Given the wide range of technology options available, it's important for healthcare IT executives to pick the right image management technology and approach for a long-term sustainable solution delivering the desired performance and ROI. This whitepaper explores solutions for multi-layered neutrality, a standards-based framework for unifying medical images and clinical documents across the enterprise and community.
As a leader in medical informatics imaging, FUJIFILM Medical Systems U.S.A. uses the best technology tools to deploy their software solutions at customer sites. VxRail allows FUJIFILM to build for scale and quick deployments that are predictable and repeatable to ensure the availability of critical systems within the healthcare enterprise.
Medical providers and facilities maintain diverse imaging systems that make management, storage and retrieval challenging. Vendor-neutral archives can address the challenges with central storage facilities, a common interface and simplified access. Learn more in this whitepaper.
A hospital’s network is the foundation for the critical applications that run on it, where most of those applications are related to the hospitals core businesses. The return on the investments made in EMR (electronic medical records), PACS (picture archiving and communication system), clinical imaging systems and workstations on wheels, can only be truly realized if those assets are always available to the people in need in a reliable, secure and highly optimized way, at a fixed location, or while mobile.
Find out how to simplify network management and enhance application and service visibility with Smart Analytics and PALM by downloading this whitepaper today.
This study surveyed radiologists about inefficiencies in their workflows, revealing a number of shortcomings with the information technologies radiologists employ to review, interpret, and report diagnostic imaging examinations.
Intel teamed up with Philips to show that servers powered by Intel® Xeon®
Scalable processors could be used to efficiently perform deep learning inference
on patients’ X-rays and computed tomography (CT) scans, without the need for
accelerators. The ultimate goal for Philips is to offer artificial intelligence (AI) to its
end customers without significantly increasing the cost of the customers’ systems
and without requiring modifications to the hardware deployed in the field.
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