Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Med Care. 2013 Aug;51(8 Suppl 3):S38-44. doi: 10.1097/MLR.0b013e31829b1de1.
As clinical data are increasingly collected and stored electronically, their potential use for comparative effectiveness research (CER) grows. Despite this promise, challenges face those wishing to leverage such data. In this paper we aim to enumerate some of the knowledge management and informatics issues common to such data reuse.
After reviewing the current state of knowledge regarding biomedical informatics challenges and best practices related to CER, we then present 2 research projects at our institution. We analyze these and highlight several common themes and challenges related to the conduct of CER studies. Finally, we represent these emergent themes.
The informatics challenges commonly encountered by those conducting CER studies include issues related to data information and knowledge management (eg, data reuse, data preparation) as well as those related to people and organizational issues (eg, sociotechnical factors and organizational factors). Examples of these are described in further detail and a formal framework for describing these findings is presented.
Significant challenges face researchers attempting to use often diverse and heterogeneous datasets for CER. These challenges must be understood in order to be dealt with successfully and can often be overcome with the appropriate use of informatics best practices. Many research and policy questions remain to be answered in order to realize the full potential of the increasingly electronic clinical data available for such research.
随着临床数据越来越多地以电子方式收集和存储，它们在比较疗效研究（CER）中的潜在用途也在增加。尽管有这样的前景，但希望利用这些数据的人仍然面临挑战。在本文中，我们旨在列举一些与这种数据再利用相关的知识管理和信息学问题。
在回顾了当前关于生物医学信息学挑战以及与 CER 相关的最佳实践的知识状况后，我们随后介绍了我们机构的 2 个研究项目。我们分析了这些项目，并强调了与进行 CER 研究相关的几个共同主题和挑战。最后，我们呈现了这些新兴的主题。
那些进行 CER 研究的人通常遇到的信息学挑战包括与数据信息和知识管理相关的问题（例如，数据重用、数据准备）以及与人及组织问题相关的问题（例如，社会技术因素和组织因素）。这些问题的例子被进一步详细描述，并提出了一个描述这些发现的正式框架。
研究人员在尝试使用通常多样化和异构的数据集进行 CER 时面临着重大挑战。为了成功应对这些挑战，必须了解这些挑战，并且通常可以通过适当应用信息学最佳实践来克服这些挑战。为了充分发挥越来越多的电子临床数据在这类研究中的潜力，还有许多研究和政策问题需要回答。
