Quality (Out of) Control: Why We Need Standardized Image Quality Metrics

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 - Doc Thinking

Image quality is vital to the overall quality of medical imaging service delivery. Instead of getting better with time, however, medical imaging quality assurance has declined due to technical, economic, cultural and geographic factors. Radiologists consequently need to be more proactive by assuming leadership roles in quality assurance education, research, clinical oversight and intervention. If attention shifts to image quality and outcome analysis data, the radiology community could differentiate itself from others as well as inner competition, consequently combatting commoditization trends and declines in reimbursement.

The battle cry has been heard far and wide. Bruce Reiner, MD, of the Baltimore VA Medical Center, is the latest warrior urging radiology to play an active role in creating new data-driven analysis and innovation strategies for the betterment of image quality analysis. Reiner contends that radiologists need to be more proactive by taking leadership stances in quality assurance education, research, clinical oversight and intervention in an article published online in the Journal of the American College of Radiology in early June.

“The current practice of quality assurance in everyday medical imaging practice is often idiosyncratic and inconsistent, with the priorities of imaging service providers often focused on productivity and workflow, which can come at the expense of quality,” wrote Reiner in an article published in the Journal of Digital Imaging in April 2013.

With the shift from analog to digital, medical imaging practice came a dichotomy, according to Reiner. During the era of analog imaging, quality assurance was systematic and rigorous thanks to several factors including active participation by all medical stakeholders, the centralized layout of the radiology department, lower exam volumes and higher reimbursements.

“With the transition to digital imaging, a number of dramatic changes occurred in the medical department related to technology, physical layout, workflow, utilization, and economies,” he wrote. Greater emphasis was suddenly placed on productivity rather than quality measures.

Reiner argues that the way image quality is perceived must first be altered; rather than a single-step event, it should be viewed as part of a chain of events. “I think anyone who’s been in the radiology game for a long time realizes that they are held captive to some extent by the work of technologists and the technology being used,” explains Reiner. “Everyone knows there are interaction effects; that’s nothing new. Something that would shed important light on the multistep nature of image quality assurance would be research and data collection. If I can validate the concepts that I talk about and show areas in need of improvement by intervening in a positive way, then improved clinical outcomes of efficacy will result.”

Poor image quality also has clinical and economic impacts, as it can result in diagnostic inaccuracy and additional tests that would otherwise be unnecessary. “If a radiologist sees that poor image quality adversely affects his or her reports, that more likely diminishes his or her confidence in diagnosis and might equivocate and suggest additional studies,” says Reiner. “This might not be required if he or she had improved image quality. If radiologists can see that their performance can improve through image quality assurance, then they might see worth in that investment of additional time.”

Little research has been done, however, to investigate the relationship between image quality deficiencies and report shortcomings.

“While the vast majority of medical imaging providers understand the criticality of quality on clinical outcomes, the reality is that quality assurance in its present form is limited at best and inherently flawed at worst,” wrote Reiner.

Economic analyses of the costs and consequences associated with poor image quality are necessary, as data would likely drive changes or mandates for standardized image quality metrics in reports. “Gathering these data is really important because if researchers can show the deleterious downstream effects of image quality deficiencies, that could heighten performance,” says Reiner.

Other variables contribute to poor image quality. “Because patients, providers, and technologies also play fundamental roles in defining image quality, variability in these groups may affect image quality outcomes by varying degrees,” wrote Reiner. “The net result