Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining

Abstract

Uncertainty has been the perceived Achilles heel of the radiology report since the inception of the free-text report. As a measure of diagnostic confidence (or lack thereof), uncertainty in reporting has the potential to lead to diagnostic errors, delayed clinical decision making, increased cost of healthcare delivery, and adverse outcomes. Recent developments in data mining technologies, such as natural language processing (NLP), have provided the medical informatics community with an opportunity to quantify report concepts, such as uncertainty. The challenge ahead lies in taking the next step from quantification to understanding, which requires combining standardized report content, data mining, and artificial intelligence; thereby creating Knowledge Discovery Databases (KDD). The development of this database technology will expand our ability to record, track, and analyze report data, along with the potential to create data-driven and automated decision support technologies at the point of care. For the radiologist community, this could improve report content through an objective and thorough understanding of uncertainty, identifying its causative factors, and providing data-driven analysis for enhanced diagnosis and clinical outcomes.

Strategies for Radiology Reporting and Communication

“…This approach is particularly well suited for communication of medical imaging data, given the fact that the data is inherently image-based and relatively independent of language. One can argue that in the creation of medical imaging reports (which is the existing primary mode of communication), the concepts and information contained within the imaging dataset are converted into textual data, which often introduce uncertainty, confusion, and ambiguity [17,18]. While textual data retains an important role in the proposed communication strategy, it becomes secondary to the primary visual data source, the imaging dataset.…”

Creating Accountability in Image Quality Analysis Part 3: Creation of a Standardized Image-Centric Mark-Up and Annotation Tool

“…An obvious quality effect may consist of an additional imaging exam, which may result in additional cost, radiation, and/or management time delay. A more insidious quality effect may consist of equivocal or ambiguous report findings, which may produce confusion or even error on the part of the clinician when instituting clinical management [17,18]. It is somewhat ironic that an insidious quality effect such as report ambiguity can produce a negative impact of greater magnitude that a more obvious quality effect and this illustrates the clinical imperative of quality improvement in medical imaging.…”

Using Analysis of Speech and Linguistics to Characterize Uncertainty in Radiology Reporting

“…Uncertainty has been described as the Achilles heel of the radiology report [9], which is the single most important basis on which radiologists are judged by their clinical colleagues [10]. A number of diverse external factors contribute to radiology report uncertainty including technical (e.g., poor image quality), clinical (e.g., insufficient clinical data), medico legal (e.g., increased risk of litigation), anatomic (e.g., anatomic variation), and societal (e.g., lack of established standards).…”

“…As data mining and knowledge discovery applications begin to play greater roles in medical reporting and analysis, technology such as natural language processing (NLP) can be applied to the task of uncertainty detection and analysis with the ultimate goal of correlating report uncertainty, diagnostic accuracy, and clinical outcomes [9]. Analysis of report uncertainty could potentially provide important insights as to the contextual and user-specific factors associated with uncertainty, and subsequent impact uncertainty plays on clinical outcomes.…”