Evaluating Different Approaches to Simplifying Data Access for Clinical Users

Abstract

Researchers have difficulty in accessing health care data for multiple different reasons. Although some technologies, like i2b2, have been developed and evaluated to overcome these difficulties, limitations and challenges remain. In addition, there are limited comparisons among query tools, such that users do not have an understanding of which tool works best in which situation. Studies that evaluate and compare such technologies to both guide users and improve tools are needed. To evaluate and compare between two self-service query tools – LEAF and I2b2, and one common data model – OMOP, I selected different representative query questions that are commonly asked by researchers based on externally-defined query categories; quality measurement, based on observational EHR research studies, and representative queries made by users to the analytics team in our organization. Most of the query questions included four main concepts: the diagnosis, patient age, length of stay, and measurement period. I used the three different query tools to answer all query questions. I then analyzed the results to determine which is the best approach to increase data access using the two main determinants in Technology Acceptance Model (TAM): perceived usefulness (PU) and perceived ease of use(PEOU). LEAF, developed by the University of Washington, returned as the best performer among the three query tools due to its flexibility, perceived ease of use, and perceived usefulness. Researchers can easily explore its customized features without needing a programming background. The development of these technologies could reduce the challenges for data access in health care.