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The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

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Architecting the Data Loading Process for an i2b2 Research Data Warehouse: Full Reload versus Incremental Updating.

Andrew R Post¹, Miao Ai¹, Akshatha Kalsanka Pai¹

¹Emory University, Atlanta, GA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|June 2, 2018

Summary

Daily incremental updates for research data warehouses, like the i2b2 data warehouse, provide fresher data for clinical trial accrual. This approach balances upfront development with potential long-term benefits.

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Area of Science:

Biomedical Informatics
Health Data Management

Background:

Research data warehouses consolidate diverse data for research purposes.
Traditional full reloads present challenges including data staleness and resource burdens.

Purpose of the Study:

To evaluate the implementation and implications of daily incremental updating for a research data warehouse.
To compare incremental updating with traditional full reload methods.

Main Methods:

Implemented daily incremental updates for the i2b2 data warehouse.
Assessed the impact on data availability, source system burden, and IT resource coordination.

Main Results:

Daily incremental updates provide more current data, beneficial for clinical trial accrual.
While requiring upfront development, incremental updates may better suit academic healthcare IT structures.
Ongoing support needs for incremental updates appear comparable or lower than full reloads.

Conclusions:

Incremental updating offers a viable alternative to full reloads for research data warehouses.
This method can enhance data utility and align with academic healthcare IT operational models.