Visualizing healthcare system dynamics in biomedical Big Data

 电子健康记录（EHR）和行政索赔数据库正在改变医学研究，使研究人员能够访问数百万患者的数据。与手动纸质病历审查相比，这些数据库将临床研究的时间和成本降低了几个数量级，使过去不可行的研究类型成为可能。然而，研究人员经常错误地将EHR和索赔数据视为简单的大型临床试验数据。然而，有重要的区别：在临床试验期间，患者信息以标准化的方式获得和记录，并检查其准确性和完整性。相比之下，EHR和索赔是观察性数据库，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，而不是患者首次患上疾病的时间。这些观察结果受到医疗保健系统动态的影响，当医生安排与他们的病人的访问，医生决定测试命令，需要记录什么代码以获得报销的程序，等忽略这个方面的数据或天真地将其视为噪音，调查人员都有可能误解真正的病人病理生理学和丢失有价值的信息内容。在先前的工作中，我们表明，在预测生存率、选择匹配的对照队列、识别健康患者和定义实验室检查的正常范围方面，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，聚焦主题领域 该提案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离的挑战，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，我们汇集了信息学和数据可视化专家，他们开发了两个广泛采用的开源软件平台，用于查询临床数据存储库（Informatics for Integrating Biology and the Bedside，i2b2）和开发模块化数据分析和可视化工具（Science of Science，Sci2）。我们将利用这些系统来执行三个具体目标：（1）创建一个可扩展的本体，用于可视化生物医学大数据的HSD维度。(2)开发交互式可视化原型，使研究人员能够研究大数据中的HSD。可视化将是简单和熟悉的调查人员，但创新的是，第一次HSD将被视为自己的信息组成部分的数据。通过将HSD放在自己的维度上，可视化将向研究人员展示其价值，并教他们如何使用它进行研究。(3)使用三个生物医学大数据源演示和评估可视化效果：来自波士顿两个医院系统的EHR数据（共700万患者）和来自安泰医疗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.