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

Ian J. Saldanha, Christopher H. Schmid, Joseph Lau, Kay Dickersin, Jesse A. Berlin, Jens Jap, Bryant T. Smith, Simona Carini, Wiley Chan, Berry Bruijn, Byron C. Wallace, Susan M. Hutfless, Ida Sim, Mohammad H Murad, Sandra A. Walsh, Elizabeth J. Whamond, Tianjing Li

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish (US)
Article number196
JournalSystematic Reviews
Volume5
Issue number1
DOIs
StatePublished - Nov 22 2016

Fingerprint

Information Systems
Software
Randomized Controlled Trials
Public Health Schools
Information Centers
Biomedical Technology
Health Services Research
Research Ethics Committees
Sample Size
Cross-Over Studies
Datasets
Data Accuracy

Keywords

  • Data abstraction
  • Randomized controlled trial
  • Systematic reviews

ASJC Scopus subject areas

  • Medicine (miscellaneous)

Cite this

Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews : Protocol for a randomized controlled trial. / Saldanha, Ian J.; Schmid, Christopher H.; Lau, Joseph; Dickersin, Kay; Berlin, Jesse A.; Jap, Jens; Smith, Bryant T.; Carini, Simona; Chan, Wiley; Bruijn, Berry; Wallace, Byron C.; Hutfless, Susan M.; Sim, Ida; Murad, Mohammad H; Walsh, Sandra A.; Whamond, Elizabeth J.; Li, Tianjing.

In: Systematic Reviews, Vol. 5, No. 1, 196, 22.11.2016.

Research output: Contribution to journalArticle

Saldanha, IJ, Schmid, CH, Lau, J, Dickersin, K, Berlin, JA, Jap, J, Smith, BT, Carini, S, Chan, W, Bruijn, B, Wallace, BC, Hutfless, SM, Sim, I, Murad, MH, Walsh, SA, Whamond, EJ & Li, T 2016, 'Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: Protocol for a randomized controlled trial', Systematic Reviews, vol. 5, no. 1, 196. https://doi.org/10.1186/s13643-016-0373-7
Saldanha, Ian J. ; Schmid, Christopher H. ; Lau, Joseph ; Dickersin, Kay ; Berlin, Jesse A. ; Jap, Jens ; Smith, Bryant T. ; Carini, Simona ; Chan, Wiley ; Bruijn, Berry ; Wallace, Byron C. ; Hutfless, Susan M. ; Sim, Ida ; Murad, Mohammad H ; Walsh, Sandra A. ; Whamond, Elizabeth J. ; Li, Tianjing. / Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews : Protocol for a randomized controlled trial. In: Systematic Reviews. 2016 ; Vol. 5, No. 1.
@article{98ae6c511fb3489cb0de9802d7070bff,
title = "Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: Protocol for a randomized controlled trial",
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",
author = "Saldanha, {Ian J.} and Schmid, {Christopher H.} and Joseph Lau and Kay Dickersin and Berlin, {Jesse A.} and Jens Jap and Smith, {Bryant T.} and Simona Carini and Wiley Chan and Berry Bruijn and Wallace, {Byron C.} and Hutfless, {Susan M.} and Ida Sim and Murad, {Mohammad H} and Walsh, {Sandra A.} and Whamond, {Elizabeth J.} and Tianjing Li",
year = "2016",
month = "11",
day = "22",
doi = "10.1186/s13643-016-0373-7",
language = "English (US)",
volume = "5",
journal = "Systematic Reviews",
issn = "2046-4053",
publisher = "BioMed Central",
number = "1",

}

TY - JOUR

T1 - Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews

T2 - Protocol for a randomized controlled trial

AU - Saldanha, Ian J.

AU - Schmid, Christopher H.

AU - Lau, Joseph

AU - Dickersin, Kay

AU - Berlin, Jesse A.

AU - Jap, Jens

AU - Smith, Bryant T.

AU - Carini, Simona

AU - Chan, Wiley

AU - Bruijn, Berry

AU - Wallace, Byron C.

AU - Hutfless, Susan M.

AU - Sim, Ida

AU - Murad, Mohammad H

AU - Walsh, Sandra A.

AU - Whamond, Elizabeth J.

AU - Li, Tianjing

PY - 2016/11/22

Y1 - 2016/11/22

N2 - 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.

AB - 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.

KW - Data abstraction

KW - Randomized controlled trial

KW - Systematic reviews

UR - http://www.scopus.com/inward/record.url?scp=84997207684&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84997207684&partnerID=8YFLogxK

U2 - 10.1186/s13643-016-0373-7

DO - 10.1186/s13643-016-0373-7

M3 - Article

C2 - 27876082

AN - SCOPUS:84997207684

VL - 5

JO - Systematic Reviews

JF - Systematic Reviews

SN - 2046-4053

IS - 1

M1 - 196

ER -