Background 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 maintain the most up-to-date data. 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.
Methods The objective of this study was to analyse the attitudes of guideline developers towards the use of machine learning and crowd-sourcing in evidence. 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 analyse 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.
Results Participants were consistently most concerned with the extent to which an innovation is in line with current values and practices (ie. 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 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.
Conclusions If 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.