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

Research data warehouses integrate research and patient data from one or more sources into a single data model that is designed for research. Typically, institutions update their warehouse by fully reloading it periodically. The alternative is to update the warehouse incrementally with new, changed and/or deleted data. Full reloads avoid having to correct and add to a live system, but they can render the data outdated for clinical trial accrual. They place a substantial burden on source systems, involve intermittent work that is challenging to resource, and may involve tight coordination across IT and informatics units. We have implemented daily incremental updating for our i2b2 data warehouse. Incremental updating requires substantial up-front development, and it can expose provisional data to investigators. However, it may support more use cases, it may be a better fit for academic healthcare IT organizational structures, and ongoing support needs appear to be similar or lower.

A Temporal Abstraction-based Extract, Transform and Load Process for Creating Registry Databases for Research.

In the CTSA era there is great interest in aggregating and comparing populations across institutions. These sites likely represent data differently in their clinical data warehouses and other databases. Clinical data warehouses frequently are structured in a generalized way that supports many constituencies. For research, there is a need to transform these heterogeneous data into a shared representation, and to perform categorization and interpretation to optimize the data representation for investigators. We are addressing this need by extending an existing temporal abstraction-based clinical database query system, PROTEMPA. The extended system allows specifying data types of interest in federated databases, extracting the data into a shared representation, transforming it through categorization and interpretation, and loading it into a registry database that can be refreshed. Such a registry's access control, data representation and query tools can be tailored to the needs of research while keeping local databases as the source of truth.

Synergies and distinctions between computational disciplines in biomedical research: perspective from the Clinical andTranslational Science Award programs.

Clinical and translational research increasingly requires computation. Projects may involve multiple computationally oriented groups including information technology (IT) professionals, computer scientists, and biomedical informaticians. However, many biomedical researchers are not aware of the distinctions among these complementary groups, leading to confusion, delays, and suboptimal results. Although written from the perspective of Clinical and Translational Science Award (CTSA) programs within academic medical centers, this article addresses issues that extend beyond clinical and translational research. The authors describe the complementary but distinct roles of operational IT, research IT, computer science, and biomedical informatics using a clinical data warehouse as a running example. In general, IT professionals focus on technology. The authors distinguish between two types of IT groups within academic medical centers: central or administrative IT (supporting the administrative computing needs of large organizations) and research IT (supporting the computing needs of researchers). Computer scientists focus on general issues of computation such as designing faster computers or more efficient algorithms, rather than specific applications. In contrast, informaticians are concerned with data, information, and knowledge. Biomedical informaticians draw on a variety of tools, including but not limited to computers, to solve information problems in health care and biomedicine. The paper concludes with recommendations regarding administrative structures that can help to maximize the benefit of computation to biomedical research within academic health centers.