Systematic Reviews: Data Extraction

This guide provides information and resources which may be helpful when undertaking a systematic review or other type of knowledge synthesis.

Data Collection


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This stage of the systematic review process involves transcribing information from each study using a structured piloted format designed to consistently and objectively capture the relevant details.  Two reviewers working independently are preferred for accuracy.  Data must be managed appropriately in a transparent way and available for future updates of the systematic review and for data sharing. A sampling of data collection tools are listed here.

Data Extraction Elements:

  • Consider your research question components and objectives
  • Consider study inclusion / exclusion criteria
  • Study characteristics such as:
    • Full citation 
    • Setting
    • Duration
    • Objectives
    • Intervention
    • Study Design and methodology
    • Participant characteristics
    • Outcome measures
    • Results
    • Study quality factors

Consult Cochrane Interactive Learning Module 4: Selecting Studies and Collecting Data for further information.  *Please note you will need to register for a Cochrane account while initially on the Mayo network. You'll receive an email message containing a link to create a password and activate your account.*

References & Recommended Reading

1.      Li T, Higgins JPT, Deeks JJ. Collecting data. In: Higgins J, Thomas J, Chandler J, et al, eds. Cochrane Handbook for Systematic Reviews of Interventions. version 6.2 ed. Cochrane; 2021:chap 5.

2.      Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ (Clinical research ed). 2021;372:n160. doi:

         See Item 9 – Data Collection Process and Item 10 – Data Items

3.      Buchter RB, Weise A, Pieper D. Development, testing and use of data extraction forms in systematic reviews: a review of methodological guidance. BMC medical research methodology. 2020;20(1):259. doi:

4.      Mathes T, Klasen P, Pieper D. Frequency of data extraction errors and methods to increase data extraction quality: a methodological review. BMC medical research methodology. 2017;17(1):152. doi:

5.      Hartling L. Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool. BMC Med Res Methodol. 2021 Aug 16;21(1):169. doi: 10.1186/s12874-021-01354-2. PMID: 34399684; PMCID: PMC8369614.