Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies
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Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies Krishna Margadhamane Gokhale1,2,4 · Joht Singh Chandan2 · Konstantinos Toulis2 · Georgios Gkoutos3,4 · Peter Tino1 · Krishnarajah Nirantharakumar2,4 Received: 12 June 2019 / Accepted: 12 August 2020 © The Author(s) 2020
Abstract The use of primary care electronic health records for research is abundant. The benefits gained from utilising such records lies in their size, longitudinal data collection and data quality. However, the use of such data to undertake high quality epidemiological studies, can lead to significant challenges particularly in dealing with misclassification, variation in coding and the significant effort required to pre-process the data in a meaningful format for statistical analysis. In this paper, we describe a methodology to aid with the extraction and processing of such databases, delivered by a novel software programme; the “Data extraction for epidemiological research” (DExtER). The basis of DExtER relies on principles of extract, transform and load processes. The tool initially provides the ability for the healthcare dataset to be extracted, then transformed in a format whereby data is normalised, converted and reformatted. DExtER has a user interface designed to obtain data extracts specific to each research question and observational study design. There are facilities to input the requirements for; eligible study period, definition of exposed and unexposed groups, outcome measures and important baseline covariates. To date the tool has been utilised and validated in a multitude of settings. There have been over 35 peer-reviewed publications using the tool, and DExtER has been implemented as a validated public health surveillance tool for obtaining accurate statistics on epidemiology of key morbidities. Future direction of this work will be the application of the framework to linked as well as international datasets and the development of standardised methods for conducting electronic pre-processing and extraction from datasets for research purposes. Keywords Epidemiology · Computer science · Extract · Transform · Load · Observational study · Research methods
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10654-020-00677-6) contains supplementary material, which is available to authorized users. * Krishna Margadhamane Gokhale [email protected] * Krishnarajah Nirantharakumar [email protected] 1
School of Computer Science, College of Engineering and Physical Sciences, University of Birmingham, Birmingham B152TT, UK
2
Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B152TT, UK
3
Chair of Clinical Bioinformatics, Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham B152TT, UK
4
Health Data Research UK, Birmingham, UK
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