Population health management (PHM) in radiology is gaining attention as a holistic method to improve health outcomes of patient groups through coordinated financial and care models.1 “Radiologists’ presence at the intersection of many aspects of healthcare, including screening, diagnostic imaging, and image-guided therapies, provides the opportunity for increased engagement in PHM. Furthermore, innovations in AI and imaging informatics will serve as critical tools to improve value in healthcare through evidence-based and equitable approaches,” says Jessica H. Porembka, MD, associate professor of radiology at UT Southwestern Medical Center, vice chair of strategy and quality, and medical director of Parkland Breast Imaging and Quality Assurance.1 Porembka recently spoke with the Bulletin to discuss proactive imaging, including incidental findings management, and opportunistic imaging.
What is incidental findings management?
Incidental findings are findings seen on imaging that are unrelated to the goal of the study. So, for example, breast mass seen on a chest CT performed to evaluate for pulmonary embolism would be an incidental finding. These findings have become increasingly important – as imaging volumes go up, so do incidental findings – and I think the main question that we’re faced with is what to do with them.
Incidental-findings management encompasses two different parts: part one is the radiologist’s recommendation on what to do with the finding and determining its significance. The ACR Incidental Findings Committee has done a lot of work to provide guidance for a range of findings through white papersNow the radiologist and the PCP have effectively created a shared follow-up care plan for that patient, and the scheduling team can then use this to get a signed order for a follow-up study, contact the patient, and schedule them for the study, while also obtaining pre-authorization. I think this is a great example of care coordination for incidental-findings management.
What is opportunistic imaging?
Opportunistic imaging is distinctly different from incidental- findings management. Opportunistic imaging uses imaging that has already been performed to purposefully identify certain findings or conditions that are not being sought out by the ordering provider, usually accomplished by applying imaging technology or AI algorithms to the imaging.3 One example that comes to mind is determining bone density on an abdominal CT to assess for osteoporosis. The ordering provider may have ordered the CT to look for a reason for the patient’s abdominal pain, but the radiologist can also look for a chronic or undiagnosed condition like osteoporosis, that, if treated, may prevent a future fracture.
How does AI play into both forms of imaging?
AI is so important when we think about how to leverage technology to help with these tasks. It should be viewed as a resource that can help radiologists work smarter and more efficiently, not to take away work. For the example of AI and incidental-findings management, AI could use natural language processing to extract information from the radiologist’s report without the radiologist having to create the critical result notification. That extraction could flow into the in-basket alert system and then flow into a system, like the RADAR system, that allows for creation of a shared care plan. AI also plays an important role in opportunistic imaging since a lot of it is happening through imaging technology or AI algorithms to process those images.
How can radiologists introduce incidental findings management or opportunistic imaging to their departments?
There are journal articles and papers on incidental-findings management and opportunistic imaging that can help lay the groundwork. Beyond that, I think the ACR will be shining a spotlight on opportunistic imaging and the management of incidental findings, so I think staying involved in the ACR will definitely help those who are interested.