clinical analytics

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Published By: Health Language     Published Date: Mar 12, 2015
Why normalizing your clinical and claims-based data into standard terminologies is critical in supporting forward-thinking initiatives such as big data analytics, population health management, and semantic interoperability among systems.
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Health Language
Published By: Caristix     Published Date: May 03, 2013
Diagnosoft selected Caristix software and consulting. Consulting work included a hands-on workshop to co-design the interface deployment workflows that Diagnosoft customer engagements would require.
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clinical analytics, interoperability, software, consulting, interface deployment, healthcare, medical, cloud computing
    
Caristix
Published By: ALE     Published Date: Dec 05, 2018
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.
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ALE
Published By: IBM     Published Date: Jul 01, 2015
This paper discusses why it is important for healthcare organizations to become data driven, gives examples of organizations that are already leveraging a wide range of big data.
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clinical analytics, enhanced data foundation, data-driven healthcare organization, integrated data sources, data lifecycle management, big data, healthcare analytics solutions, information technology
    
IBM
Published By: SAS     Published Date: Jun 27, 2019
In the quest to understand how a therapeutic intervention performs in actual use – in real medical practice outside the controlled environment of clinical trials – many life sciences organizations are stymied. They rely on one-off processes, disconnected tools, costly and redundant data stores, and ad hoc discovery methods. It’s time to standardize real-world data and analytics platforms – to establish much-needed consistency, governance, repeatability, sharing and reuse. The organizations that achieve these goals will formalize their knowledge base and make it scalable, while significantly reducing turnaround times, resources and cost. Learn the seven key components for putting that structure to real-world evidence – and four ways to take it to the next level.
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SAS
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