The Role of Language in Remote Healthcare Triage: A Meta-Aggregative Review

Abstract

AIM(S): To provide an overview of evidence on the role of language in remote healthcare services prioritisation, from now on termed triage. This study synthesises literature, to better understand how language affects triage interactions, aiming to improve these processes. DESIGN: We conducted a meta-aggregative review. METHODS: A systematic search in Scopus from inception to September 2023 identified 437 studies on language in remote healthcare triage, of which 23 were included. Information was selected using both inductive and deductive coding, focusing on six linguistic features of interaction that have been described in the literature on studies using conversation analysis: turn-taking organisation, overall structure, sequence organisation, turn design, lexical choice, and epistemological and other forms of asymmetry. The process followed the RAMESES Publication Standards for Meta-narrative Reviews. RESULTS: Two main findings emerged. First, all six linguistic features are present in triage conversations, indicating that language involves more than just what is said. It also matters, for example, how and when a question is asked. Second, computerised decision support systems (CDSS) significantly affect conversation flow and dynamics. CONCLUSION: Language in triage involves more than literal speech and is heavily influenced by CDSS. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Our study suggests that quality assessments of triage conversations should consider not only what is said but cover all relevant aspects of language. The influence of computerised decision support systems (CDSS) on linguistic features highlights the need for systems to be adaptable, to improve conversation quality and better addressing caller needs rather than focusing on one-size-fits-all questions. IMPACT: This review highlights the complex role of language in triage conversations and its impact on interaction. It calls for a broader view of language in quality assessments, recognising that both call-takers and callers contribute to call quality. Insights from this review can help developers enhance question types, sequence, and delivery methods of computerised decision support systems. Finally, education for call-takers in healthcare sectors may be improved based on our findings. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.