临床研究数据收集表上的分布式认知人工制品。

Meredith Nahm, Vickie D Nguyen, Elie Razzouk, Min Zhu, Jiajie Zhang
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引用次数: 0

摘要

病历摘要是二次数据使用中的一种主要数据收集方式,与高错误率有关。认知因素尚未被研究用于解释病历摘要错误。我们采用分布式表征和表征分析理论,系统地评估了病历抽取过程中的认知需求,以及临床研究数据收集表样本中采用的外部认知支持程度。此外,对于最复杂的数据元素,数据收集表格不支持外部认知。工作记忆要求高可能是数据错误与需要抽象者解释、比较、映射或计算的数据元素相关联的一个原因。这里使用的表征分析可用于识别认知要求高的数据元素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Distributed cognition artifacts on clinical research data collection forms.

Distributed cognition artifacts on clinical research data collection forms.

Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Cognitive factors have not been studied as a possible explanation for medical record abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms.We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

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