Devine Emily Beth, Capurro Daniel, van Eaton Erik, Alfonso-Cristancho Rafael, Devlin Allison, Yanez N David, Yetisgen-Yildiz Meliha, Flum David R, Tarczy-Hornoch Peter
University of Washington.
EGEMS (Wash DC). 2013 Sep 10;1(1):1025. doi: 10.13063/2327-9214.1025. eCollection 2013.
The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems.
Investigators from Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure.
SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm's Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill 'go-live' at each site.
A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15-18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted-25% from structured data; 25% from text mining.
In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.
临床研究信息学领域包括创建用于开展质量改进（QI）活动和比较效果研究（CER）的临床数据存储库（CDR）。理想情况下，CDR数据是从不同卫生系统的各种电子健康记录（EHR）中准确且直接提取的。
华盛顿州外科护理结果与评估项目（SCOAP）比较效果研究转化网络（CERTAIN）的研究人员正在创建这样一个CDR。本文描述了用于创建此数字基础设施的自动化和验证方法。
SCOAP是一项QI基准测试计划。数据从EHR中手动提取并输入到数据管理系统中。CERTAIN研究人员现在正在部署Caradigm公司的Amalga™工具，以促进从多个不同的EHR中自动提取数据。计算一致性以将自动提取的数据与手动提取的数据进行比较。计算Amalga与每个源EHR之间的性能指标。验证在重复循环中进行，并随着时间的推移不断改进。当自动提取达到当前的提取准确性基准——95%时，它将在每个站点上线。
在14个站点完成了技术分析。5个站点正在提供数据；其余站点将满足有意义使用标准作为优先事项。参与站点正在提供15 - 18种独特的数据源，总共13个手术登记用例。常见的数据源包括挂号、实验室、转录/听写、放射学和药物。在1320个指定数据元素中，约50%正在被自动提取——25%来自结构化数据；25%来自文本挖掘。
在半自动化数据提取并进行严格验证的过程中，CERTAIN研究人员将半自动化数据收集以开展QI和CER，同时推动学习型医疗系统的发展。
