Myneni Sahiti, Patel Vimla L
Center for Decision Making and Cognition, Department of Biomedical Informatics, Arizona State University, USA.
AMIA Annu Symp Proc. 2009 Nov 14;2009:463-7.
Biomedical researchers often have to work on massive, detailed, and heterogeneous datasets that raise new challenges of information management. This study reports an investigation into the nature of the problems faced by the researchers in two bioscience test laboratories when dealing with their data management applications. Data were collected using ethnographic observations, questionnaires, and semi-structured interviews. The major problems identified in working with these systems were related to data organization, publications, and collaboration. The interoperability standards were analyzed using a C(4)I framework at the level of connection, communication, consolidation, and collaboration. Such an analysis was found to be useful in judging the capabilities of data management systems at different levels of technological competency. While collaboration and system interoperability are the "must have" attributes of these biomedical scientific laboratory information management applications, usability and human interoperability are the other design concerns that must also be addressed for easy use and implementation.
生物医学研究人员常常需要处理海量、详细且异构的数据集,这给信息管理带来了新的挑战。本研究报告了对两个生物科学测试实验室的研究人员在处理其数据管理应用程序时所面临问题的性质的调查。数据通过人种志观察、问卷调查和半结构化访谈收集。在使用这些系统时发现的主要问题与数据组织、出版物和协作有关。使用C(4)I框架在连接、通信、整合和协作层面分析了互操作性标准。结果发现,这种分析对于判断不同技术能力水平的数据管理系统的能力很有用。虽然协作和系统互操作性是这些生物医学科学实验室信息管理应用程序的“必备”属性,但可用性和人员互操作性也是为便于使用和实施而必须解决的其他设计问题。