A meta-narrative review of coding tools for healthcare interactions and their applicability to written communication

Background: Although healthcare professionals (HCP) undergo communicative skills training, these are sometimes unsatisfactory for patients (empathy, discussion managing). Existing coding tools overlook the interaction and patients' responses. Meanwhile, remote consultations are redefining communication channels. While some researchers adapt those tools to telehealth, few investigate written interactions. Objective: To identify and evaluate coding tools for healthcare interactions and examine their suitability for written interactions. Methods: We conducted a meta-narrative review in PubMed, PsycINFO, Embase, Web of Science, CINAHL, and Scopus databases up to December 2022 with Communicati* AND Human* AND Linguistic* AND Professional-Patient Relation* as search terms. We extracted data regarding methodology, unit of analysis (UoA), coding categories, reliability, strengths, weaknesses, and inter-rater reliability (IRR). Results: We identified 11 mixed-methods tools. Qualitatively, coding dimension was focused (n = 6) or comprehensive (n = 5). Main quantitative methods were descriptive statistics (n = 4) and cross-tabulations (n = 4). Main UoA was utterance (n = 7). Relevant categories were processes (n = 4), content (n = 3), emotional expressions and responses (n = 3), and grammatical format (n = 2). IRR ranged from 0.68 to 0.85 for coding categories. Conclusion: Despite similarities, category terminologies were inconsistent, one-sided, and mostly covered conversation topics and behaviours. A tool with emotional and grammar categories could bridge the gap between a speaker's intended meaning and the receiver's interpretation to enhance patient-HCP communication. Furthermore, we need empirical research to determine whether these tools are suitable for written interactions. Innovation: This review presents a comprehensive and state-of-the-art overview of healthcare interactions' coding tools and identifies their barriers. Our findings will support communication researchers in selecting appropriate coding tools for evaluating health interactions and enhancing HCP training