Health information exchanges (HIEs) are vital components of the health IT ecosystem, helping to facilitate the efficient and safe exchange of patient data between providers at either the state or regional level. The ultimate goal of the HIE is to improve the efficiency, quality, and cost-effectiveness of patient care by ensuring this data exchange is fast, accurate, and secure. This is more involved than just sending one file back and forth, though. HIEs strive to transfer a complete, up-to-date patient record – which can include hundreds of files in various formats.
It’s a task that’s is easier said than done.
First, consider the subjectiveness of documentation at the point of care. The clinical terminology and use of standardized medical coding terms will naturally vary between providers. On top of this, there is no universal standard for how data from the patient record must be formatted when sent from different health systems to HIEs. So, one system may send local codes, one may send free text descriptions, another may only send primary standard codes, and yet another may send all primary and secondary codes available. This lack of uniformity in EHR terminology, where one patient may only have ICD-10-CM codes and someone else has only free text descriptions, creates gaps in different patient records and presents a major challenge for HIEs to address.
Extrapolate this out to numerous health information systems across a regional or statewide HIE network, and it’s easy to see how this setup has the potential to create a large volume of very inconsistent patient data.
Transforming this data into a complete patient record by filling in any missing data, like corresponding CPT terms or SNOMED CT codes, is a monumental task for HIEs and requires significant time and resources. Moreover, common solutions like code crosswalks – translations or mappings between two or more standard code sets – consistently fall short. Because of this, some HIEs are seeking out services that can standardize this disparate patient data from various systems into a common, clinical terminology, a process known as data normalization.
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