Developing scalable algorithms to incorporate unstructured electronic health records for causal inference based on real-world data

开发可扩展的算法以合并非结构化电子健康记录，以基于真实世界数据进行因果推断

• 批准号: 10581591
• 负责人: JOSHUA K LIN
• 金额: $ 64.48万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581591
• 关键词: Address; Algorithms; Clinical; Clinical Research; Code; Cohort Studies; Comparative Effectiveness Research; Complex; Confounding Factors (Epidemiology); Consumption; Data; Data Set; Data Sources; Databases; Electronic Health Record; Elements; Empirical Research; Ensure; Environment; Evaluation; Future; Healthcare; Healthcare Systems; Influentials; Knowledge; Knowledge acquisition; Left; Link; Machine Learning; Manuals; Massachusetts; Maximum Likelihood Estimate; Medicaid; Medical; Medicare; Methods; Modeling; Names; Natural Language Processing; North Carolina; Operative Surgical Procedures; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Performance; Physicians; Probability; Property; Proxy; Randomized, Controlled Trials; Reporting; Research; Research Design; Research Personnel; Risk; Risk Factors; Semantics; Severities; Specific qualifier value; Stratification; Structure; Symptoms; System; Techniques; Testing; Texas; Text; Therapeutic; Time; Training; Treatment outcome; Validation; Work; analytical method; care delivery; comparative effectiveness study; comparison control; comparison group; computerized; cost; disease prognosis; disorder risk; eHealth; electronic health database; electronic health information; electronic health record system; evidence base; flexibility; health care service utilization; high dimensionality; improved; innovation; machine learning method; novel strategies; operation; preservation; randomized trial; research study; routine care; safety study; simulation; sound; tool; treatment choice; unstructured data

Project Summary/Abstract The routine operation of the US Healthcare system produces an abundance of electronically-stored data that captures the care of patients as it is provided in settings outside of controlled research environments. The potential for utilizing these data to inform future treatment choices and improve patient care and outcomes of all patients in the very system that generates the data is widely acknowledged. Given these key properties of the routine-care data and the abundance of electronic healthcare databases covering millions of patients, it is critical to strengthen the rigor of analyses of such data. Our group has previously developed an analytic approach to reduce bias when analyzing routine-care databases, which has proven effective in more than 50 empirical research studies across a range of topics and data sources. However, this approach currently cannot incorporate free-text information that is recorded in electronic health records, such as clinical notes and reports. This limitation has left a large amount of rich patient information underutilized for clinical research. We thus aim to adapt and refine a set of established computerized natural language processing algorithms that can identify and extract useful information from the clinical notes and reports in electronic health records and incorporate them into our validated analytical approach for balancing background risks of different comparison groups, a key step to ensure fair evaluation when comparing different therapeutic options. To test this newly integrated and augmented approach, we will implement and adapt it in simulation studies where we can evaluate and improve the performance of these new analytic methods in a controlled but realistic fashion. In addition, we will assess the performance of our new approach in 8 practical studies comparing medical or surgical treatments that are highly relevant to patients. To ensure highest level of data completeness and quality, we have linked multiple healthcare utilization (claims) databases, spanning from 2007 to 2016, with 3 electronic health records systems, including one each in Massachusetts, North Carolina, and Texas. This data will allow testing of our newly integrated approach in a variety of care delivery systems and data environments, which will be very informative for the application of our products in the real-world settings.

项目概要/摘要 美国医疗保健系统的日常运行会产生大量的电子存储数据， 捕捉在受控研究环境之外的环境中提供的患者护理。这 利用这些数据为未来的治疗选择提供信息并改善患者护理和所有结果的潜力 生成数据的系统中的患者得到了广泛认可。鉴于这些关键属性 日常护理数据和覆盖数百万患者的丰富电子医疗保健数据库至关重要 加强此类数据分析的严谨性。我们小组之前开发了一种分析方法 在分析常规护理数据库时减少偏差，这已在 50 多个实证研究中被证明是有效的 跨一系列主题和数据源的研究。然而，这种方法目前无法纳入 电子健康记录中记录的自由文本信息，例如临床记录和报告。这 由于局限性，大量丰富的患者信息未能充分利用于临床研究。因此，我们的目标是 适应和完善一套已建立的计算机自然语言处理算法，可以识别和 从电子健康记录中的临床记录和报告中提取有用信息并将其合并 进入我们经过验证的分析方法来平衡不同比较组的背景风险，这是关键一步 以确保在比较不同治疗方案时进行公平评估。为了测试这个新集成的和 增强方法，我们将在模拟研究中实施和调整它，我们可以在其中评估和改进 以受控但现实的方式执行这些新的分析方法。此外，我们将评估 我们的新方法在 8 项实际研究中的表现，比较了以下药物或手术治疗方法： 与患者高度相关。为了确保最高水平的数据完整性和质量，我们链接了多个 2007年至2016年的医疗保健利用（索赔）数据库，拥有3个电子健康记录系统， 其中包括马萨诸塞州、北卡罗来纳州和德克萨斯州各一个。这些数据将允许测试我们的新产品 各种护理服务系统和数据环境中的综合方法，这将提供非常丰富的信息 用于我们的产品在现实世界中的应用。