The views of health guideline developers on the use of automation in health evidence synthesis

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. 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 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. Results: Participants 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. 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.

Automated summary

The study analyzed the attitudes of guideline developers towards the use of automation in health evidence synthesis and found that compatibility with current values and practices was the primary concern, followed by relative advantage and observability. Participants expressed a desire for transparency in automation software methodology. Complexity and trialability were of less interest. The conclusions emphasized the importance of ensuring new technologies align with current values and maximizing transparency to address the concerns of guideline developers for wider use of machine learning and automation technologies in systematic reviews and guideline development.