Standardizing Coding in HIEs

June 24, 2013
With the proliferation of health information exchanges (HIEs), standardization in coding and vocabularies has never been so important. According to a recent KLAS report, live HIEs have more than doubled from last year, up to 228 from 89 in last year’s study.
With the proliferation of health information exchanges (HIEs), standardization in coding and vocabularies has never been so important. According to a recent KLAS report, live HIEs have more than doubled from last year, up to 228 from 89 in last year’s study. (For more about the KLAS report, “Health Information Exchanges, Rapid Growth in an Evolving Market,” check our website soon for an interview with the report’s lead author Mark Allphin.) With more HIEs sprouting up, there needs to be standards in place in order to successfully harvest this data for usable analytics.In an interview with Eric Miller, vice president, information technology, Indiana Health Information Exchange (IHIE), he emphasized that his exchange spent a great deal of time normalizing data for analysis. “The way we’re structured with common vocabulary and consistent databases under the centrally managed federated model it lets us add a lot of value from an analytics perspective, and also the ability to do research on the data,” he says.“When the data comes in, we have to map it to a standardized vocabulary for our repository,” Miller adds. “We retain the original code set, but we also map it and tag it with a standardized vocabulary so we can do the analytics across the data set.”In the current healthcare landscape where most organizations use their own local code sets for clinical data, there has been, and must continue to be, a movement toward using LOINC and other standard code sets. This work has to be in addition to normalizing the meaning of the data, which I wrote about in an earlier post. Standardizing the meaning of the data is imperative so that when organizations compare data across different organizations, such as blood glucose levels, there is a common understanding that these levels were all taken after fasting, so the data can be properly compared.Joe Bormel, M.D., CMO, QuadraMed, made a great comment on that previous post on standardizing meaning in HIEs. He said, “HIEs, like any powerful tool, can lead to either good or bad impacts, depending on how appropriately data is captured and interpreted.” He went on to note that the problem is endemic in physician documentation as well, where capturing elements like when specific medications are continued or discontinued during medication reconciliation, and why various Medicare Quality Measures are ignored, is another frontier for healthcare IT.That’s why adopting national coding standards, or checking out the standards your state or region are adopting, is important to consider when thinking about HIE. Undoubtedly, the ONC will make more stringent requirements for HIE coding standards in future stages of meaningful use, so continuing these discussions on standardization is key.

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