Review ArticleChoice of data extraction tools for systematic reviews depends on resources and review complexity
Introduction
Systematic reviews allow for the rigorous identification, selection, and summary of research evidence to answer a focused question [1], [2]. Increasingly, clinicians, patients, researchers, and policy makers rely on high-quality systematic reviews for decision making. A key step in the review process is the structured extraction of data from eligible primary studies, a procedure that, typically, pairs of reviewers conduct in duplicate and independently. In any systematic review, a crucial task for the project's leader, or in a better-funded situation, a dedicated systematic review coordinator, is managing the data. Several data-extraction tools may assist reviewers in conducting systematic reviews. Selecting the optimal tool requires balancing the upstart and maintenance effort and costs to obtain the necessary functionality for often complex projects.
In this report, we collate the collective experience of several systematic reviewers, from different countries, to offer a reference guide for investigators planning, coordinating, and conducting systematic reviews, henceforth referred to as systematic reviewers, choosing and designing data-extraction procedures for systematic reviews. To our knowledge, this is the first assessment of data-extraction tools for conducting systematic reviews.
Section snippets
Methods
We drafted a preliminary table capturing the experience of two systematic review coordinators (M.B.E. and D.N.F.) that outlined the most common data-extraction tools used in our systematic review group, the Knowledge and Encounter Research (KER) Unit at Mayo Clinic in Rochester, Minnesota [3]. The tools used for data extraction were paper and pencil and their equivalent electronic word processing software, spreadsheets, local relational databases, web-based forms, and electronic web-based
Results
Table 1 describes the collected tools and characteristics that resulted from our exploration. Additionally, Table 2 describes the different ways to distribute the selected tool to reviewers, a key consideration in selecting extraction tools.
Discussion
The data-extraction step in a systematic review requires extensive planning and piloting. This planning includes not only the design of the data-extraction procedures, but also the selection of the optimal tool and piloting its performance in conducting the specific review. Volume, nature, and complexity of the data, the number, location, and computer literacy and access (both to a computer and to the Internet) of the reviewers, and the available funding for the project play a major role in the
Conclusion
No single data-extraction method is best for all systematic reviews in all circumstances. Before selecting a tool, reviewers must bear in mind the volume, nature and complexity of the data, the number, location, and computer literacy and access (both to a computer and to the internet) of the reviewers, and the available funding for the project. Planning and careful selection and piloting of the appropriate data-extraction tools are paramount in efficiently yielding data for a high-quality
Acknowledgment/funding
Matthias Briel is supported by the Swiss National Foundation (PASMA-112951/1). Holger Schünemann is supported by a “The human factor, mobility and Marie Curie Actions Scientist Reintegration” European Commission Grant: IGR 42192. Regina Kunz is supported by santesuisse and the Bangerter-Rhyner-Foundation.
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