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Related Experiment Video

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Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews:

Ian J Saldanha1, Christopher H Schmid2, Joseph Lau3

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room W6507-B, Baltimore, MD, 21205, USA. isaldan1@jhmi.edu.

Systematic Reviews
|November 24, 2016
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Summary

This study evaluates the Data Abstraction Assistant (DAA), a software tool designed to improve the accuracy and efficiency of data abstraction in systematic reviews. The randomized controlled trial compares DAA-facilitated abstraction with traditional methods to provide evidence for best practices.

Keywords:
Data abstractionRandomized controlled trialSystematic reviews

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Area of Science:

  • Medical Informatics
  • Systematic Review Methodology
  • Health Services Research

Background:

  • Data abstraction in systematic reviews is crucial but often time-consuming and error-prone.
  • Current data abstraction standards lack a strong evidence base.
  • The Data Abstraction Assistant (DAA) software was developed to streamline this process.

Purpose of the Study:

  • To compare the effectiveness of DAA-facilitated data abstraction versus traditional methods.
  • To evaluate the accuracy and efficiency of different data abstraction approaches.
  • To provide evidence for strengthening systematic review data abstraction recommendations.

Main Methods:

  • A randomized controlled trial (RCT) involving 24 pairs of abstractors (48 participants).
  • Three-arm, crossover design comparing (A) DAA-facilitated single abstraction + verification, (B) traditional single abstraction + verification, and (C) traditional independent dual abstraction + adjudication.
  • Data abstraction performed using the Systematic Review Data Repository (SRDR).

Main Results:

  • Primary outcomes include the proportion of data abstraction errors and total time taken.
  • The study aims to determine which method yields the highest accuracy and efficiency.
  • Results will inform best practices for data abstraction in systematic reviews.

Conclusions:

  • The DAA trial will offer critical evidence on optimizing data abstraction processes.
  • Findings are expected to enhance the reliability and speed of systematic reviews.
  • This research addresses a significant gap in the evidence base for systematic review methodology.