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）：使用自然语言处理来测量和改善结肠镜检查质量。 结肠镜检查是美国结直肠癌的主要方法。然而，其在筛查中的有效性受到性能差异的限制。例如，在结肠镜检查过程中，医生在结肠镜检查过程中检测到称为腺瘤的癌症前体的速率已显示出从一位医生到另一个医师的三倍。腺瘤检测率较低的医生进行结肠镜检查的患者患有更高的结直肠癌风险。 我们的建议集中于衡量，理解和改善结肠镜检查质量。这项工作的主要创新是使用自然语言处理（NLP）来衡量结肠镜检查的质量。 NLP是一个计算机科学领域，在该领域中，计算机被训练以“读取”文本，以确定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），该数据分析了结肠镜检查和相关的病理学报告。我们的主要质量度量是腺瘤检测率，因为它是一种与大肠癌发生率有关的常见，经过验证的措施。但是，我们使用许多二级质量措施。我们将C-Qual应用于一个卫生系统中的25,000多个结肠镜检查报告，发现医师在质量指标上的表现差异很大。 在这项先前的工作的基础上，我们的目标是使用C-Qual来测量美国练习环境中的结肠镜检查质量，以了解导致结肠镜检查质量变化的原因，并提高结肠镜检查质量。在AIM 1中，我们建议使用C-Qual工具来衡量4种不同的医疗保健系统中的性能。这将是对腺瘤检测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AIM 2中，我们试图了解为什么质量有所不同。 我们将在4个医疗保健系统的提供者调查可能影响质量的因素。 我们将将这些调查结果与AIM 1评估的腺瘤检测率联系起来，并寻找关键关联。在AIM 3中，我们将使用一种新颖的反馈方法来提高质量。我们将在一个医疗保健系统中将医生随机将两种不同类型的反馈提供，并在两年期间跟踪其质量改进。 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降低结肠镜检查性能的变化。总之，这3个目标的结果与NCI的重点放在提高结直肠癌筛查的质量上一致。