About Systematic Reviews

What Is the Purpose of Extracting
Data for Systematic Reviews

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Data extraction is an integral part of a systematic review–it refers to the process where reviewers have chosen a relevant study and need to analyze data to form a conclusion.

Using a tool for extracting data from a database for systematic reviews is good practice since it is more efficient and minimizes the potential for the reviewer to make mistakes or apply unintentional bias. There are several examples of data extraction forms for systematic reviews; still, the correct approach should allow a reviewer to collect good quality data that will produce reliable and valid conclusions.

Guidelines for Data Extraction

Documenting procedures and any calculations conducted before analysis is part of record keeping, which allows reviewers to demonstrate transparency and accuracy. Reporting guidelines state that data extraction as part of a systematic review should:

  • Record the source of each data item
  • Follow a detailed yet flexible strategy
  • Use a professionally designed data extraction form
  • Identify potential duplication issues
  • Outline solutions to anticipated problems

Reviewers can answer pertinent questions about their study and data selection by working through the process methodically. For example, can quantitative data be extracted from qualitative data, or is a secondary data source required?

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The Role of Data Extraction in a Systematic Review

Data extraction requires reviewers to retrieve, collate or collect data of varying types from a range of potential sources. Sometimes, that data might be entirely unstructured or without a logical order or categorization that makes it possible to sort.

The reviewer uses data extraction to process that data, refining it so they can highlight those specific metrics or characteristics that relate to the focus of the systematic review. Reviewers used data extraction tools since attempting this manually would be time-consuming, inefficient, and often unviable where large volumes of data are concerned.

Benefits of Data Extraction Tools

Extracting data with a purpose-built tool or resource is a faster way to manage the process without allocating excessive resources toward this element of the broader systematic review.

Data extraction tools offer a range of advantages:

  • Control over data migration from external and third-party sources to collate data into one database
  • Greater agility to consolidate data of different types into one centralized record base or system for future reference and to summarize the data used in the review conclusions
  • Simplified sharing opportunities with common, usable formatting as a way to validate the outcomes of the review
    Reduced potential for error, where manual coding processes increase the prevalence of data entry duplications or mistakes many times over,
  • Some tools have the capabilities to automatically highlight discrepancies for resolution.

Well-planned data extraction allows reviewers to re-enter or categorize large data volumes without compromising integrity. As cloud storage and computing become more prevalent in all industries, advanced data extraction tools have become essential for reviewers to access the wealth of data resources and stored data assets managed online.

For researchers, there is an opportunity linked with wearable tech, data security, technologically connected medical devices, and even Wi-Fi-enabled household appliances. These new tracking resources allow cloud-compatible data extraction tools to add a new depth of value to a range of data metrics.

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