Tools to support the automation of systematic reviews
Creators: name: Khalil, Hanan
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Medicine and Health Sciences

description: Given the rapid development of automation tools for systematic reviews and the urgent need to undertake reviews in a short space of time that was uncovered by the pandemic, we sought to systematically map the literature regarding the availability of automation tools, and which steps they are supporting in the systematic review. The following reviews questions will also be explored using a scoping review methodology 1. The reliability and validity of the available tools. 2. The limitations of using these tools. 3. Any recommendations to further improve the use of these tools. Methods This review will follow a scoping review methodology published by Khalil, et al., 2017 and Peters et al., 2020. Inclusion criteria Participants This review will consider any automation tools that are used in the process of automation of systematic reviews. Tools will be included if a software program that has a user interface and a file system or a database was used for automation as opposed to using an algorithm that required user knowledge of computer science and codes. Concept The concept of interest for the scoping review is the automation of systematic reviews to enable faster completion of reviews or completion of any steps of the reviews undertaken by a machine learning process rather than a human researcher. Context The context of this review is systematic reviews. Any review that was used to summarise the available literature on a particular topic. Types of studies This scoping review will consider any quantitative, qualitative studies including experimental, descriptive, evaluation studies, observational studies and any discussion papers that reported on automation of systematic reviews. Due to time constraints, only data published in English will be considered for the review. No gray literature will be searched as we are interested in studies that are published in peer reviewed journals based on scientific methods that use evidence to develop conclusions. Search strategy The search strategy will aim aim to identify studies published from the inception of the included databases until June 2021. A three-step search strategy was utilized in this review. An initial limited search of Ovid MEDLINE will be undertaken followed by analysis of the text words contained in the title and abstract and of the index terms used to describe the article. A second search using all identified keywords and index terms will be undertaken across all included databases. The following databases will be searched: Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials, JBI EBP database, and Global Health. The search strategy of all the databases followed the same strategy shown in Appendix I. The reference list of all identified reports and articles will be searched for additional studies. The following keywords will used automation, methods improvement, machine learning, natural language processing, methods evaluation, technology assessment, systematic review accelerator, facilitator, artificial intelligence, Systematic review, evidence synthesis, knowledge synthesis. Data extraction Relevant data will be extracted from the included studies to address the review question using the methodology outlined by Peters et al. The data extracted included the following: Author (s), year of publication, origin/country (where the study was published or conducted), study type, tools used, any reporting of reliability, validity, user feedback and any limitations stated about the tool.