Sponsored Content | Whitepapers | Artificial Intelligence | November 11, 2020

A Roadmap for Achieving a Largely Automated, AI and Analytics Assisted Learning Health System

An adjunct or starting point to today’s VNA-based Enterprise Imaging Platform

This paper will focus on the infrastructure of both software and hardware required to support the collection, normalization, and contextualization of all forms of clinical content. Collectively discrete content and imaging data is subsequently acted upon by Artificial Intelligence algorithms in the creation of predictive/suggestive and prescriptive analytics which can be used by clinicians to help reduce turnaround times and help improve patient care through better patient outcomes. The necessary infrastructure can be thought of as a next generation of Vendor Neutral Archive (VNA), or a standalone adjunct to the existing VNA. This new environment, referred to by some software designers as VNAi Services, is the go between inside human/clinical driven workflows vs machine/data driven workflows. In combination with all of the other application components of an enterprise imaging platform, workflow, analytics, visualization, this is the necessary “next step” for a healthcare organization that seeks to meet the Office of the National Coordinator for Health Information Technology’s Interoperability Roadmap for 2025 and thereby achieve a Learning Health System.

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