Departments of Medicine and Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
Gastrointest Endosc. 2012 Jun;75(6):1233-9.e14. doi: 10.1016/j.gie.2012.01.045. Epub 2012 Apr 4.
Gastroenterology specialty societies have advocated that providers routinely assess their performance on colonoscopy quality measures. Such routine measurement has been hampered by the costs and time required to manually review colonoscopy and pathology reports. Natural language processing (NLP) is a field of computer science in which programs are trained to extract relevant information from text reports in an automated fashion.
To demonstrate the efficiency and potential of NLP-based colonoscopy quality measurement.
In a cross-sectional study design, we used a previously validated NLP program to analyze colonoscopy reports and associated pathology notes. The resulting data were used to generate provider performance on colonoscopy quality measures.
Nine hospitals in the University of Pittsburgh Medical Center health care system.
Study sample consisted of the 24,157 colonoscopy reports and associated pathology reports from 2008 to 2009.
Provider performance on 7 quality measures.
Performance on the colonoscopy quality measures was generally poor, and there was a wide range of performance. For example, across hospitals, the adequacy of preparation was noted overall in only 45.7% of procedures (range 14.6%-86.1% across 9 hospitals), cecal landmarks were documented in 62.7% of procedures (range 11.6%-90.0%), and the adenoma detection rate was 25.2% (range 14.9%-33.9%).
Our quality assessment was limited to a single health care system in western Pennsylvania.
Our study illustrates how NLP can mine free-text data in electronic records to measure and report on the quality of care. Even within a single academic hospital system, there is considerable variation in the performance on colonoscopy quality measures, demonstrating the need for better methods to regularly and efficiently assess quality.
胃肠病学专业学会主张，医务人员应定期评估其结肠镜检查质量措施的执行情况。这种常规测量受到手动审查结肠镜检查和病理报告所需的成本和时间的阻碍。自然语言处理（NLP）是计算机科学的一个领域，其中程序经过训练，可以以自动化的方式从文本报告中提取相关信息。
展示基于 NLP 的结肠镜检查质量测量的效率和潜力。
在一项横断面研究设计中，我们使用了经过验证的 NLP 程序来分析结肠镜检查报告和相关的病理笔记。由此产生的数据用于生成医务人员在结肠镜检查质量措施上的表现。
匹兹堡大学医学中心医疗系统的 9 家医院。
研究样本由 2008 年至 2009 年的 24157 份结肠镜检查报告和相关病理报告组成。
7 项质量措施的提供者表现。
结肠镜检查质量措施的表现普遍较差，且表现差异很大。例如，在各个医院，准备的充分性仅在 45.7%的手术中得到记录（9 家医院的范围为 14.6%-86.1%），盲肠标志物在 62.7%的手术中得到记录（范围为 11.6%-90.0%），腺瘤检出率为 25.2%（范围为 14.9%-33.9%）。
我们的质量评估仅限于宾夕法尼亚州西部的一个单一医疗系统。
我们的研究说明了 NLP 如何挖掘电子记录中的自由文本数据，以衡量和报告护理质量。即使在单一的学术医院系统中，结肠镜检查质量措施的执行情况也存在相当大的差异，这表明需要更好的方法来定期、有效地评估质量。
