Data Extraction Template for
Systematic Review
Automate every stage of your literature review to produce evidence-based research faster and more accurately.
There are several long-standing challenges to extracting a large volume of data and organizing it logically to enable reviewers to interpret and analyze patterns and trends. A data extraction form for qualitative studies can address all of these pain points.
Generally, reviewers rely on spreadsheets, expanding the document as data becomes available, or is identified as relevant to the theory or topic. However, having a strategy for data synthesis in systematic review exercises is essential to avoid misinterpreting results.
Benefits of Using a Data Extraction Template
A sample data extraction form for systematic review links the review process with primary research as a basis for understanding, summarizing, and appraising the outcomes. Developing and testing such templates is essential in systematic review standards, mitigating the ongoing potential for data extraction errors and false outcomes.
The Prevalence of Data Extraction Errors
Data extraction is central to any systematic review, allowing reviewers to collate data from studies to enable them to answer a question, trace a pattern or formulate an idea.
Error rates in systematic reviews are high:
- Frequencies of outcome errors range from 8% to 63%.
- Inaccuracies cause analytical results to change by over 0.1 in over 70% of cases.
Rigorous, structured data extraction isn’t solely important to verify the correctness of outcome data but to ensure that a systematic review provides sufficient reporting to demonstrate the relevance of primary studies used to provide data sets.
Around 35% of clinical trials and 90% of intervention assessments do not communicate outcomes in enough detail to be replicated, which is often due to the lack of data collection forms.
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Components of a Data Extraction Template for Systematic Review
Data extraction forms are a valuable asset and ensure that systematic reviews quantify and qualify the data sources used, provide verifiable outcomes, and are free from statistically meaningful errors.
Templates are designed around the appropriate data category and can include fixed text, free text, and numerical fields as relevant, although free text should be reduced as far as possible to simplify data analysis. One of the primary advantages is that data can be collated and categorized quickly, sequentially extracting only relevant data and synthesizing that data accordingly.
Reliance on spreadsheets is rarely efficient for systematic reviews involving large studies or subject volumes, or those including meta-analyses. Although a spreadsheet can be customized, the frequency of errors is unacceptably high.
Efficiencies of Using Data Extraction Templates
Reviewers can adapt systematic review data extraction templates to the specific review undertaken, using key question fields and exclusion criteria to inform their work. One of the easiest approaches is to formulate the final data summary table and prioritize those metrics that will inform the summary, excluding those which do not add value, context, or relevance to the review.
When completed, a data extraction form can quickly produce summary tables, identifying the outcomes of each characteristic included within the study without additional data entry. This functionality means that reviewers can formulate conclusions faster, refer back to accurate summary tables, and customize their data extraction processes for maximum efficiency.
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