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: 10372142
• 负责人: JOSHUA K LIN
• 金额: $ 55.4万
• 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/10372142
• 关键词: Address; Algorithms; Clinical; Clinical Research; Code; Cohort Studies; Comparative Effectiveness Research; Complex; Confounding Factors (Epidemiology); Consumption; Data; Data Set; Data Sources; Data Store; Databases; Electronic Health Record; Elements; Empirical Research; Ensure; Environment; Equilibrium; Evaluation; Future; Gold; Healthcare; Healthcare Systems; Influentials; Knowledge; Knowledge acquisition; Left; Link; Machine Learning; Manuals; Massachusetts; Medical; Medicare/Medicaid; Methods; Modeling; Names; Natural Language Processing; North Carolina; Operative Surgical Procedures; Patient Care; 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; Weight; Work; analytical method; base; care delivery; care outcomes; comparative effectiveness study; comparison group; computerized; cost; disease prognosis; disorder risk; 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.

项目摘要/摘要 美国医疗保健系统的常规操作产生了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对患者的护理。这个 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ALL结果的可能性 生成数据的系统中的患者得到了广泛的认可。在给定了 常规医疗数据和覆盖数百万患者的电子医疗数据库的丰富，至关重要 加强对这类数据的严谨分析。我们小组之前已经开发了一种分析方法来 在分析常规护理数据库时减少偏见，这已在50多项经验中被证明有效 一系列主题和数据来源的研究性研究。然而，这种方法目前不能将 记录在电子健康记录中的自由文本信息，如临床记录和报告。这 局限性使得大量丰富的患者信息没有被用于临床研究。因此，我们的目标是 调整和改进一套成熟的计算机化自然语言处理算法，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的信息并将其合并 进入我们经过验证的分析方法，以平衡不同比较组的背景风险，这是关键的一步 以确保在比较不同的治疗方案时进行公正的评估。为了测试这个新集成的和 增强的方法，我们将在模拟研究中实施和调整它，在那里我们可以评估和改进 这些新的分析方法在可控但现实的方式下的表现。此外，我们还将评估 我们的新方法在8个实际研究中的表现，比较了内科或外科治疗 与患者高度相关。为了确保最高级别的数据完整性和质量，我们将多个 保健利用(索赔)数据库，从2007年到2016年，有3个电子健康记录系统， 包括马萨诸塞州、北卡罗来纳州和德克萨斯州各一名。这些数据将允许测试我们的新产品 在各种护理提供系统和数据环境中采用集成方法，这将是非常有用的 用于我们的产品在现实世界中的应用。