The DEDUCE Guided Query tool: Providing simplified access to clinical data for research and quality improvement

被引：152

作者：

Horvath, Monica M. ^{[1
]}

Winfield, Stephanie ^{[1
]}

Evans, Steve ^{[1
]}

Slopek, Steve ^{[1
]}

Shang, Howard ^{[1
]}

Ferranti, Jeffrey ^{[1
,2
]}

机构：

[1] Duke Univ Hlth Syst, Duke Hlth Technol Solut, Durham, NC 27705 USA

[2] Duke Univ, Dept Pediat, Sch Med, Durham, NC 27706 USA

来源：

JOURNAL OF BIOMEDICAL INFORMATICS | 2011年 / 44卷 / 02期

基金：

美国国家卫生研究院;

关键词：

Translational research; Medical informatics; Clinical informatics; Medical records systems; Computerized health care evaluation mechanisms; Hospital information systems; HEALTH-CARE; INFORMATICS; ERR;

D O I：

10.1016/j.jbi.2010.11.008

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (c) 2010 Elsevier Inc. All rights reserved.

引用

页码：266 / 276

页数：11

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