The radiology community needs to play an active role in creating new data-driven analysis and innovation strategies for the betterment of image quality analysis, according to an article published online June 2 in the Journal of the American College of Radiology.
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 thanks to technical, economic, cultural and geographic factors. The article’s author, Bruce Reiner, MD, of the Baltimore VA Medical Center, contends that radiologists needs to be more proactive by assuming leadership roles in quality assurance education, research, clinical oversight and intervention.
Reiner first argued that the way in which image quality is perceived must 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,” explained Breiner in an interview with Health Imaging. “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,” said Breiner. “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.”
Currently, little research has been done to investigate the relationship between image quality deficiencies and report shortcomings. Economic analyses of the costs and consequences associated with poor image quality are also necessary, as data would likely drive changes or mandates for standardized image quality metrics in reports. “Gathering this data is really important because if researchers can show the deleterious downstream effects of image quality deficiencies, that could heighten performance,” said Breiner.
Other variables additionally 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 Breiner in the article. If attention shifted to image quality and outcome analysis data, the community could differentiate itself from others as well as inner competition, consequently combatting commoditization trends and declines in reimbursement.
In order to produce an effective intervention strategy, a standardized data infrastructure must first be established. Once an infrastructure is made, supporting data pertinent to exam type, clinical indication, technology, protocol parameters, provider identification and patient attributes can be recorded and then directly incorporated into the radiology report—similar, in many ways, to BI-RADS.
“The quality-centric data and analytics can evolve to include data from each step in the collective imaging chain with the goal of objectively defining cause and effect relationships between individual data points and identifying specific areas for process improvement,” wrote Breiner.
Breiner views this as an opportunity to justify the value of radiologists and expand their role to include quality educator, consultant, researcher and supervisor. Getting to this point, however, will take some major changes.
“The reality is that most of the time, to get universal changes requires mandates and legislation at a much higher level,” explained Breiner. “If the government or CMS mandated standardized imaging quality metrics, that’s the way I think would most likely effect change. If it’s done on a voluntary basis, there’s going to be a mixed degree of user acceptance. If on the other hand the metrics are mandated and tied to reimbursements, then