Renaissance Computing Institute (RENCI), University of North Carolina, Chapel Hill, NC, USA.
J Biomed Inform. 2012 Feb;45(1):101-6. doi: 10.1016/j.jbi.2011.09.003. Epub 2011 Sep 20.
Comparative Effectiveness Research (CER) is designed to provide research evidence on the effectiveness and risks of different therapeutic options on the basis of data compiled from subpopulations of patients with similar medical conditions. Electronic Health Record (EHR) system contain large volumes of patient data that could be used for CER, but the data contained in EHR system are typically accessible only in formats that are not conducive to rapid synthesis and interpretation of therapeutic outcomes. In the time-pressured clinical setting, clinicians faced with large amounts of patient data in formats that are not readily interpretable often feel 'information overload'. Decision support tools that enable rapid access at the point of care to aggregate data on the most effective therapeutic outcomes derived from CER would greatly aid the clinical decision-making process and individualize patient care. In this manuscript, we highlight the role that visual analytics can play in CER-based clinical decision support. We developed a 'VisualDecisionLinc' (VDL) tool prototype that uses visual analytics to provide summarized CER-derived data views to facilitate rapid interpretation of large amounts of data. We highlight the flexibility that visual analytics offers to gain an overview of therapeutic options and outcomes and if needed, to instantly customize the evidence to the needs of the patient or clinician. The VDL tool uses visual analytics to help the clinician evaluate and understand the effectiveness and risk of different therapeutic options for different subpopulations of patients.
比较效果研究（CER）旨在根据来自具有相似医疗条件的患者亚群的数据，提供关于不同治疗选择的有效性和风险的研究证据。电子健康记录（EHR）系统包含大量可用于 CER 的患者数据，但 EHR 系统中包含的数据通常只能以不利于快速综合和解释治疗结果的格式访问。在时间紧迫的临床环境中，面对难以理解的大量患者数据格式的临床医生通常会感到“信息过载”。决策支持工具可以在护理点快速访问汇总的最有效治疗结果数据，这将极大地帮助临床决策过程并实现患者护理的个体化。在本文中，我们强调了视觉分析在基于 CER 的临床决策支持中的作用。我们开发了一个“VisualDecisionLinc”（VDL）工具原型，该工具使用视觉分析提供总结的 CER 衍生数据视图，以促进对大量数据的快速解释。我们强调了视觉分析提供的灵活性，以概述治疗选择和结果，如果需要，可立即将证据定制为患者或临床医生的需求。VDL 工具使用视觉分析帮助临床医生评估和理解不同治疗选择对不同患者亚群的有效性和风险。
