Humans have a deep-seated need for interpersonal contact, and understanding that need should guide not only how we structure our work relationships in the post-COVID era, but also our development and deployment of new technologies like AI in radiology.
That’s according to James Whitfill, MD, who gave Thursday’s opening address at SIIM 2023. Whitfill’s talk – which was followed by a raucous audience participation exercise – was a ringing demonstration that in-person meetings like SIIM still have relevance despite the proliferation of Zoom calls and remote work.
Whitfill, chief transformation officer at HonorHealth in Arizona and an internist at the University of Arizona, was chair of the SIIM board in 2020 when the society made the difficult decision to move its annual meeting to be fully online during the pandemic.
The experience led Whitfill to ponder whether technology designed to help us work and collaborate virtually was an adequate substitute for in-person interaction. Unfortunately, the research suggests otherwise:
- Numerous studies have demonstrated the negative effect that the isolation of the COVID pandemic has had on adolescent mental health and academic performance
- Loneliness can also have a negative effect on physical well-being, with a recent U.S. Surgeon General’s report finding that prolonged isolation is the health equivalent of smoking 15 cigarettes a day
- Peer-reviewed studies have shown that people working in in-person collaborative environments are about 10% more productive and creative than those working virtually.
Whitfill’s talk was especially on-point given recent research indicating that workers across different industries who used AI were more lonely than those who didn’t, a phenomenon that shouldn’t be ignored by those planning radiology’s AI-based future.
That said, virtual technologies can still play a role in making access to information more equitable. Whitfill noted that some 160 people were following the SIIM proceedings entirely online, and they otherwise would not have been able to benefit from the meeting’s content.
To drive the point home, Whitfill then had audience members participate in a team-based Rochambeau competition that sent peals of laughter ringing through Austin Convention Center.
Whitfill’s point was underscored repeatedly by SIIM 2023 attendees, who reiterated the value of interpersonal connections and networking at the conference. It’s ironic that a meeting devoted at least in part to intelligence that’s artificial has made us better appreciate relationships that are real.
AUSTIN – Before AI came along, the Society for Imaging Informatics in Medicine (SIIM) seemed to be a conference in search of itself. SIIM (and before it, SCAR) built its reputation on education and training for radiology’s shift to digital image management.
But what happens when the dog catches the truck? Radiology eventually fully adopted digital imaging, and that meant less need to teach people about technology they were already using every day.
Fast forward to the AI era, and SIIM seems to have found its new mission. Once again, radiology is faced with a transformative IT technology that few understand and even fewer know how to put into clinical practice. With its emphasis on education and networking, SIIM is a great forum to learn how to do both.
That’s exemplified by the SIIM keynote address on Wednesday, by Ziad Obermeyer, MD, a physician and researcher in machine learning at UC Berkeley who has published important research on bias in machine learning.
While not a radiologist, Obermeyer served up a fascinating talk on how AI should be designed and adopted to have maximum impact. His advice included:
- Don’t design AI to perform the same tasks humans do already. Train algorithms to perform in ways that make up for the shortcomings of humans.
- Training algorithms on medical knowledge from decades ago is likely to produce bias when today’s patient populations don’t match those of the past.
- Access to high-quality data is key to algorithm development. Data should be considered a public good, but there is too much friction in getting it.
To solve some of these challenges, Obermeyer is involved in two projects, Nightingale Open Science to connect researchers with health systems, and Dandelion Health, designed to help AI developers access clinical data they need to test their algorithms.
The rise of AI – particularly generative AI models like ChatGPT – has given SIIM a shot in the arm from a content perspective, and the return of in-person meetings plays to the conference’s strength as an intimate get-together where the networking and relationship-building is almost as important as the content. Please follow along with the proceedings of SIIM 2023 on our Twitter and LinkedIn pages.
In the never-ending quest to get referring physicians to follow radiologist recommendations for follow-up imaging, Massachusetts researchers in JAMA Network Open offer an IT-based solution: Structured reporting software that was found to triple the number of radiology reports judged to be complete.
A recent study found that 65% of radiologist recommendations for follow-up imaging aren’t followed by referring physicians. Authors of that study found that recommendations that were strongly worded and communicated directly to referring doctors had higher uptake.
But what if radiologists don’t follow this advice? In the new paper, researchers from Brigham and Women’s Hospital and Harvard Medical School offer a more structured solution thanks to software developed as part of their Addressing Radiologist Recommendations Collaboratively project.
The ARCC software is a closed-loop communication system that’s designed to channel radiologist recommendations into a structured format that’s clearly understood, while also tracking whether they were accepted and fulfilled. The ARCC tool runs separately from the radiologist’s dictation software, so while it asks them to include a standardized recommendation sequence in their report, it leaves the specific free-text language up to them.
Under the ARCC criteria, the main factors that make up a complete follow-up recommendation are:
- Reason for imaging study
- Timeframe when study should be completed
- Imaging modality to be used
The researchers implemented the ARCC software in October 2019 in thoracic imaging, and rolled it out to other departments through December 2020. Use of the software was “strongly encouraged but voluntary.”
In testing the ARCC software’s effectiveness, the researchers found that the number of follow-up recommendations considered to be complete – with all three key elements – rose from 14% to 46%. Even so, one-third of reports filed with ARCC “still contained ambiguous language” in the free-text section – indicating that old habits are hard to break.
Radiologists may hate it when their recommendations for follow-up imaging are ignored, but referring physicians are also frustrated with free-text radiology reports that are wishy-washy and contain vague impressions. The ARCC software could bridge the gap by steering radiologists toward recommendations that are more concrete and specific – and more likely to be followed.