Patient-Centered Radiology Requires Facing Patients

DebenedectisThis post was contributed by Carolynn DeBenedectis, MD, a breast imaging specialist and residency program director at UMass Memorial

Sometimes in medicine, radiologists are the first physicians to give a patient a life-changing diagnosis. Recently, I had a patient who came in with a palpable mass in her breast. I was the first physician to tell her this was likely breast cancer and to walk her though all the next steps. I went on to read her breast MRI, discuss the results with her and then perform her MR-guided biopsy. Eventually, I was the person who did her needle localization and sentinel node injection for lumpectomy. It was only later that this patient told me that I — at the most difficult part of her diagnosis, amidst the initial shock and uncertainty — was the physician she had the most contact with and the information I provided made those initial weeks less scary. When you, as a radiologist, are the first physician to help your patient through a life-altering diagnosis, it’s critical to be an effective communicator.

Involving patients and families in their care isn’t only the right thing for radiologists to do, but research has shown that it also improves patient outcomes. Radiologists can and should be available to discuss imaging findings with patients and answer their questions, along with primary care physicians. We can also help coordinate next steps in a care pathway and leverage existing and new technologies to communicate with other physicians and patients. Unfortunately, these critical aspects of patient care are often not addressed in clinical education.

These are just some of the many reasons why, in June, the ACR Patient- and Family-Centered Care (PFCC) Committee on Education launched the new ACR Communication Curriculum for Radiology Residents. This free and interactive resource for residency training programs is customizable and centered on best practices for communications with patients, families and physicians. The curriculum can be incorporated as early as first-year and contains on-demand training modules, patient/doctor simulations, skills assessments and sample case study communication. It also enables residency programs to meet ACGME requirements for resident communication training.

It was an honor to collaborate with my colleagues David Sarkany, MD, and Priscilla Slanetz, MD, and a panel of other experts, to create this much-needed resource for residencies nationwide. It’s our hope that residency programs will leverage this ready-to-use content to provide training that best meets their program’s needs ― training that they may not have otherwise had the resources to provide.

We’ve already heard from several residency program directors and chief residents that this curriculum is making a difference. We hope that you’ll be next ― and we’re here to support you along the way as you prepare future radiologists to add significant value to the care of their patients.

  • How are you training your residents to more effectively engage with patients and families?
  • Have you personally engaged with a patient or their family recently? What difference did your involvement make?
  • For more information and to access the free curriculum, visit

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Connecting the Dots: AI’s Impact on Radiology Economics, Quality and Safety

LaurenGoldingMDThis post was contributed by Lauren P. Golding, MD, a radiologist at Triad Radiology Associates in North Carolina and faculty for the 2018 ACR Annual Conference on Quality and Safety

Artificial Intelligence (AI). There’s no question it will have a big impact on radiology. But don’t buy into all the hype that it’s a threat to our profession. More truthfully, it holds tremendous potential to help radiologists deliver more value through better quality, which will both drive down unnecessary costs and enhance our critical contribution to patient care particularly in the big picture of population health.

This fall, I’ll be among the faculty presenting at the 2018 ACR Annual Conference on Quality and Safety in Boston during the session “The Chicken and the Egg: Economics and Improvement.” We’ll dive into the business case for improvement, regulatory imperatives and opportunities with AI, practical strategies for measuring and communicating the value-add of radiology, and the costs of delivering good and poor quality. Earlier in the day, Dr. Andrew Rosenkrantz will walk us through his work on radiologist accountability for costs associated with incidental findings. Registration is now open, and I hope that many of you will join us.

This isn’t the first time we’ve talked about AI’s impact on radiology economics, and it won’t be the last. As my fellow faculty, Dr. Gregory Nicola, pointed out in a post for this blog last month, AI application will further advance our ability to reduce unnecessary care and variance. AI could also improve efficiency in data management and integration, building upon and amplifying the benefits gained from participation in the Radiology Support, Communication and Alignment Network (R-SCAN™) and registries. With shifts in US payment policy from traditional fee-for-service to value-based reimbursement models in the Medicare Quality Payment Program, AI presents opportunities – and challenges – for reimbursement.

The ACR Data Science Institute™ (DSI) is working to assist radiologists, AI algorithm developers and federal agencies by defining use cases, providing validation services and enhancing performance-monitoring capabilities. The DSI co-hosted the Spring 2018 Data Science Summit: Economics of Artificial Intelligence in Healthcare at the Society for Imaging Informatics in Medicine (SIIM) 2018 Annual Meeting in May. And in August, DSI will co-sponsor the National Institute of Biomedical Imaging and Bioengineering (NIBIB)’s free two-day workshop on AI in Medical Imaging; a workshop that was so popular it reached capacity on the same day that NIBIB opened registration.

We don’t practice in silos. Cost, quality and safety are closely tied together. By examining the whole system and strategically engaging in regulatory and payment policy issues in AI, we can leverage AI to deliver better value for patients, radiologists and the healthcare economy. See you in Boston!

  • What are some ways you are adding value to your practice by improving quality and safety? Has it resulted in an increased ability to control costs as well?

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Building the Artificial Intelligence Road Map

Am. College of Radiology-AMCLC

This post was contributed by ACR Data Science Institute (DSI) Chief Medical Officer Bibb Allen Jr., MD, FACR.

The ACR Data Science Institute™ (ACR DSI) is working with other heavy hitters in the field to create a framework to move artificial intelligence (AI) into medical imaging workflow to improve patient care.

Just this week, the ACR DSI released its first use cases in the TOUCH-AI library to industry leaders for comment prior to the projected release of the use cases in the fall of 2018. Use cases are clinical scenarios in which AI use may improve care. Until now, there has been no national body to identify or develop such cases. No other national organizations are yet involved in determining if algorithms developed by vendors actually answer a relevant clinical question to improve care. This process is an initial focus of the ACR DSI.

The ACR-DSI will also co-sponsor an August 23–24 workshop on AI in Medical Imaging, along with the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and others. The two-day event will discuss gaps in both foundational and translational research in machine learning, data needs for machine learning in clinical imaging, examples of machine learning in the imaging life cycle and implementation issues. The workshop proceedings will be published as a research roadmap to be shared with health professionals in academia, industry and government.

With each passing day, the ACR DSI is laying the groundwork to take artificial intelligence in medical imaging from a novel idea to an everyday experience. This is an exciting time for radiology. Be glad you are here.

  • What are some use cases where you see that imaging AI can help radiologists do their jobs better (more efficiently)?
  • Where do you think imaging AI will be in 510 years?

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