Follow-Up Imaging Is Challenging. Here’s How to Improve It.
By Christopher Cheney
Follow-up imaging can require complicated processes to set up studies and make sure they are completed, with multiple clinicians often involved in a patient’s care. Establishing standardized processes is crucial, and artificial intelligence has the potential to improve radiology workflows.
That’s why St. Tammany Health System is using high reliability organization principles and artificial intelligence to make sure follow-up imaging is conducted for patients.
“The need for follow-up imaging is a potential risk for patients because follow-up recommendations may not be completed or they may not be in keeping with the provider team’s intent for what they had planned for images,” says Patrick Torcson, MD, senior vice president and CMO of St. Tammany Health System. “The traditional approach that has been used is a series of workarounds; historically, there has not been a hard-wired process for how follow-up imaging occurs.”
The health system is using a high-reliability approach to follow-up imaging to hard wire the process, so it does not rely on the memory of care team members or some type of workaround.
“It is part of the workflow process that goes with the scheduling, registration, completion of the study, and the interpretations of the imaging that patients expect to get,” Torcson says. “It is a complex process that occurs over several sites and over time, so we are reducing risk with standardization to make sure the follow-up imaging occurs.”
“In the past when it comes to functions such as follow-up imaging, we have relied on individual accountability, whether it has been relying on the patient’s role in receiving follow-up imaging or relying on an ordering physician such as a primary care doctor,” Torcson says. “What we have learned is that follow-up imaging requires a system-level process.”
AI tool adoption
Using an AI tool to improve the follow-up imaging process is part of St. Tammany Health System’s efforts to establish high reliability in this area of care. AI offers a great opportunity to integrate with current radiology technology and the electronic health record to make sure that patients get the follow-up imaging that is needed in multiple sites of care, Torcson explains.
“It is a complex process to arrange follow-up imaging,” Torcson says. “It is more complex than human-focused appointment scheduling and should not rely on memory.”
AI can help standardize and automate the follow-up imaging process.
The AI tool automatically identifies studies requiring additional imaging or consultation, then relays the information to referring clinicians, which streamlines communication between imaging centers and clinicians. It also continuously tracks open follow-ups. By offering follow-up management with the AI tool, imaging centers can position themselves as partners with referring clinicians and contribute to better patient care.
“AI provides an augmentation for the workflow that makes the follow-up imaging process hard wired, so it occurs consistently and non-ambiguously,” Torcson says. “It also helps create a standardized process. With AI, we can establish a process that exceeds human cognitive capacity, especially when you factor in different sites of care and different providers.”
Christopher Cheney is the CMO editor at HealthLeaders.