Measuring and Improving Colonoscopy Quality Using Natural Language Processing

使用自然语言处理测量和提高结肠镜检查质量

• 批准号: 8752179
• 负责人: Ateev Mehrotra
• 金额: $ 63.48万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-26 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8752179
• 关键词: Address; Affect; Cancer Etiology; Cecum; Cessation of life; Colon; Colonoscopy; Colorectal; Colorectal Cancer; Computer software; Computers; Data; Detection; Effectiveness; Environment; Feedback; Future; Gastroenterology; Geographic Locations; Goals; Gold; Health system; Healthcare; Healthcare Systems; Hospitals; Incidence; Institution; Knowledge; Link; Malignant Neoplasms; Manuals; Measurement; Measures; Methods; Metric; Monitor; Names; Natural Language Processing; Pathology Report; Patients; Performance; Physicians; Provider; Randomized; Reading; Recommendation; Records; Reporting; Research; Risk; Science; Societies; Sum; Surveys; System; Testing; Text; Training; United States National Institutes of Health; Variant; Work; adenoma; base; colorectal cancer screening; computer science; cost effective; high risk; improved; innovation; medical specialties; novel; payment; peer; screening; symposium; tool

DESCRIPTION (provided by applicant): Measuring and Improving Colonoscopy Quality Using Natural Language Processing. Colonoscopy is the predominant method for screening for colorectal cancer in the US. Yet, its effectiveness in screening is limited by variation in performance. For example, the rate at which physicians detect cancer precursors called adenomas during a colonoscopy has been shown to vary three-fold from one physician to another. A patient whose colonoscopy is performed by a physician with a low adenoma detection rate has a higher risk of subsequent colorectal cancer. Our proposal centers on measuring, understanding, and improving colonoscopy quality. The major innovation of this work is to use natural language processing (NLP) to measure the quality of colonoscopy. NLP is a field of computer science in which a computer is trained to "read" text to identify relevant data We developed and validated the first NLP-based computer software application (C-QUAL) that analyzes colonoscopy and associated pathology reports. Our primary quality measure is adenoma detection rate because it is a common, validated measure that is linked to colorectal cancer incidence. However, we use a number of secondary quality measures. We applied C-QUAL to over 25,000 colonoscopy reports in one health system and found large variation in physician's performance on the quality measures. Building on this prior work, our goal is to use C-QUAL to measure colonoscopy quality across a spectrum of US practice environments, to understand what drives variation in colonoscopy quality, and to improve colonoscopy quality. In Aim 1, we propose to use the C-QUAL tool to measure performance in 4 diverse health care systems. This will be one of the largest assessments of the variation in adenoma detection rates and will span different geographic regions, payment systems, and practice settings. In Aim 2, we seek to understand why there is variation in quality. We will survey providers at the 4 health care systems about factors that might affect quality. We will link those survey results to the adenoma detection rates assessed in Aim 1 and look for key associations. In Aim 3, we will use a novel feedback method to improve quality. We will randomize physicians in one health care system to two different types of feedback and track their quality improvement over a two-year period. Our proposal is the first to use this innovative method to measure colonoscopy quality and to use the quality scores to decrease the variation in colonoscopy performance. Together the results of the 3 aims are consistent with the NCI's focus on improving the quality of colorectal cancer screening.

描述（由申请人提供）：使用自然语言处理测量和提高结肠镜检查质量。 结肠镜检查是美国筛查结直肠癌的主要方法。然而，其筛查效果受到性能差异的限制。例如，医生在结肠镜检查期间发现称为腺瘤的癌症前体的比率已被证明在不同医生之间存在三倍的差异。由腺瘤检出率较低的医生进行结肠镜检查的患者，其后续患结直肠癌的风险较高。 我们的建议集中于测量、理解和提高结肠镜检查质量。这项工作的主要创新点是利用自然语言处理（NLP）来衡量结肠镜检查的质量。 NLP 是计算机科学的一个领域，其中计算机被训练为“阅读”文本以识别相关数据。我们开发并验证了第一个基于 NLP 的计算机软件应用程序 (C-QUAL)，用于分析结肠镜检查和相关病理报告。我们的主要质量指标是腺瘤检出率，因为它是与结直肠癌发病率相关的常见且经过验证的指标。然而，我们使用了一些次要的质量衡量标准。我们将 C-QUAL 应用到一个卫生系统的 25,000 多份结肠镜检查报告中，发现医生在质量衡量方面的表现存在很大差异。 在先前工作的基础上，我们的目标是使用 C-QUAL 来衡量美国各种实践环境中的结肠镜检查质量，了解导致结肠镜检查质量变化的因素，并提高结肠镜检查质量。在目标 1 中，我们建议使用 C-QUAL 工具来衡量 4 个不同医疗保健系统的绩效。这将是对腺瘤检出率变化的最大评估之一，并将跨越不同的地理区域、支付系统和实践环境。在目标 2 中，我们试图了解质量差异的原因。 我们将对 4 个医疗保健系统的提供者进行调查，了解可能影响质量的因素。 我们将把这些调查结果与目标 1 中评估的腺瘤检出率联系起来，并寻找关键关联。在目标 3 中，我们将使用一种新颖的反馈方法来提高质量。我们将把一个医疗保健系统中的医生随机分配给两种不同类型的反馈，并在两年内跟踪他们的质量改进情况。 我们的建议是第一个使用这种创新方法来测量结肠镜检查质量并使用质量评分来减少结肠镜检查性能的变化。这 3 个目标的结果与 NCI 提高结直肠癌筛查质量的重点是一致的。