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.
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.
"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.
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.
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.
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.
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.