AI Applied Across Healthcare
The possibilities for both Artificial Intelligence (AI) and robotics in healthcare are endless. Just like in our daily lives, AI and robotics are increasingly a part of our healthcare eco-system. It is now essential for automation in radiology and pathology, but it provides a means to support the human, not replace. AI increases the ability for healthcare professionals to better understand the daily patterns and needs of the people they care for, and with that understanding they are able to provide stronger feedback, guidance and support for a proactive healthy lifestyle.
AI is getting extremely sophisticated at doing what humans do, but more efficiently, more quickly and at a lower cost. It puts consumers in control of health and well-being and allows them to work faster when time is everything.
Benefits of a VNA
Most end users discover that there are five primary benefits to utilizing a vendor neutral archive to manage data and images.
One of the obvious values of a VNA is Central and Consolidated Storage Architecture. By managing all supporting documentation (images) and discrete content or evidence documents(reports/results), regardless of its originating clinical system or specialty, an enterprise archive allows an organization to consolidate, centralize storage (hardware and software) and reduce the silos that exist within the organization. Reducing silos in clinical systems and data leverages the management of storage platforms and independent archives. This is the shift from vertical application deployment to enterprise application deployment. In short moving from service line applications to enterprise applications.
True Image Lifecycle Management is an option with the appropriate VNA. And true ILM provides the ability to use storage tiers to reduce the overall cost of archiving data long-term. While some solutions have begun to adopt a model for managing old, outdated and nonrelevant image data for the purpose of purging, there remains no full imaging lifecycle management (ILM) solution on the market at the PACS level with the functionality provided by some VNA vendors.
A robust VNA can become the platform that serves as the Single Point of Integration for all data stored. Redirecting the integration point away from each disparate clinical system and onto the VNA there becomes a single point of integration to system-wide solutions (EHR, EMR, HIE). This reduction in interfaces and integration simplifies the support model, reduces costs and speeds the testing and support to adoption. This is a focus on data normalization and core to ONCs interoperability reimbursement model.
Data Viewing is Simplified. For referring physicians, we strive to make doing business easy. By adopting a VNA solution with a single viewer (either provided by the VNA vendor or utilizing a third-party solution), many are finding a way to reduce the complexity of managing multiple image viewers. This single viewer solution improves the experience for the referring clinical community as well as the support model within IT.
A true VNA solution will mitigate future migrations associated with system replacement or consolidation. Thus, reducing or eliminating future migration costs that are often higher than the cost of a replacement system. The philosophy of a true VNA is that the user is able to manage data, which includes migration efforts, going forward. Rather than vendor-dependent efforts for any data management or conversion initiatives, VNA users are able to take control of the data. This makes system replacement a much easier, and cheaper, option. This also decouple the link between a viewing application and the data directly. By breaking the proprietary connection between the viewing application and the VNA, a goal of a loosely coupled but highly cohesive solution can be achieved.
Related Article: Enterprise Imaging PACS Evolution
Artificial Intelligence in the VNA
The 2019 Radiological Society of North America (RSNA) annual meeting featured a vastly expanded AI presence, indicating that AI is indeed playing a role in the future and growth of the VNA market. And we know that today’s VNAs can now go well beyond simply storing and distributing images. While a traditional VNA may provide essential image archiving for enterprise image management, a next generation VNA (VNAi) utilizes a data framework for archiving and message orchestration to provide a comprehensive health information library. An organization can then easily utilize emerging tools such as machine learning, AI, and analytics for care coordination, population health and outcomes tracking. Due to its speed and knowledge, it is clear that AI in medical imaging can outperform and assist humans in certain tasks.
With a traditional VNA, implementing AI in a diverse environment requires extensive effort to connect, normalize, synchronize, and orchestrate semantics of all data library sources. By implementing VNAi, all structured and unstructured data is ingested by the VNA, indexed and stored within a single archive for a simplified perceived single-point of integration. Next generation VNAs have prepared for this by establishing integration points to connect with third-party AI connectors and AI marketplaces. The result is faster and cleaner data integrations that produce accurate datasets for analysis. The outcome of this cleansed and cataloged data is robust clinical information for diagnosis, stronger AI decision-making, and meaningful analytics across a longitudinal patient jacket and population portfolio.
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