• Data Translation vs Data Migration

    Migrating medical data is different than migrating other types of data because of stringent accuracy standards and the pervasive inconsistencies in source data.

    That’s why medical data migration needs a different solution. It needs translation!

    Traditional migration options consist of the following:

    1. Automated:  Software is used to convert data from the source to the target EHR with little human intervention. This works if your source data is consistent and every source data point has a match (and only one) in the target EHR. Unfortunately, exceptions to the rule occur in as much as 60% of medical data. To resolve them, the software code is adjusted and the data is run through again. Sometimes, several iterations of this are necessary to move 80% of the data, which is typically considered a “success.”
    2. Hand Keying:  Staff members manually key historical data into the new system. The same 60% exception rate applies. Instead of coding an answer, which is at least somewhat consistent, each person makes an independent decision, potentially resulting in huge discrepancies. Hand keying is four to five times slower than translation, impacting go-live dates and quadrupling labor costs. What’s more, personnel are distracted from their primary job responsibilities.
    3. Scanning:  Although inexpensive, this method makes data virtually unusable in the new system. It isn’t discrete and providers have to open up multiple attachments to access a patient’s history.

    Medical Data Translation: A Better Approach to Data Conversion

    Versio’s proven system combines the efficiency of intelligent automation with the accuracy of skilled human translators to produce unmatched results:  99.8% accuracy with no data left behind!

    Our translators validate one data point at a time to resolve inconsistencies in real-time. Because we can identify and correct errors from your source data, the translation process doesn’t just maintain data quality – it actually improves data quality.

    Read more about the Human Factor.



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