Effectiveness of conversational agents (virtual assistants) in healthcare: protocol for a systematic review (Preprint)
de Cock C., Milne-Ives M., van Velthoven MH., Alturkistani A., Lam C., Meinert E.
<sec> <title>BACKGROUND</title> <p>Conversational agents have evolved in recent decades to become multimodal, multifunctional platforms that have the potential to automate a diverse range of health-related activities, supporting the general public, patients and physicians. Multiple studies have reported the development of these agents and recent systematic reviews have described the scope of use of conversational agents in healthcare. However, there is little focus on the effectiveness of these systems, thus the viability and applicability of these systems is unclear.</p> </sec> <sec> <title>OBJECTIVE</title> <p>The objective of this systematic review is to assess the effectiveness of conversational agents in healthcare and to identify limitations, adverse events and areas for future investigation of these agents.</p> </sec> <sec> <title>METHODS</title> <p>The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols will be used to structure this protocol. The focus of the systematic review is guided by a population, intervention, comparator, and outcome framework . A systematic search of PubMed (Medline), EMBASE, CINAHL, and Web of Science will be conducted. Two authors will independently screen the titles and abstracts of identified references and select studies according to the eligibility criteria. Any discrepancies will then be discussed and resolved. Two reviewers will extract and validate data, respectively, from included studies into a standardised form and conduct quality appraisal.</p> </sec> <sec> <title>RESULTS</title> <p>At the time of writing, we have begun a preliminary literature search and piloting of the study selection process.</p> </sec> <sec> <title>CONCLUSIONS</title> <p>This systematic review aims to clarify the effectiveness, limitations and future applications of conversational agents in healthcare. Our findings may be used to inform future development of conversational agents and further the personalisation of care.</p> </sec>