Evaluating the informatics for integrating biology and the bedside system for clinical research

Abstract

BackgroundSelecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive.MethodsOur evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone.ResultsWe found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre- or post-processing, modifications to the hive metadata, etc.ConclusionThe i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data.

Unlocking Data for Clinical Research – The German i2b2 Experience

“…The final query step required the temporal combination of query items in the sense that they had to occur in a specific sequence over time. As it has been noted before [15], i2b2 does not provide functions to define this type of query, so this step could not be carried out in i2b2. The integrated timeline view could have been used to manually examine individual patient histories with regard to temporal conditions, but this approach would not be feasible for selecting records from a large dataset.…”

“…Even though the addition of temporal constraints would be desirable, the increased complexity of the user interface should be balanced against overall usability. Alternatively, data could be preprocessed before importing to provide derived fact items containing the required temporal restrictions [15].…”

“…i2b2 has reached wide adoption in the United States and created an active user community [8][9][10][11][12][13]. i2b2 uses a generic Entity-Attribute-Value (EAV) database schema [14,15] that facilitates rapid integration with additional data sources as well as a simplified user interface that allows clinicians to formulate complex Boolean queries. Limitations of this approach have been discussed, including restrictions in the formulation of queries containing post-coordination as well as certain temporal or aggregated expressions [15,16].…”

Building the Informatics Infrastructure for Comparative Effectiveness Research (CER)

“…Principally, the literature addressed the most well-established efforts, including: caBIG, [36][37][38][39] DARTNet, 40 HMORN, [41][42][43][44]31,[45][46][47]42 REDCap, 48 Sentinel Initiative and Mini-Sentinel, 42,49,50 and TRIAD. 51 These papers provide an overview of various efforts that are underway using clinical informatics for research, many of which include information on the projects' areas of focus; provide progress updates; or describe tools and platforms that have been developed or are in development.…”

Entity Attribute Value Style Modeling Approach for Archetype Based Data

“…However, the use of EAV in i2b2 hinders constraint definition, and thus, i2b2 is completely dependent upon the individual contributing systems for the implication of constraints [59]. In contrast, openEHR provides a dual layer modelling approach that segregates the information (in reference model) from knowledge (in archetype model).…”