Stubbs Brendan, Kale David C, Das Amar
Stanford University, Stanford, CA, USA.
AMIA Annu Symp Proc. 2012;2012:891-900. Epub 2012 Nov 3.
Clinicians at the bedside are increasingly overwhelmed by an inundation of information and must rely largely on pattern recognition and professional experience to comprehend complex clinical data and treat their patients in a timely manner. Traditional decision support systems are based on rules and predictive models and often fail to take advantage of increasingly large digital clinical data stores available in real-time. We propose an alternative approach to delivering data-driven decision support based on an interactive system for exploring and visualizing a context of physiologically similar patients from a database. Here we present Sim•TwentyFive, a highly flexible, responsive, intuitive prototype with a comprehensive set of interaction techniques that effectively reduces the cognitive burden of querying, exploring, analyzing and comparing similar past patient episodes. Quantitative performance tests and anonymous summative evaluations from PICU physicians indicated that Sim•TwentyFive is an efficient, intuitive and clinically-useful tool.
床边的临床医生越来越被大量信息所淹没,并且在很大程度上必须依靠模式识别和专业经验来理解复杂的临床数据并及时治疗患者。传统的决策支持系统基于规则和预测模型,往往无法利用实时可用的越来越大的数字临床数据存储。我们提出了一种基于交互式系统的数据驱动决策支持的替代方法,该系统用于从数据库中探索和可视化生理状况相似患者的情况。在此,我们展示Sim•TwentyFive,这是一个高度灵活、响应迅速、直观的原型,具有一套全面的交互技术,可有效减轻查询、探索、分析和比较过去相似患者病例的认知负担。儿科重症监护病房(PICU)医生进行的定量性能测试和匿名总结性评估表明,Sim•TwentyFive是一种高效、直观且临床实用的工具。