5 Hot Topics in Healthcare Interoperability

In the Healthcare Interoperability Glossary, interoperability is defined as “The ability of two or more systems or components to exchange information and to use the information that has been exchanged.” Even with the industry’s current focus on health information exchange and electronic health records, a truly interoperable healthcare system can at times seem as likely as a pot of gold at the end of the rainbow.

Despite the monumental task at hand, there are many health organizations making significant strides toward interoperability and the ultimate goal – providing patients with quality, accountable care. I asked my good friends at Perficient to provide their insight on the five following interoperability questions. (You can follow Perficient on Twitter @Perficient_HC.)

Special thanks to Michael Planchart, Enterprise Architecture and Interoperability Consultant in Health IT at Perficient, who was kind enough to participate in the interview.

1. When you first begin working with a client, are there common “first steps” you take to make the interoperability process easier?

As a solution architect, I perform several actions when I engage with the client.

First, I take a clear assessment of their current state and available resources, their integration platform and their integration engines.

I perform a very comprehensive “data discovery” of what source systems are providing HL7 messages and other types of discrete data, such as X12 and DICOM objects. This data discovery is carefully documented using specialized templates.

Second, I perform a very thorough discovery of what source systems provide unstructured data, such as clinical notes, discharge summaries, scanned documents, etc.

Third, I perform a gap analysis to determine what type of information they are missing based on common practices in the provider space.

Fourth, I assess what they want to do once they do become interoperable, or what they hope to accomplish, buy conducting a requirements gathering exercise. For example, do they want to perform predictive analytics for clinical use cases? Do they want real time point of care applications? If so, I backtrack to see if they have the required data available from the source systems. I then find out what their capabilities are and where their gaps may be. This is what I call a top-down approach to understanding how the use case will align with their current data entities.

The information I obtain from these steps is used to complete architectural diagrams and other mapping artifacts that provide a comprehensive overview of interfaces running through the integration engine, including the types of standards being used with each connection, and how information is being captured and how the data is being transmitted from one system to another. It also helps to create a conceptual data model of the future state.

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