With healthcare initiatives calling for more efficient imaging studies, patients demanding increased access to medical data, and a frustrating lack of imaging interoperability across siloed systems, it’s no wonder that facilities are looking towards technology innovators to lead the way. Ambra Health was joined on a live webinar event by several partners leveraging Ambra for Developers (the first cloud development platform for medical imaging) who are featured in the Ambra Solution Directory, to discuss solutions and collaborations that can lead to better outcomes for facilities and patients alike. Alex Risman CEO & CTO of Realize, Andy Macqueen, VP Business Development at Blackford Analysis, and Shmulik Shpiro, VP Business Development of DiACardio, took part in the panel discussion and offered demos of their forward thinking imaging technologies, each of which provides a way to innovate part of your imaging technology workflow.
What is a major IT/technology goal for your practice or facility?
Here at Ambra Health, we often hear an extensive wishlist of imaging items from our customers. Business continuity when PACS hardware fails, the cost of imaging data storage, and more holistic approaches to viewing the patient record are just a few of the items CIOs often seek to address.
One approach that providers are taking to address some of those imaging strategy concerns is to leverage a cloud archive that serves as a central imaging store. In fact, 71% of audience members polled shared that a major goal of theirs was to get all imaging into one comprehensive archive. More than ever, we’re hearing physicians saying Why Not? when it comes to solving image management hurdles with the cloud because of the anytime, anywhere access it offers, as well as the elasticity of scale, more cost-effective storage, and modern options for interoperability that are inherent in cloud solutions. Once imaging data is freed from being siloed in PACS, it becomes a strategic asset that can be leveraged to drive innovation- deep learning, image analysis automation, and more.
How do you think artificial intelligence will transform imaging in next 3 years?
There has been much talk of AI’s (artificial intelligence’s) potential to streamline read times and improve accuracy. Particularly, machine learning, a set of algorithms that can learn complex patterns and make predictions from data, has shown promise in radiology. 50% of audience attendees believe that in 3 years using artificial intelligence in radiology could help reduce imaging errors, and 30% believe artificial intelligence can work to automate workflows such as patient matching.
Blackford Analysis demonstrated their sophisticated navigation aid for PACS users that can enable instantaneous navigation to the same location across all series in compared studies with a single click. Radiologists open an exam from within their existing workflow using Blackford to identify an anomaly, and with a single click, relevant prior images are opened to the same location as the previous exam automatically. Andy Macqueen shared that, “Physicians have experienced 56% faster matching lung nodule locations across serial chest CT exams.”
Realize then launched their AI platform, which can automatically detect diseases in imaging using deep learning. They have a successful, “5% missed lesion rate at 2 false positives per scan in CT lung nodule detection, compared to 23-33% for leading FDA-approved alternatives,” shared Alex Risman. Innovative imaging tools like Blackford and Realize can build on each other to help drive efficiencies in image processing and analysis.
What challenges is your facility facing that innovative technology solutions could help with?
Audience members shared that above all, improvements for patient care is the area they wish to focus most of their efforts on. After focusing on patient improvements for lung nodule screenings with Blackford and Realize, DiACardio demonstrated how revolutionary technology can offer automated heart function analysis with their award winning LVIVO line. Shmulik Shpiro shared that, “The ability to measure left ventricular ejection fraction automatically is an outstanding addition to the diagnosis and treatment of patients with known or suspected cardiovascular disease.”
During the live panel discussion, Shpiro highlighted that the need for real-time technology is critical in high-pressure environments like the ER and surgical suites. “They need the basic tools to quickly analyze what’s going on for the best possible outcome,” remarked Shpiro. Macqueen also discussed the amazing outcomes that machine learning and artificial intelligence could offer patient care by integrating and analyzing complex data sets. Both noted that the goal is not to remove the physician from the process, but rather aid them in making the best decision possible for a patient. In fact, Risman noted that the job of physicians regarding AI currently should be to keep up with it and use it when it’s good, but “rigorously correct it when it’s bad.”
How do you think innovations in artificial intelligence and predictive analysis will play out in radiology over the next 5 years? Do you see more automated image analysis changing the role of the physician?