Abstract
BackgroundThe increasingly rapid rate of evidence publication has made it difficult for evidence synthesis—systematic reviews and health guidelines—to be continually kept up to date. One proposed solution for this is the use of automation in health evidence synthesis. Guideline developers are key gatekeepers in the acceptance and use of evidence, and therefore, their opinions on the potential use of automation are crucial.MethodsThe objective of this study was to analyze the attitudes of guideline developers towards the use of automation in health evidence synthesis. The Diffusion of Innovations framework was chosen as an initial analytical framework because it encapsulates some of the core issues which are thought to affect the adoption of new innovations in practice. This well-established theory posits five dimensions which affect the adoption of novel technologies: Relative Advantage, Compatibility, Complexity, Trialability, and Observability. Eighteen interviews were conducted with individuals who were currently working, or had previously worked, in guideline development. After transcription, a multiphase mixed deductive and grounded approach was used to analyze the data. First, transcripts were coded with a deductive approach using Rogers’ Diffusion of Innovation as the top-level themes. Second, sub-themes within the framework were identified using a grounded approach.ResultsParticipants were consistently most concerned with the extent to which an innovation is in line with current values and practices (i.e., Compatibility in the Diffusion of Innovations framework). Participants were also concerned with Relative Advantage and Observability, which were discussed in approximately equal amounts. For the latter, participants expressed a desire for transparency in the methodology of automation software. Participants were noticeably less interested in Complexity and Trialability, which were discussed infrequently. These results were reasonably consistent across all participants.ConclusionsIf machine learning and other automation technologies are to be used more widely and to their full potential in systematic reviews and guideline development, it is crucial to ensure new technologies are in line with current values and practice. It will also be important to maximize the transparency of the methods of these technologies to address the concerns of guideline developers.
Highlights
The increasingly rapid rate of evidence publication has made it difficult for evidence synthesis— systematic reviews and health guidelines—to be continually kept up to date
Half of the participants had between 5 and 10 years of experience in evidence synthesis; five participants had between 10 and 20 years of experience; two participants had more than 20 years of experience, and two had less than 5 years of experience
Analyzed via the lens of the Diffusion of Innovations framework, the results of this study strongly demonstrate that Compatibility with professional cultural values is the most significant consideration for guideline developers in the potential adoption of automation
Summary
The increasingly rapid rate of evidence publication has made it difficult for evidence synthesis— systematic reviews and health guidelines—to be continually kept up to date. One proposed solution for this is the use of automation in health evidence synthesis. Guideline developers are key gatekeepers in the acceptance and use of evidence, and their opinions on the potential use of automation are crucial. Evidence-based guidelines are overwhelmed by the rate of research publication As guidelines increasingly incorporate an evidencebased medicine approach, the systematic reviews which are a crucial component of this evidence have become overwhelmed by the rate of publication of new evidence [1]. Research is at risk of being wasted, leading to out-of-date healthcare and guidelines and impacting on population health outcomes. Limited literature addresses adoption of automation Given this, there is increasing interest in the use of automation in the completion of systematic reviews [3,4,5,6]. Automation technologies include machine learning (ML), natural language processing (NLP), and text mining, among other technologies
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