We extract legacy data from retired systems.
Whether it’s a healthcare or human resource/payroll system, we’ll provide the most complete patient and employee data extraction resource to meet your data extraction needs.
MediQuant’s data extraction experts have successfully extracted and mapped thousands of healthcare data records from hundreds of legacy EMR systems, EHR records, other clinical systems as well as ERP and patient accounting applications. Whether a small provider or a large hospital, we’re able to pull discrete and non-discrete data from even the most archaic systems and migrate necessary patient data while offering EMR archival for records that must be maintained. MediQuant has years of experience with data archive products and transferring your data warehouse using a proven, repeatable approach.
Data Migration Process
We specialize in database extraction.
Should you need to read or extract data from any database solution, we can assist with questions and your export request, including reviewing your .dbd file and exporting your database to an ASCII file with your choice of delimiters (.csv, xml, etc…) or to a SQL database.
What our clients say
“Using MediQuant, we created that single, continuous record, even back into the legacy world…if you are in a patient’s EHR today, you can click one button to view important legacy data and also know whether that data had existed in Cerner or Meditech or any of our other legacy systems, who entered it and when.”
“Whenever there was a concern or request, the MediQuant team made necessary adjustments and let us know once they were complete so we could test the change. The open, continuous communication and cooperation MediQuant provided us was imperative to the success of the project.”
“Our physician users found the transition to DataArk to be seamless due to the ease of use of DataArk and the auto-invoke feature. The HIM staff members are able to easily fulfill ROI requests for records that came from disparate systems and our billing staff didn’t miss a beat after the cut-over to DataArk.”