The rise of big clinical databases.

Journal article

BACKGROUND: The routine collection of large amounts of clinical data, 'big data', is becoming more common, as are research studies that make use of these data source. The aim of this paper is to provide an overview of the uses of data from large multi-institution clinical databases for research. METHODS: This article considers the potential benefits, the types of data source, and the use to which the data is put. Additionally, the main challenges associated with using these data sources for research purposes are considered. RESULTS: Common uses of the data include: providing population characteristics; identifying risk factors and developing prediction (diagnostic or prognostic) models; observational studies comparing different interventions; exploring variation between healthcare providers; and as a supplementary source of data for another study. The main advantages of using such big data sources are their comprehensive nature, the relatively large number of patients they comprise, and the ability to compare healthcare providers. The main challenges are demonstrating data quality and confidently applying a causal interpretation to the study findings. CONCLUSION: Large clinical database research studies are becoming ubiquitous and offer a number of potential benefits. However, the limitations of such data sources must not be overlooked; each research study needs to be considered carefully in its own right, together with the justification for using the data for that specific purpose.

Authors: Cook, J; Collins, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: John Wiley and Sons Ltd
• Journal: British journal of surgery
• Volume: 102
• Issue: 2
• Pages: e93-e101
• Publication date: 2015-01-01
• DOI: 10.1002/bjs.9723
• EISSN: 1365-2168
• ISSN: 0007-1323
• Language: English
• Keywords: Risk Assessment; Databases, Factual; Observational Study as Topic; Delivery of Health Care; Information Dissemination; Biomedical Research; Data Collection; Research Design; Data Interpretation, Statistical; Patient Outcome Assessment; Humans