HeartIT, the global leader that pioneered the first FDA approved zero-footprint medical imaging workstation, announced today that it has received a $2.5M grant from the NIH in order to develop the "Cloud Based Corelab of the Future."
Medical drugs and devices are regulated by the U.S. Food and Drug Administration (FDA) based on data from multicenter clinical trials. Over the past decade the efficiency of clinical trials has been improved by electronic data capture (EDC) systems, whose use has increased from 38% to 61% from 2006 to 2011. While medical imaging plays a key role in clinical trials, often serving as the basis for study end-points and/or safety evaluations, few if any EDC systems integrate imaging corelab workflows such as image quality assurance, site queries, and reporting of end-point analyses.
The overall goal of the NIH grant is to create a "cloud-based imaging corelab of the future" that provides support for all imaging corelab activities and integration into EDCs.
HeartIT is already a leader in the Clinical Trials market ( http://www.heartit.com/index.php/solutions ), and received an additional $2.25M NIH grant in 2011 ( http://www.heartit.com/index.php/2011/11 ).
Dr. Robert M. Judd, President of HeartIT stated: "Heart Imaging Technologies developed the world's first "zero footprint" medical image viewer, WebPAX®, which displays diagnostic-quality images in a bare bones web browser. We are the natural choice for this type of project. By moving imaging corelabs out of their traditional 'brick-and-mortar' environments and into the 'cloud,' medical images can be interpreted from anywhere in the world within minutes rather than weeks."
Judd added, "Moving corelabs to 'the cloud' will not only improve corelab workflows, but will also improve communication between the corelab and the other clinical trials stakeholders such as the CRO, the data safety and monitoring board (DSMB), the sponsor, and the FDA. In addition, making images available rapidly will enable new trial designs that reduce site-to-site variability of the study population, thereby reducing the number of patients needed to detect treatment effects, trial durations, and trial costs."
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