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Syst Rev. 2016 Nov 22;5(1):196.

Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial.

Author information

1
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room W6507-B, Baltimore, MD, 21205, USA. isaldan1@jhmi.edu.
2
Department of Biostatistics, and Center for Evidence-based Medicine, Brown University School of Public Health, Providence, RI, USA.
3
Department of Health Services, Policy and Practice, and Center for Evidence-based Medicine, Brown University School of Public Health, Providence, RI, USA.
4
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room W6507-B, Baltimore, MD, 21205, USA.
5
Epidemiology, Johnson & Johnson, Titusville, NJ, USA.
6
Center for Evidence-based Medicine, Brown University School of Public Health, Providence, RI, USA.
7
Department of Medicine, University of California San Francisco School of Medicine, San Francisco, CA, USA.
8
Internal Medicine, Kaiser Permanente Northwest, Portland, OR, USA.
9
National Research Council Information and Communications Technologies Portfolio (NRC-ICT), Ottawa, ON, Canada.
10
Northeastern University College of Computer and Information Science, Boston, MA, USA.
11
Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
12
College of Medicine, and Evidence-based Practice Center, Mayo Clinic, á…źRochester, MN, USA.
13
California Breast Cancer Organizations, Davis, CA, USA.
14
Cochrane Consumer Network, Fredericton, NB, Canada.

Abstract

BACKGROUND:

Data abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or "pin") the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction.

METHODS:

This is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors (i.e., sample size is 48 participants), each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction (by two abstractors in the pair, including verification and/or adjudication).

DISCUSSION:

The DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches.

TRIAL REGISTRATION:

The trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li . All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion.

KEYWORDS:

Data abstraction; Randomized controlled trial; Systematic reviews

PMID:
27876082
PMCID:
PMC5120497
DOI:
10.1186/s13643-016-0373-7
[Indexed for MEDLINE]
Free PMC Article

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