Department of Information Sciences, City of Hope National Medical Center, Duarte, California, USA.
J Am Med Inform Assoc. 2012 Jan-Feb;19(1):111-5. doi: 10.1136/amiajnl-2011-000513. Epub 2011 Nov 14.
The identification and grading of adverse events (AEs) during the conduct of clinical trials is a labor-intensive and error-prone process. This paper describes and evaluates a software tool developed by City of Hope to automate complex algorithms to assess laboratory results and identify and grade AEs. We compared AEs identified by the automated system with those previously assessed manually, to evaluate missed/misgraded AEs. We also conducted a prospective paired time assessment of automated versus manual AE assessment. We found a substantial improvement in accuracy/completeness with the automated grading tool, which identified an additional 17% of severe grade 3-4 AEs that had been missed/misgraded manually. The automated system also provided an average time saving of 5.5 min per treatment course. With 400 ongoing treatment trials at City of Hope and an average of 1800 laboratory results requiring assessment per study, the implications of these findings for patient safety are enormous.
在临床试验进行期间，识别和分级不良事件（AEs）是一项劳动密集型且容易出错的工作。本文介绍并评估了 City of Hope 开发的一款软件工具，该工具可自动执行复杂算法，以评估实验室结果并识别和分级 AEs。我们将自动系统识别的 AEs 与之前手动评估的 AEs 进行了比较，以评估漏报/误报的 AEs。我们还对自动评估与手动 AE 评估进行了前瞻性配对时间评估。我们发现，自动分级工具的准确性/完整性有了很大提高，它额外识别出了 17%之前手动漏报/误报的严重 3-4 级 AEs。该自动系统还平均每治疗疗程节省了 5.5 分钟的时间。City of Hope 目前有 400 项正在进行的治疗试验，每项研究平均需要评估 1800 次实验室结果，这些发现对患者安全具有重大意义。
