Quantifying Analysis of Uncertainty in Medical Reporting: Creation of User and Context-Specific Uncertainty Profiles

被引:0
|
作者
Bruce I. Reiner
机构
[1] Maryland VA Healthcare System,
来源
Journal of Digital Imaging | 2018年 / 31卷
关键词
Uncertainty; Data mining; Profiling; Outcomes analysis;
D O I
暂无
中图分类号
学科分类号
摘要
While uncertainty is ubiquitous in medical practice, minimal work to date has been performed to analyze the cause and effect relationship between uncertainty and patient outcomes. In medical imaging practice, uncertainty in the radiology report has been well documented to be a source of clinician dissatisfaction. Before one can effectively create intervention strategies aimed at reducing uncertainty, it must first be better understood through context- and user-specific analysis. One strategy for accomplishing this task is to characterize the source of uncertainty and create user-specific uncertainty profiles which take into account a number of provider-specific variables which may contribute to report uncertainty. The resulting data can in turn be used to create real-time report uncertainty metrics aimed at providing uncertainty analytics at the point of care, for the combined purposes of decision support, improved communication, and enhanced clinical/economic outcomes.
引用
收藏
页码:379 / 382
页数:3
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