9.7
通讯作者：
Escobar, Gabriel
中科院分区：
文献类型：
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作者：
Bates, David W.;Saria, Suchi;Escobar, Gabriel
美国医疗保健系统正在迅速采用电子健康记录，这将大大增加电子可用的临床数据的数量。与此同时，临床分析技术也取得了快速的进步，这些技术用于分析大量数据并从分析中收集新的见解，这是所谓的大数据的一部分。因此，使用大数据来降低美国医疗保健成本的机会前所未有。我们提出了六个用例，即关键示例，其中存在一些通过使用大数据来降低成本的最明显机会：高成本患者，再入院，分诊，失代偿（当患者病情恶化时），不良事件以及影响多器官系统的疾病的治疗优化。我们讨论了可能从临床分析中出现的见解类型，获得此类见解所需的数据类型，以及组织需要执行必要的分析并实施改善护理同时降低成本的变化的基础设施-分析，算法，注册表，评估评分，监控设备等。我们的研究结果对监管监督、解决隐私问题的方法以及对分析研究的支持具有政策意义。
The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics-techniques for analyzing large quantities of data and gleaning new insights from that analysis-which is part of what is known as big data. As a result, there are unprecedented opportunities to use big data to reduce the costs of health care in the United States. We present six use cases-that is, key examples-where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation (when a patient's condition worsens), adverse events, and treatment optimization for diseases affecting multiple organ systems. We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure-analytics, algorithms, registries, assessment scores, monitoring devices, and so forth-that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics.