Duke Health Technology Solutions, Duke University Health System, 2424 Erwin Road, Durham, NC 27705, USA.
J Biomed Inform. 2011 Apr;44(2):266-76. doi: 10.1016/j.jbi.2010.11.008. Epub 2010 Dec 2.
In many healthcare organizations, comparative effectiveness research and quality improvement (QI) investigations are hampered by a lack of access to data created as a byproduct of patient care. Data collection often hinges upon either manual chart review or ad hoc requests to technical experts who support legacy clinical systems. In order to facilitate this needed capacity for data exploration at our institution (Duke University Health System), we have designed and deployed a robust Web application for cohort identification and data extraction--the Duke Enterprise Data Unified Content Explorer (DEDUCE). DEDUCE is envisioned as a simple, web-based environment that allows investigators access to administrative, financial, and clinical information generated during patient care. By using business intelligence tools to create a view into Duke Medicine's enterprise data warehouse, DEDUCE provides a Guided Query functionality using a wizard-like interface that lets users filter through millions of clinical records, explore aggregate reports, and, export extracts. Researchers and QI specialists can obtain detailed patient- and observation-level extracts without needing to understand structured query language or the underlying database model. Developers designing such tools must devote sufficient training and develop application safeguards to ensure that patient-centered clinical researchers understand when observation-level extracts should be used. This may mitigate the risk of data being misunderstood and consequently used in an improper fashion.
在许多医疗保健组织中,由于缺乏对患者护理过程中产生的副产品数据的访问权限,比较有效性研究和质量改进(QI)调查受到阻碍。数据收集通常依赖于手动图表审查或临时向支持传统临床系统的技术专家提出请求。为了在我们的机构(杜克大学健康系统)促进这种数据探索的必要能力,我们设计并部署了一个用于队列识别和数据提取的强大 Web 应用程序 - 杜克企业数据统一内容浏览器(DEDUCE)。DEDUCE 被设想为一个简单的基于 Web 的环境,允许研究人员访问患者护理期间生成的行政、财务和临床信息。通过使用商业智能工具创建对 Duke Medicine 的企业数据仓库的视图,DEDUCE 提供了使用向导式界面的引导查询功能,使用户能够筛选数百万条临床记录、探索汇总报告,并导出提取。研究人员和 QI 专家可以获得详细的患者和观察级别的提取,而无需了解结构化查询语言或底层数据库模型。设计此类工具的开发人员必须投入足够的培训并开发应用程序保护措施,以确保以患者为中心的临床研究人员了解何时应使用观察级别的提取。这可能会降低数据被误解并因此以不当方式使用的风险。