Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84112, USA.
J Am Med Inform Assoc. 2011 Jul-Aug;18(4):491-7. doi: 10.1136/amiajnl-2011-000187.
To understand how the source of information affects different adverse event (AE) surveillance methods.
Retrospective analysis of inpatient adverse drug events (ADEs) and hospital-associated infections (HAIs) detected by either a computerized surveillance system (CSS) or manual chart review (MCR).
Descriptive analysis of events detected using the two methods by type of AE, type of information about the AE, and sources of the information.
CSS detected more HAIs than MCR (92% vs 34%); however, a similar number of ADEs was detected by both systems (52% vs 51%). The agreement between systems was greater for HAIs than ADEs (26% vs 3%). The CSS missed events that did not have information in coded format or that were described only in physician narratives. The MCR detected events missed by CSS using information in physician narratives. Discharge summaries were more likely to contain information about AEs than any other type of physician narrative, followed by emergency department reports for HAIs and general consult notes for ADEs. Some ADEs found by MCR were detected by CSS but not verified by a clinician.
Inability to distinguish between CSS false positives and suspected AEs for cases in which the clinician did not document their assessment in the CSS.
The effect that information source has on different surveillance methods depends on the type of AE. Integrating information from physician narratives with CSS using natural language processing would improve the detection of ADEs more than HAIs.
了解信息来源如何影响不同的不良事件(AE)监测方法。
通过计算机化监测系统(CSS)或手动病历审查(MCR)检测到的住院不良药物事件(ADE)和医院相关感染(HAI)的回顾性分析。
通过两种方法检测到的事件的描述性分析,按 AE 类型、AE 信息类型和信息来源进行分类。
CSS 比 MCR 检测到更多的 HAI(92% vs 34%);然而,两个系统检测到的 ADE 数量相似(52% vs 51%)。两个系统之间的一致性对于 HAI 来说大于 ADE(26% vs 3%)。CSS 遗漏了没有编码格式信息或仅在医生叙述中描述的事件。MCR 使用医生叙述中的信息检测到 CSS 遗漏的事件。出院小结比任何其他类型的医生叙述更有可能包含 AE 信息,其次是 HAI 的急诊报告和 ADE 的一般咨询记录。一些通过 MCR 发现的 ADE 通过 CSS 检测到,但未经临床医生验证。
无法区分 CSS 假阳性和怀疑的 AE,因为在这种情况下,临床医生没有在 CSS 中记录他们的评估。
信息来源对不同监测方法的影响取决于 AE 的类型。使用自然语言处理将医生叙述中的信息与 CSS 集成,可以提高 ADE 的检测率,而不是 HAI。