Open AccessThe development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews
Author(s) -
Fala Cramond,
Alison O’Mara-Eves,
Lee Doran-Constant,
Andrew S.C. Rice,
Malcolm Macleod,
James Thomas
Publication year - 2018
Publication title -
wellcome open research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.298
H-Index - 21
ISSN - 2398-502X
DOI - 10.12688/wellcomeopenres.14738.1
Subject(s) - data extraction , computer science , process (computing) , data mining , software , reliability (semiconductor) , the internet , graphical user interface , graph , data science , information retrieval , world wide web , medline , operating system , power (physics) , physics , theoretical computer science , quantum mechanics , political science , law , programming language
Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process. Methods: To mitigate some of the above problems we developed a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison to standard practice. Results: We found that our new graphical data extraction tool is not inferior to users’ prior preferred current approaches. Our study was not designed to show superiority, but suggests that there may be a saving in time of around 6 minutes per graph, accompanied by a substantial increase in accuracy. Conclusions: Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.