Data integration imperative for evidence-based imaging
Accessing and integrating diagnostic imaging data from modalities, quality-assurance workstations, RIS, PACS and EMR—if used properly and prospectively—can serve as a valuable tool to improve clinical practice and elevate the quality of care, according to Bruce L. Reiner, MD, and Eliot L. Siegel, MD.

“The pooled data and knowledge offered through medical informatics and its supporting technologies provide the infrastructure to facilitate evidence-based radiology (EBR), which, in theory, leads to improved clinical outcomes,” wrote the authors in an article published online before print (May 5, 2009) in the Journal of Digital Imaging.

“In its present form, however, EBR focuses almost exclusively on the radiology report and imaging diagnosis,” they wrote. “By doing so, however, many of the essential steps in the imaging chain are largely ignored—steps that ultimately affect the quality of imaging services and clinical outcomes.”

The duo, from the radiology department at the Baltimore Veterans Affairs Medical Center, noted that the collective radiology product is a sum total of multiple steps, performed by multiple individuals, using multiple technologies.

Because data is contributed by these individuals at each step of the process, Siegel and Reiner observe that it is possible to use medical informatics to objectively analyze performance deliverables and differentiate medical imaging service providers in data-driven qualitative and quantitative terms.

“Medical informatics provides the mechanism to track, store, analyze and report quality performance indicators intrinsic to radiology practice, thereby providing an objective mechanism for providers to differentiate themselves based on quality metrics,” they wrote.

They argue that it is imperative that diagnostic imaging providers begin to see their practices in such a data-holistic perspective.

“This data-driven, quality-oriented analysis is crucial to the long-term survival of medical imaging, where the trend toward commoditization is accelerating because of globalization, increased information exchange, and technological developments,” they wrote.

Reiner and Siegel propose five primary objectives for radiology practice data mining:
1.    Objectification
2.    Quantification
3.    Stratification
4.    Clarification
5.    Rectification

The elements of objectification and quantification replace subjective assessments with repeatable metrics that can be tracked and analyzed.

“Image quality, for example, is most often assessed by subjective analysis, taking the form of a ‘beauty contest’ in which medical images are judged by their aesthetic appearance,” they wrote. “In reality, this assessment may have little to do with an image’s intrinsic diagnostic value.”

Stratification allows the data to be parsed into multiple components, variables indentified and comparative analysis performed on common and independent elements. Clarification is the analysis of the data, while rectification is the utilization of the information.

“The last (and perhaps most important) objective of data mining is to take the information gained and utilize it to create effective solutions to address documented deficiencies,” noted Reiner and Siegel.

The duo believe that data mining for EBR can point out existing deficiencies and provide an effective means to enhance ongoing education and training initiatives.

“In the end, it is not essential that practicing radiologists understand the intricacies of medical informatics,” they wrote. “What is important is that they realize the intrinsic value of objective data to enhance everyday clinical practice and use this to prevent their own medical specialty from becoming a commodity, devoid of objective, quality-oriented performance measures.”