Stoto Michael A
Georgetown University.
EGEMS (Wash DC). 2013 Dec 18;1(3):1055. doi: 10.13063/2327-9214.1055. eCollection 2013.
Whether one reads or Institute of Medicine issue briefs, it’s clear that most now accept the idea that existing electronic clinical data (ECD) and other health records can be used to manage and improve the processes, outcomes, and the quality of health care. Indeed the increasing popularity of the term “learning healthcare system” signals the broad acceptance of the idea that routinely collected clinical data can – indeed should – be used to advance knowledge and support continuous learning. But despite what the big data enthusiasts say, none of this is easy without the appropriate analytical methods. This commentary introduces the seven papers in eGEMs’ second special issue, which are the result of invitations to researchers who have participated in EDM Forum activities as well as an open call for paper in early summer 2013. These papers offer a beginning snapshot of the ways innovative thinkers across the country are developing methodology to advance the national dialogue on the use of ECD to conduct CER, support QI, and generally to improve outcomes in a learning healthcare system.
无论阅读医学研究所的问题简报还是其他资料，很明显，现在大多数人都接受这样一种观点，即现有的电子临床数据（ECD）和其他健康记录可用于管理和改进医疗保健的流程、结果及质量。事实上，“学习型医疗系统”一词日益流行，这表明人们广泛接受这样一种观点，即常规收集的临床数据能够——实际上也应该——用于推动知识进步并支持持续学习。但是，尽管大数据爱好者们所言不假，但如果没有适当的分析方法，这一切都并非易事。本评论介绍了eGEMs第二期特刊中的七篇论文，这些论文是邀请参与电子数据管理（EDM）论坛活动的研究人员以及在2013年初夏公开征集论文的成果。这些论文初步展示了全国各地创新思想家们开发方法的方式，以推动关于利用ECD进行比较效果研究（CER）、支持质量改进（QI）以及总体上改善学习型医疗系统结果的全国性对话。
