Visualizing healthcare system dynamics in biomedical Big Data

 描述(由申请人提供)：电子健康记录(EHR)和行政索赔数据库正在改变医学研究，使调查人员能够访问数百万患者的个人数据。与人工纸质图表审查相比，这些数据库将临床研究的时间和成本减少了数量级，使过去不可行的研究类型成为可能。然而，调查人员经常错误地将EHR和索赔数据简单地视为临床试验数据的大版本。然而，有重要的区别：在临床试验期间，患者信息是以标准化的方式获得和记录的，并检查其准确性和完整性。相比之下，EHR和Claims是观察性数据库，不仅反映了患者的健康状况，还反映了他们与医疗保健系统的互动。例如，与糖尿病代码相关的日期是医生做出诊断的时间，而不是患者第一次出现疾病的时间。这些观察结果受到医疗系统动态的影响--当医生安排与病人的会诊时，医生决定安排哪些测试，需要记录哪些代码才能获得手术补偿，等等。如果忽略数据的这个维度，或者天真地将其视为噪音，调查人员可能会曲解真正的患者病理生理，并丢失宝贵的信息内容。在之前的工作中，我们表明，在预测生存、选择匹配的对照队列、识别健康患者以及定义实验室测试的正常范围方面，对观察性数据库的“医疗保健系统动力学”(HSD)维度的分析实际上比患者病理生理学更有用。然而，向研究人员传达HSD的概念并帮助他们有效地使用它是困难的。因此，将重点放在主题领域 在数据可视化方面，这项建议解决了在观察数据库中将医疗保健系统动力学与病理生理学分开的挑战，以便大数据研究人员可以使用这两个维度来生成有关患者健康的新知识。为此，我们聚集了信息学和数据可视化专家，他们开发了两个被广泛采用的开源软件平台，用于查询临床数据存储库(用于整合生物学和床边的信息学，i2b2)，并开发模块化的数据分析和可视化工具(Science of Science，Science 2)。我们将利用这些系统来实现三个具体目标：(1)创建可扩展的本体，以可视化生物医学大数据的HSD维度。(2)开发原型交互可视化，使研究人员能够在大数据中研究HSD。可视化对调查人员来说将是简单和熟悉的，但创新之处在于，HSD将首次被视为数据自身的信息组成部分。通过将HSD真正放在自己的维度上，可视化将向研究人员展示其价值，并教他们如何将其用于研究。(3)使用生物医学大数据的三个来源来演示和评估可视化：来自波士顿两个医院系统的EHR数据，总计700万患者；以及来自安泰健康保险的全国索赔数据，共3400万患者。

 DESCRIPTION (provided by applicant): Electronic health records (EHR) and administrative claims databases are transforming medical research by giving investigators access to data on millions of individual patients. Compared to manual paper chart review, these databases reduce the time and cost of clinical studies by orders of magnitude, enabling types of research that were unfeasible in the past. However, investigators often incorrectly treat EHR and claims data as simply big versions of clinical trials data. Yet, there are important differences: During clinicl trials, patient information is obtained and recorded in a standardized way and checked for accuracy and completeness. In contrast, EHR and claims are observational databases, which reflect not only the health of the patients, but also their interactions with the healthcare system For example, the date associated with a code for diabetes is when the physician made the diagnosis, not when the patient first developed the disease. These observations are influenced by the dynamics of the healthcare system-when physicians schedule visits with their patients, which tests physicians decide to order, what codes need to be recorded to get reimbursed for procedures, etc. By ignoring this dimension of the data or naively treating it as noise, investigators risk both misinterpreting the true patient pathophysiology and losing valuable information content. In prior work we showed that analysis of the "healthcare system dynamics" (HSD) dimension of observational databases can actually be more useful than the patient pathophysiology in predicting survival, selecting matched control cohorts, identifying healthy patients, and defining normal ranges of laboratory tests. Yet, conveying the concept of HSD to researchers and helping them use it effectively is difficult. Therefore, focusing on the topic area of Data Visualization, this proposal addresses this challenge of separating healthcare system dynamics from pathophysiology in observational databases, so that Big Data researchers can use both dimensions to generate new knowledge about patient health. To do this, we bring together informatics and data visualization experts who developed two widely adopted open source software platforms for querying clinical data repositories (Informatics for Integrating Biology and the Bedside, i2b2) and developing modular data analysis and visualization tools (Science of Science, Sci2). We will leverage these systems to perform three Specific Aims: (1) Create an extensible ontology for visualizing the HSD dimensions of biomedical Big Data. (2) Develop a prototype interactive visualization to enable investigators to study HSD in Big Data. The visualization will be simple and familiar to investigators, but innovative in that for the firs time HSD will be treated as its own informative component of the data. By literally placing HSD on its own dimension, the visualization will show investigators its value and teach them how to use it for research. (3) Demonstrate and evaluate the visualizations using three sources of biomedical Big Data: EHR data from two hospital systems in Boston with a total of 7 million patients and nationwide claims data from Aetna health insurance with 34 million patients.