A value based care mindset calls for a value based care toolset. Conserus Workflow Intelligence helps radiologists demonstrate their value to the rest of the enterprise. It’s a rules engine that helps you get information and tasks to the right people at the right time. With this automated solution in place, you can match demand to supply and use your available resources and talents most effectively.

This solution is flexible, configurable and vendor-neutral. It consolidates a radiologist’s entire set of everyday tasks into a dynamic worklist and uses up to 99 levels of priority to determine which ones should be read first. The user interface is color coded so users can easily see which studies are the most urgent. It’s also dynamic, so a study’s priority level automatically adjusts and it moves to the top of the list as it becomes more urgent. It can even be configured to send alerts if studies aren’t read within your defined time period. It helps you close communication loops between departments and facilities

Whether your healthcare system involves one facility with a single PACS, or is a complex, distributed environment, Conserus Workflow Intelligence can help you make impactful, measurable improvements in areas like turnaround times and case prioritization. And once you’ve measured your progress, you can easily reconfigure your rules so your workflows can get more efficient.

Conserus Workflow Intelligence helps you improve quality and efficiency, all within your regular PACS workflow. To learn more about how it can help you improve communication and turnaround times in your health system, watch this brief video. 

A value based care mindset calls for a value based care toolset. Conserus Workflow Intelligence helps radiologists demonstrate their value to the rest of the enterprise. It’s a rules engine that helps you get information and tasks to the right people at the right time. With this automated solution in place, you can match demand to supply and use your available resources and talents most effectively.

This solution is flexible, configurable and vendor-neutral. It consolidates a radiologist’s entire set of everyday tasks into a dynamic worklist and uses up to 99 levels of priority to determine which ones should be read first. The user interface is color coded so users can easily see which studies are the most urgent. It’s also dynamic, so a study’s priority level automatically adjusts and it moves to the top of the list as it becomes more urgent. It can even be configured to send alerts if studies aren’t read within your defined time period. It helps you close communication loops between departments and facilities

Whether your healthcare system involves one facility with a single PACS, or is a complex, distributed environment, Conserus Workflow Intelligence can help you make impactful, measurable improvements in areas like turnaround times and case prioritization. And once you’ve measured your progress, you can easily reconfigure your rules so your workflows can get more efficient.

Conserus Workflow Intelligence helps you improve quality and efficiency, all within your regular PACS workflow. To learn more about how it can help you improve communication and turnaround times in your health system, watch this brief video. 

A discussion with Andy Colbert, managing director and founding member of Ziegler’s Healthcare Investment Banking practice, on the reasons for and strategy involved in the business trend of radiology practice consolidation. He spoke to ITN at RSNA 2016. Read the blog “Risk Abatement May Determine the Future of Radiology,” and the article “Opportunities for Growth in a Competitive Radiology Climate.”
 

Learn more about myQA, IBA’s unique platform that connects QA applications, people, and know-how through a central database and the Cloud. It offers full support throughout all of your QA, and enables you access to the different software modules and all of your data from one intuitive interface – anywhere and anytime.

A discussion with Simon Dixon, M.D., MBChB, on the use of fractional flow reserve-computed tomography (FFR-CT) to evaluate chest pain patients in the emergency department. He is chairman of the Department of Cardiovascular Medicine at Beaumont Health System and a professor of Medicine at the Oakland University William Beaumont School of Medicine. He discussed the first year of experience with FFR-CT at Beaumont Hospital in Royal Oak, Mich., during the Transcatheter Cardiovascular Therapeutics (TCT) 2016 annual meeting. Read the article “Clinical Applications of FFR-CT.”

At RSNA 2016, the key buzzwords were “deep learning,” “machine learning” and “artificial intelligence.” Vendors and major academic centers are developing a wide array of artificial intelligence neural networks to aid radiologists in clinical diagnosis and clinical decision support. In the future, AI may also be able to help train radiologists on both normal and abnormal presentations of various organs and body systems so as to more easily identify related disease states and conditions. The following video offers two examples of how the IBM Watson system examines imaging studies.

The first case seen here demonstrates how Watson can arrive at a differential diagnosis of an aortic dissection by analyzing an abdominal computed tomography (CT) scan. The second case involves the discovery of a fibroadenoma of the breast from Watson’s analysis of a mammogram.

Watson first analyzes the text of the radiology report, identifying and pulling out key words or phrases that may indicate the potential diagnosis. It then examines the CT scan to locate relevant visible anatomic structures such as the heart, aorta and pulmonary artery. Each structure is examined for anomalies, which identifies a possible aortic dissection; the dissection is displayed within the context of the entire 3-D CT scan. Finally, Watson applies its existing clinical knowledge to the findings from the CT scan and the radiology report, establishing pathways to numerous possible conclusions until arriving at the right one.

See examples of real products using AI at RSNA 2017 in the VIDEO "Examples of How Artificial Intelligence Will Improve Medical Imaging."  ITN also created an in-depth VIDEO: Technology Report — Artificial Intelligence at RSNA 2017,  with interviews with numerous AI vendors.

Watch the VIDEO: “Development of Artificial Intelligence to Aid Radiology,” an interview with Mark Michalski, M.D., director of the Center for Clinical Data Science at Massachusetts General Hospital, explaining the basis of artificial intelligence in radiology.

