- Patients’ data is often duplicated in multiple systems. To resolve this issue, a solution that can find corresponding records and properly attach them to a single master source is needed.
- Data may have typos, incorrect values in key identifiers – social security number, DOB, HICN, etc.
- Limited amount of time to download and transform large amount of data (gigabytes) on a daily basis.
- Mixture of source systems: different versions of SQL Server, FoxPro, custom APIs and CSV uploads in different formats.
- Data normalization to achieve field-level data consistency. I.e. same hospital or doctor may be spelled differently in different source systems.
- Scalable data processing workflows which run on multiple servers in parallel.
- Automatic intelligent data analysis and matching algorithm which is tolerant of data variance.
- Configurable semi-automatic mapping engine for data normalization.
- The resulting solution produces point of reference patient information as a superset of data imported from different systems.
- All non-reconciled data mistakes and discrepancies are automatically reported to the responsible party.
- APEX was able to significantly reduce the amount of time spent on manually maintaining patients’ data.