Open AccessThe rise of big clinical databases
Author(s) -
Cook J. A.,
Collins G. S.
Publication year - 2015
Publication title -
british journal of surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.202
H-Index - 201
eISSN - 1365-2168
pISSN - 0007-1323
DOI - 10.1002/bjs.9723
Subject(s) - medicine , observational study , big data , data science , data collection , data source , data quality , health care , psychological intervention , quality (philosophy) , database , data mining , computer science , pathology , nursing , economic growth , metric (unit) , philosophy , statistics , operations management , mathematics , epistemology , economics
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.