Mahadevappa Mahesh, MS, Ph.D., chief physicist and professor of radiology and radiological science at Johns Hopkins Hospital, explains the basics of medical imaging dose monitoring technologies. This includes monitoring and recording software meet new Joint Commission requirements, state dose laws and to improve patient safety regarding X-ray radiation exposure. Read the article “The Role of Dose Tracking Systems in Radiation Safety Programs.”
 

Emanuel Kanal, M.D., director of MRI services and professor of radiology and neuroradiology at the University of Pittsburgh Medical Center, explains what is known about MRI contrast retention in the brain and other MRI safety concerns. He spoke to ITN at RSNA 2016. 

Gadolinium-based contrast agents have been used for diagnosis and treatment guidance in more than 100 million patients worldwide over the past 25 years. These agents enhance the quality of MR images by altering the magnetic properties of nearby water molecules in the body. By improving the visibility of specific organs, blood vessels or tissues, contrast agents help physicians diagnose and treat a wide variety of medical conditions. On its own, gadolinium can be toxic. Therefore, when used in contrast agents, gadolinium is bonded with a molecule called a chelating agent, which controls the distribution of gadolinium within the body. 

Read the article "Gadolinium May Remain in Brain after Contrast MRI."

Mark Michalski, M.D., director of the Center for Clinical Data Science at Massachusetts General Hospital, explains the basis of the utilization of artificial intelligence (aka deep learning and machine learning) in radiology. He also explains where things are at in development of these neuro networks at RSNA 2016. Watch the VIDEO “Examples of Artificial Intelligence in Medical Imaging Diagnostics.” 
 

Sabrina Newell, MS, RCS, clinical analyst at healthcare consulting firm MD Buyline, explains some of the trends and new technology in ultrasound at RSNA 2016. For more information about cardiac ultrasound advances, watch the video "Trends and Advances in Echocardiography at ASE 2016."

Accuray recently unveiled the company’s newest innovation, the Radixact Treatment Delivery System, a uniquely smart, fully integrated treatment delivery, treatment planning and data management system. Hear from the Accuray team about this new, next-generation TomoTherapy platform, as well as how recent innovations to the CyberKnife platform reduce treatment time while providing leading-edge motion tracking and real-time beam adjustment.  Discover how Accuray systems can help clinicians deliver precise tumor treatments with confidence.

Leveraging RESTful web services to unify communications between various health IT platforms the Unifier solves a wide range of interoperability challenges. Visit Dicom Systems’ RSNA Booth 1508 for a live demo.

Rachael Bennett, clinical analyst for MD Buyline, discusses trends in radiation oncology at ASTRO 2016.

Contributing Editor Greg Freiherr offers an overview of digital radiography (DR) advances at the Association for Medical Imaging Management (AHRA) 2016 meeting. Read the article “The Coming Push for DR.”  Watch a technology report sidebar video on new DR Systems technology.

See how Carestream's exclusive Web Portal and personalized dashboard can give users peace of mind.

How to turn your imaging center into a Swiss army knife to maximize ROI was a key radiology business session at the 2016 Association for Medical Imaging Management (AHRA) annual meeting in Nashville, Tenn. Robert Junk, AIA, and Tobias Gilk, M.Arch, of RAD-Planning, explain how radiology departments can assess their business model to make the most of their capital investments. Read the related article that include Junk and Gilk "Rethinking the Radiology Business Model."
 

Interview with Kent Hutson, M.D., CPE, of Radiology Alliance in Nashville, Tenn., on the principles of data mining and how they can be used in radiology, at the 2016 Association for Medical Imaging Management (AHRA) annual meeting. For more information, read the article "Analytics: The Next Big Health IT Undertaking."
 

AAPM President Bruce Curran, MEng, FAAPM, FACMP, FACR, discusses the key topics at the 2016 meeting of the American Association of Physicists in Medicine. Topics included the advancement of radiomics in medical imaging for quantification for evidence-based clinical decision making in cancer, and improving how quality assurance is done in radiation therapy. 

At SIIM 2016, itnTV caught up with opening keynote speaker and SIIM treasurer Rasu B. Shrestha, M.D., MBA, Chief Innovation Officer, University of Pittsburgh Medical Center and Executive Vice President, UPMC Enterprises, to discuss the dynamic changes that imaging is facing today.

Eliot L. Siegel, M.D., Dwyer Lecturer; Closing Keynote Speaker, Vice Chair of Radiology at the University of Maryland and the Chief of Radiology for VA Maryland Healthcare System, talks about the current state of the industry in computer-aided detection and diagnosis at SIIM 2016.  Watch the VIDEO “Development of Artificial Intelligence to Aid Radiology,” an interview with Mark Michalski, M.D., director of the Center for Clinical Data Science at Massachusetts General Hospital, explaining the basis of artificial intelligence in radiology. Watch the VIDEO “Examples of Artificial Intelligence in Medical Imaging Diagnostics.”

At SIIM 2016, Paul G. Nagy, Ph.D., CIIP, FSIIM, discussed the main challenges to adaptive change in healthcare IT, and the adaptive leadership skills necessary to complement technological changes in a clinical setting. He also discussed his vision for SIIM as he assumes leadership as incoming chair.