Data Extraction Tools and
Techniques for Systematic Reviews
Automate every stage of your literature review to produce evidence-based research faster and more accurately.
The easiest and most effective way to acquire useful data for a study is a process called data extraction. This process of acquiring data for research is very common during systematic reviews. As you prepare for a systematic review, you need to keep in mind how extraction data is evaluated during systematic reviews and become familiar with data extraction forms for systematic reviews.
You also need to familiarize yourself with available data extraction tools and the techniques used for systematic reviews.
What Is a Systematic Review?
A systematic review synthesizes scholarly evidence on a specific topic using specialized techniques to identify, define, and analyze existing research on the topic. When conducting a systematic review, you extract and interpret information obtained from previous studies on the same topic. From the data collected, authors analyze, describe, and summarize their interpretations to support a detailed and logical conclusion.
What Is Data Extraction in Systematic Reviews?
During systematic reviews, data extraction involves selecting the important characteristics in research and organizing the information found in reports and periodical articles in a structured and consistent format. This process is important for evaluating the partiality of each study and blending the findings.
Whether you’re doing interventional, diagnostic, or analytical systematic reviews, you must identify the suitable forms of data extraction. This will save time and money because you won’t have to engage your reviewers in the tedious and repetitive data extraction tasks if it were done manually.
For instance, if you incorporate a data extraction tool into your systematic review processes, you can automate all the critical stages of every process to save time and other resources. Using technology and a data extraction tool for quantitative research is essential because it automates and manages the collection of literature, as well as screening and assessing the collected data using artificial intelligence (AI) and intelligent workflows. It simplifies your systematic review by allowing you to easily manage and configure the available literature for more transparent and compliant results so it will be ready for auditing.
Furthermore, it can be integrated with different data providers like PubMed, AI-powered duplicate detection and deletion, and automatic review updates to make your systematic reviews simpler and more reliable. This means that you can search and obtain references from a data provider within the review software, automatically acquire newly published literature, and identify and delete duplicate citations to avoid skewed or biased reviews.
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Best Data Extraction Techniques for Systematic Reviews
As mentioned above, a systematic review is a comprehensive review and amalgamation of the available evidence on a specific topic using a structured protocol. Although this review is typically done by highly experienced methodologists and subject matter experts, any professional can do it as long as they use the right tools and follow the proper methods.
The basic technique for performing a systematic review involves several critical steps, including formulating the key question, mapping the available evidence, appraising the available literature, synthesizing evidence, and developing a logical summary. To accomplish this, you have to employ the right data extraction tools and methods. These methods are important because the success of your systematic review depends on the success of your data extraction.
You should only include reports that meet the specific criteria spelled out in your protocol; therefore, your methods or results sections must describe the entities that need to be extracted. At least one entity should be automatically extracted with evaluation results. You should not extract any reports that were developed without the data extraction process being applied, lack an evaluation component, or that were developed as editorials, commentaries, or other copied research reports.
You can extract useful data using the search methods described in your protocol. For example, you can use an automation program to search as many electronic databases as possible. Use this program to collect the primary sets of reports for your systematic reviews and develop a search strategy with the help of your protocol.
This technique involves de-duplication of your reference citations–this helps you to calibrate and refine your inclusion and exclusion standards. You can randomly select and independently review as many citations as possible. A good automation tool can help you to screen all retrieved reports. You can also hire an independent reviewer to screen your abstracts as needed. In the event there is conflicting information, choose the third reference to come to a consensus.
These data extraction methods and tools will simplify your systematic reviews and reduce the amount of time you take to complete each review.