How to Measure Value in Mining From Radiology

As the industry transitions from one based on fee-for-service medicine to one dominated by value-based reimbursement models, the question threading through conversations from plenary session presenters to conversations at the coffee stand on the exhibit floor is this: How can radiologists drive more value out of the patient care experience? And they’re asking that question while surrounded by millions of dollars worth of the latest diagnostic imaging equipment available from around the world.

In his opening address on Sunday, RSNA President Richard Baron M.D., answered the question – and raised a lot of eyebrows – by challenging radiologists to get back to basics by focusing on what’s best for the patient rather than on what’s more convenient, efficient or lucrative for themselves. In the old fee-for-service model, the value of radiology was measured by throughput. This many imaging procedures were good. 

Today, value is measured by better clinical outcomes at less cost. In that equation, throughput becomes less important and the quality of the imaging procedure becomes more important. By that I mean not just reading the image correctly and sending it to the treating physician. I mean integrating clinical data from the patient’s EMR into the imaging process and looking for things based on that clinical data that may not have been initially ordered. I mean including in the radiology report not just what was found but an interpretation of what was found along with treatment recommendations or recommendations for further diagnostic imaging tests based on that interpretation.

Its most doctors belief that new technologies that connect imaging to other patient information systems will more than pay for themselves from the savings generated by improved clinical outcomes driven by earlier and more effective clinical interventions.

As Keith Dreyer, D.O., noted in his presentation during the opening plenary session, machine learning, or artificial intelligence, will become a tremendous asset to radiologists as they adapt to this new value-driven approach to what they do. Dreyer, who is vice chairman of radiology computing and information services at a Hospital, said radiologists will be able to use AI in screening technologies to detect diseases by synthesizing aggregate and patient-specific clinical data from all sources to produce what he called “precision radiology reports.” It may take some time for Dr. Dreyer’s vision to become a reality, but when it does, it will augment and greatly assist radiologists in driving more value from imaging procedures.