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 th...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2016-11-01
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| Series: | Systematic Reviews |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13643-016-0373-7 |
| _version_ | 1811225228818251776 |
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| author | Ian J. Saldanha Christopher H. Schmid Joseph Lau Kay Dickersin Jesse A. Berlin Jens Jap Bryant T. Smith Simona Carini Wiley Chan Berry De Bruijn Byron C. Wallace Susan M. Hutfless Ida Sim M. Hassan Murad Sandra A. Walsh Elizabeth J. Whamond Tianjing Li |
| author_facet | Ian J. Saldanha Christopher H. Schmid Joseph Lau Kay Dickersin Jesse A. Berlin Jens Jap Bryant T. Smith Simona Carini Wiley Chan Berry De Bruijn Byron C. Wallace Susan M. Hutfless Ida Sim M. Hassan Murad Sandra A. Walsh Elizabeth J. Whamond Tianjing Li |
| author_sort | Ian J. Saldanha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T09:03:42Z |
| format | Article |
| id | doaj.art-3b73b29a64e8480db248cf2deb722103 |
| institution | Directory Open Access Journal |
| issn | 2046-4053 |
| language | English |
| last_indexed | 2024-04-12T09:03:42Z |
| publishDate | 2016-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Systematic Reviews |
| spelling | doaj.art-3b73b29a64e8480db248cf2deb7221032022-12-22T03:39:09ZengBMCSystematic Reviews2046-40532016-11-015111110.1186/s13643-016-0373-7Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trialIan J. Saldanha0Christopher H. Schmid1Joseph Lau2Kay Dickersin3Jesse A. Berlin4Jens Jap5Bryant T. Smith6Simona Carini7Wiley Chan8Berry De Bruijn9Byron C. Wallace10Susan M. Hutfless11Ida Sim12M. Hassan Murad13Sandra A. Walsh14Elizabeth J. Whamond15Tianjing Li16Department of Epidemiology, Johns Hopkins Bloomberg School of Public HealthDepartment of Biostatistics, and Center for Evidence-based Medicine, Brown University School of Public HealthDepartment of Health Services, Policy and Practice, and Center for Evidence-based Medicine, Brown University School of Public HealthDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public HealthEpidemiology, Johnson & JohnsonCenter for Evidence-based Medicine, Brown University School of Public HealthCenter for Evidence-based Medicine, Brown University School of Public HealthDepartment of Medicine, University of California San Francisco School of MedicineInternal Medicine, Kaiser Permanente NorthwestNational Research Council Information and Communications Technologies Portfolio (NRC-ICT)Northeastern University College of Computer and Information ScienceDepartment of Medicine, Johns Hopkins School of MedicineDepartment of Medicine, University of California San Francisco School of MedicineCollege of Medicine, and Evidence-based Practice Center, Mayo ClinicCalifornia Breast Cancer OrganizationsCochrane Consumer NetworkDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public HealthAbstract 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.http://link.springer.com/article/10.1186/s13643-016-0373-7Data abstractionSystematic reviewsRandomized controlled trial |
| spellingShingle | Ian J. Saldanha Christopher H. Schmid Joseph Lau Kay Dickersin Jesse A. Berlin Jens Jap Bryant T. Smith Simona Carini Wiley Chan Berry De Bruijn Byron C. Wallace Susan M. Hutfless Ida Sim M. Hassan Murad Sandra A. Walsh Elizabeth J. Whamond Tianjing Li Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial Systematic Reviews Data abstraction Systematic reviews Randomized controlled trial |
| title | Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial |
| title_full | Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial |
| title_fullStr | Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial |
| title_full_unstemmed | Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial |
| title_short | Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial |
| title_sort | evaluating data abstraction assistant a novel software application for data abstraction during systematic reviews protocol for a randomized controlled trial |
| topic | Data abstraction Systematic reviews Randomized controlled trial |
| url | http://link.springer.com/article/10.1186/s13643-016-0373-7 |
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