Guest post from Robin Braun, Chief Commercial Officer, Vyasa
One in eight women will develop breast cancer in their lifetime.
Unfortunately, these numbers only get bleaker when you dive into demographics. For example, Black women are more likely to develop more aggressive and more advanced breast cancer, with industry non-profit Breastcancer.org noting less access to mammography and lower quality medical care as causes.
The COVID-19 pandemic certainly hasn’t helped improve this scenario. The CDC reports sharp declines in breast and cervical cancer screenings during the pandemic, reaching as high as an 87% decline for breast cancer screenings. While screening rates have started to rebound, catching up on postponed care has created immense pressure on healthcare and oncology professionals.
A Strained System
It’s always been up to healthcare providers to remind or follow up with patients to help make sure their regular care takes place. We see this in the form of public service announcements around screenings at a certain age and physicians’ offices calling to remind patients of a yearly check-up.
However, healthcare systems continue to be overwhelmed. With the Journal of American Medical Association (JAMA) noting that the number of cancer patients is outpacing the number of clinicians, we simply can’t rely on traditional methods for keeping track of patients. For instance, analyzing patient data can typically require hours of manually sifting through and analyzing records saved in multiple formats across a variety of locations.
Getting Back on Track with Deep Learning
Advancements in deep learning are revolutionizing the way we access and extract insight from data. At Vyasa, we’ve developed a novel approach to unifying data across various systems, allowing users such as healthcare providers to quickly identify insights they need to complete a given task.
In the case of identifying high-risk patients in need of a cancer screening, healthcare organizations can integrate patient records within our Layar data fabric which catalogs the content and makes it easily searchable. From there, users can ask questions of the data such as “Which patients are at highest risk?” or “Which patients haven’t been seen in the last 18 months?” to identify the most qualified patients for their particular project.
Vyasa quickly pulls the most relevant answers and presents them in an easy-to-navigate knowledge graph. Users can click on each identified patient and access more details within their medical record, ensuring that they are choosing the most viable candidates for outreach regarding an annual scan.
When considering the disproportionate ratio between high-risk patients and physician staff, having the ability to quickly search within patient records means saving valuable time and resources that can be spent on more impactful tasks like direct patient care.
Accelerating Deployment with NVIDIA AI Enterprise
Adopting powerful deep learning capabilities no longer needs to be a headache. NVIDIA AI Enterprise is a software suite that streamlines the often significant complexities of integration with existing infrastructure and new applications that would usually take years to complete. Vyasa, in collaboration with NVIDIA, offers solutions that can be easily deployed, managed and scaled within a healthcare system.
Sustaining a Healthy Patient Population
Managing and accessing relevant data has been a burden on the healthcare system for decades. Fortunately, advancements in deep learning and compute architectures are introducing new approaches to how the industry can leverage critical insights within their data while eliminating many of the headaches associated with this process. By improving operational efficiencies around data analysis, healthcare providers can more easily identify and manage patient journeys, leading to improved paths of care and a healthier patient population.
Robin Braun is the Chief Commercial Officer for Vyasa, driving GTM and Sales. Robin has worked in strategic positions across the technology industry for 25+ years including customer care, GTM, business modernization, quality and partner engagement. Previously, she was the Global Storage CIO for Healthcare at Dell Technologies where she engaged globally in discussing the role of data and AI in healthcare.