A typical Healthcare Information Exchange (HIE) accepts data from an array of disparate sources. Often the data it accepts is semantically and syntactically altered by a providing system to satisfy interfacing requirements. However, there are also cases where data from different sources need to be properly merged before reaching the HIE's interface.
It’s been about a year and half since we first released Intel® SOA Expressway for Healthcare, which is a purpose-built, easy-to-deploy integration appliance for connecting islands of information together to enable a secure, high-performance, best-of-breed health information infrastructure. We have now begun shipping release 2.2, which contains a number of new feature enhancements.
Hospitals and IDNs are increasingly looking for new ways to stay connected with their affiliated physicians to enable sharing of health information, streamline the referrals process, and provide seamless access to hospital, lab, PBMs and payer networks. Both hospitals and their affiliated physicians really get excited about the idea of having a consolidated view of patient information and a more integrated workflow, as it is an enabler for increased efficiency, reduced errors and improved outcomes.
My last few posts have looked at the role of data standardization and terminology translation in enabling healthcare organizations to exchange information that can be understand by all. Terminology translation acts as a bridge to make it possible for two organizations to share and understand health data that is "codified" differently.
My last couple posts have touched on the importance of data standards in enabling interoperability in healthcare. It is important to recognize, however, that data standardization is not about dictating the way organizations capture and share clinical data.
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.
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. Part 2 examined an architectural approach to address those challenges as well as some technology enablers to realize a vision for high quality 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.