In my last post I looked under the hood at data interoperability, examining the need for the normalization of both "syntactic" and "semantic" aspects of healthcare data. In this post I will present a high-level architecture for data normalization to share some understanding of how health information exchange is implemented in practice.
What If
Realizing the value of SOA
I went to the InfoWorld SOA Executive forum held in New York this week. The theme of the conference was "Realizing the value of SOA". There were several well delivered presentations on the importance of understanding business process first, organizing the right team skills and structure and picking the right early projects in order to crawl, walk and then run with SOA. Without a disciplined approach focused on these basics any technology selection is doomed to either failu
Peeling back the onion of health data interoperability
Data interoperability is vital to today’s healthcare computing environment, allowing clinical information to be effectively and consistently exchanged, compared, and analyzed among healthcare partners such as insurers, pharmacies, affiliated providers, and public health departments. Put simply, data interoperability enables better decision making.
Intel SOA "Soft" Appliance in Financial Services
"Time is money". That old saying is as relevant as ever in the modern financial service markets. Complex, real-time, and algorithmic trading are constantly pushing the envelope of that phase.
Intel SOA Expressway Service Patterns
The way SOA implementations are being implemented today, there's a new demand for middleware component that provides necessary services such as high-speed message level parsing, validation, transformation and translation to different message formats, message-level routing, service management, governance and control. Intel’s high-performance software solution is uniquely positioned to offer variety of such services critical in implementing true SOA.
SOA Trends
Intel's working definition of SOA is that it is a design practice with a strong technology focus, especially on XML, virtualization, manageability (governance), and multi-core processing. Intel SOA Expressway is a core constituent of Intel's approach for implementing Service Oriented Architecture (SOA) in an enterprise. With the emerging trend of XML and Web Services, it is critical to define best practices and design principles behind SOA that address the evolving requirements of modern data center and to align the software architecture with data center architecture.
A Vision for Scalable High Quality Health Information Exchange - Part 3
This is the third of a three-part article looking at the area of interoperability and health information exchange (HIE) in the healthcare industry. In the first part, I intended to clearly articulate the key challenges and barriers to adoption faced by those looking to engage in HIE.
A Vision for Scalable High Quality Health Information Exchange - Part 2
This is the second of a three-part article looking at the area of interoperability and health information exchange (HIE) in the healthcare industry. In the first part, I intended to clearly articulate the key challenges and barriers to adoption faced by those looking to engage in HIE. Part 2 will examine an architectural approach to address those challenges and discuss some technology enablers to realize a vision for high quality HIE.
Vertical Applications of a SOA "soft appliance"
So far I have been posting the last week or so about how to apply a SOA "soft appliance" to overall scenarios like Federated SOA, SaaS, and Mainframe deployments. XML performance acceleration, in-line / on-the-wire security processing, and
Intel® Summary Statistics Library: how to detect outliers in datasets?
Earlier I computed various statistical estimates like mean or variance-covariance matrix using Intel® Summary Statistics Library. In those cases I knew for sure that my datasets did not contain “bad” observations (points which do not belong to the distribution which I observed) or outliers. However, in some cases we need to deal with datasets which are contaminated with outliers.
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