About Systematic Reviews
Data Extraction Tools for Systematic Reviews
Automate every stage of your literature review to produce evidence-based research faster and more accurately.
Data extraction tools such as DistillerSR, for example, can help better manage, categorize, and refine data gathered from studies in a systematic review, leading to more accessible, understandable, and organized results.
What Is Data Extraction?
Data extraction is a step of the systematic review process that involves collecting, analyzing, and organizing data to develop evidence and summary tables based on the characteristics of a study, its results, or both. These tables will help determine which studies are eligible for synthesis and provide detailed information and a high-level overview of research findings. Data supposed to be used in systematic reviews are usually from a variety of sources, many of which are completely unstructured or poorly organized.
Data extraction requires a lot of planning, and it’s recommended that a research team has at least two reviewers to reduce bias and errors, and to properly organize a systematic review. This is necessary to ensure that information is relevant, accurate, and complete, and is presented in an accessible way for data sharing and future review updates.
What Data To Extract For A Systematic Review
Systematic reviews gather data from several sources of primary research. These include:
- Journal articles
- Conference abstracts
- Errata and letters
- Trial registers
- Clinical study reports (CSRs)
- Regulatory reviews
- Participant data
Extracting data from these is done based on pre-established guidelines, which guide the types of data and sources that need to be analyzed based on the purpose of the systematic review. Some information to be considered are:
- Title
- Author
- Year of publication
- Journal
- Topic, research question, or hypothesis
- Purpose
- Conceptual framework
- Research methodology or study type
- Interventions
- Outcomes and conclusions
Learn More About DistillerSR
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What Is a Data Extraction Tool?
Data extraction tools are tools used to manage data in systematic reviews. There are several available ones, including paper forms, electronic forms, and specialized software.
Each tool offers benefits and drawbacks: specialized web-based software is well suited in most ways but is associated with higher setup costs. Other approaches vary in their setup costs and difficulty, training requirements, portability and accessibility, versatility, progress tracking, and the ability to manage, present, store, and retrieve data.
Paper Forms
Paper forms refer to the manual recording of data, i.e., the use of pen and paper. This is a low-cost, low-resource use that’s suitable for small-scale reviews (with less than 10 included studies reviewed by a small team). That said, extra care must be taken with this method since it’s susceptible to human error and it can be difficult to amend when changes arise.
Electronic Forms
Electronic forms, which refer to online software, allow data to be processed, edited, stored, shared, and collated electronically. They make the step easier and faster while reducing the risk of errors (versus paper forms). These are best suited for small- to medium-scale reviews of teams with a few more resources, granted that the reviewers have familiarity with the software packages.
Data Systems
Data systems, like DistillerSR, are the most versatile and efficient data extraction tools suitable for all systematic review scales and team resource levels. They offer a wide range of benefits, including systematic review-specific functions (data automation, data integration, data export, etc.) and simultaneous access for all reviewers. Depending on your needs, it can require a bit of investment, both financially and in the effort that it takes to learn how to use it, but the tradeoff is worth it.
Tips For Extracting Data for A Systematic Review
Data extraction is an important stage in a systematic review. To derive comprehensive, accurate, and relevant data summaries, you must use an exceptional data extraction tool like DistillerSR, obtain the necessary training for quality data extraction, establish standards to guide the process, and pilot your data extraction forms to ensure the recording of similar data, and be meticulous with the gathering and recording of data and practices. You must appropriately define your research question and objectives, develop appropriate criteria for including studies, and strictly follow the specified criteria to enable effective data extraction.
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