Modifications to the Roter Interaction Analysis System (RIAS) coding scheme: A scoping review

OBJECTIVE: The Roter Interaction Analysis System (RIAS) is a widely used coding scheme for analyzing communication in clinical encounters. Researchers often modify RIAS to fit the specific aims of their studies, but the process and rationale for modification are unclear. Without a standardized approach to modifications, it is challenging to interpret RIAS findings and benchmark results across studies. This scoping review aimed to identify and describe published RIAS modifications. METHOD: We searched PubMed, CINAHL, Scopus, and Web of Science for empirical studies reporting adaptations of RIAS. A narrative synthesis summarized modifications, including additions, reductions, or combinations of codes. RESULTS: Of 551 articles identified, 27 met the inclusion criteria. While based on the original 40 mutually exclusive RIAS codes, most studies reported modifications to the coding framework. In total, 159 new codes across modified codebooks. Most newly added codes were task-focused (71%), particularly question-related. Additionally, five studies reduced and combined codes, five combined codes only, and one reduced codes. The main reasons for modification were: scope expansion, study setting, and practical considerations. Most added codes were introduced to adapt the RIAS to specific study settings. In contrast, reduced codes were often implemented for practical considerations. The majority of papers did not document their approach to modification or assessment of the modified tool. CONCLUSIONS: Shortening RIAS was a common modification, suggesting a need to revisit and streamline the coding system to enhance usability. Inconsistent documentation of modifications challenges the reproducibility and interpretation of results. PRACTICE IMPLICATIONS: Following established research methods and conducting psychometric testing on modified tools is critical to ensure reliability and validity. These practices improve research quality, comparability, and transparency, making findings more robust and applicable across studies.