Imaging

Explore how to successfully transform individual departments’ medical imaging into powerful enterprise-wide assets that benefit the clinical, operation and financial health of your care system.

RadNet and vRad have deployed natural language processing tools to help them tackle over-utilization and help perform tasks that are challenging for humans.

According to KLAS, up to 50% of IT implementation projects fail, here's 5 tips to make yours a success.

New imaging techniques are helping radiologists, cardiologists, oncologists and other diagnosticians with greater anatomical and clinical details, highlighting the need for fast access to imaging reports and results and collaborative workflows. Augmented Intelligence can ease the workload on health imaging experts and simultaneously improve their performance. AGFA HealthCare reached out to senior radiologists, surgeons and clinical leaders around the world, and has included their responses in the following perspectives. Task-based workflow optimization AI will not replace radiologists or other physicians, but in fact enhance their workflow, helping them to make collaborative and intelligent decisions. Enterprise imaging, powered by AI, will help improve radiology even further with task-based workflow optimization. Physicians will obtain faster access to critical results, helping to reduce wait times and improve referral services for cases that require urgent patient care coordination. In a recent survey, our respondents emphasized the need for exploring the capabilities of machine learning and AI in addressing certain mistakes and errors that could alert radiographers and radiologists, and automate certain nonessential tasks to ease workload. “We should free our experts to undertake expert work by removing as many nonessential tasks for them as possible,” noted Angie Craig, assistant director of operations and performance, Leeds Teaching Hospitals Trust, National Health Service, United Kingdom. “That’s where artificial intelligence comes in.” Disseminate DL intelligence with AI As we break down silos of imaging workflows and enable multidisciplinary consolidation and collaboration, the power of a consolidated platform results in the creation of a vast data lake, ready for analysis by radiologists, diagnosticians, researchers and academics to help improve quality of care by better understanding disease and population health data. This helps care organizations progress from descriptive to predictive analytics models to improve early detection of diseases, and introduce care plan models that help enforce and improve patient engagement and compliance. During discussions with senior radiologists and diagnosticians, we witnessed a consensus regarding the clinical application of AI to help address screening challenges associated with pressing healthcare problems that include cancers, chronic chest diseases, musculoskeletal conditions, neurological disorders and cardiac conditions as well as the detection of various other clinical conditions. When it comes to oncology, our respondents discussed the significance of AI not only for initial diagnosis but also for follow-up after the treatment in helping track the potential recurrence. “In my group, we’ve already demonstrated that an AI system for chest X-ray triaging and prioritization can lead to much faster reporting turnaround time,” said Giovanni Montana, professor of data science at the University of Warwick, U.K. “We’ve also shown potential diagnostic benefits in early detection of lung cancer.” Personalized medicine and smart applications Care organizations and health authorities across the globe are faced with pressing population health challenges. Whether it comes to detecting cancers or chronic diseases, machine learning and advanced analytics will help radiologists and diagnosticians to focus less on manual repetitive tasks, and more on improving care pathways. “I think one of the biggest contributions deep learning/artificial intelligence will realistically make to me as a radiologist in the near future is not directly helping with image interpretation, but in bringing the relevant information out of the electronic medical record and presenting it to me in a meaningful way to better inform my clinical judgment,” said Bill Anderson, MD, Edmonton zone medical director for diagnostic imaging at Alberta Health Services. “Incorporating this directly into the report will be how we can really add value as radiologists using deep learning. It not only will streamline my workflow but also will be a major step towards more personalized medicine in radiology.” As the healthcare IT industry drives the progress of augmented intelligence, new imaging techniques will personalize and streamline workflow, improve multidisciplinary collaboration and increase predictive analytics across the continuum of care. Building an ecosystem of augmented intelligence powered by machine learning, cognitive reasoning and a task-based rules engine will help enable innovative solutions to meet the need for rapid and accurate care delivery. About the Author: Anjum M. Ahmed, global director of imaging information systems, AGFA HealthCare

The vendor is working to improve workflow, security and efficiency with its Exa platform for radiologists and clinicians.

After years of skepticism and uncertainty, the future of imaging informatics is firmly tied to the cloud, a Frost & Sullivan study says.

The goal is to make screening more comfortable and to curb anxiety.

Partners will collaborate with Google to apply AI to imaging analytics, launch a hybrid cloud VNA and more.

Company says the average hospital generates 50 petabytes of data annually, yet less than 3 percent of that data is actionable, tagged or analyzed.

Ambra Health announced that Atrius Health and Envision Radiology both picked its imaging management service. The cloud-based tools will enable Atrius and Envision to exchange radiology images within their health systems. Ambra said its platform offers vendor neutral archive (VNA) capabilities and a diagnostic viewer user experience. “The cloud is increasingly seen as the best path forward for advancing technology-driven business strategies, efficiently bringing new applications and infrastructure on board, developing new innovation, reducing costs, and improving business and IT agility,” according to research published by the Business Performance and Innovation Network. While the BPI Network’s report involves a broad spectrum of industries, that is proving true in healthcare as well and that includes enterprise imaging. A new report from Reaction Data, meanwhile, suggests that hospitals purchasing practices when it comes to imaging technologies are starting to shift toward a best-of-breed approach, rather than opting for a single vendor for all their radiology needs. The report found that in 2016, for instance, 92 percent of the 269 imaging professionals Reaction Data queried indicated a preference for working with just one vendor but that fell to 77 percent in 2017. “Almost two-thirds of Directors of Radiology and Imaging prefer the simplicity of working with a single vendor, while over half of Radiologists instead prefer the functionality and possibilities of using multiple vendors for their PACS and VNA,” according to the report. Atrius Health’s Administrative Director of Radiology Ray Wilburn said the Ambra service enables clinicians to access imaging done within the region ahead of appointments. “The scheduling staff is able to provide the prior even before the patient walks in the door, saving valuable staff time and reducing redundancies,” Wilburn said. Jeff Emery, CIO of Envision Radiology, added that the cloud-based VNA and image exchange has helped the diagnostic practice “phase out the need for costly and time-consuming image transfer via CDs,” when exchanging images with surgeons and specialists. Twitter: SullyHIT Email the writer: tom.sullivan@himssmedia.com