Clinician-Spoken Plain Language in Health Care Encounters: A Qualitative Analysis to Assess Measurable Elements

Abstract

Purpose Good communication and use of plain language in health care encounters improve outcomes, including emotional health, symptom resolution, and functional status. Yet there is limited research on how to measure and report spoken plain language, which is the use of familiar, clear language. The authors aimed to describe key, measurable elements of spoken plain language that can be assessed and reported back to clinicians for self-reflection. Method The authors conducted secondary analysis of transcripts from recorded encounters between breast cancer surgeons and patients with early-stage breast cancer. Two coders used a hybrid qualitative analysis with a framework based on U.S. Federal Plain Language Guidelines. To develop major themes, they examined (1) alignment with the Guidelines and (2) code frequencies within and across transcripts. They also noted minor themes. Results From 74 transcripts featuring 13 surgeons, the authors identified 2 major themes representing measurable elements of spoken plain language: (1) clinicians had a propensity to use both explained and unexplained medical terms, and (2) clinicians delivered information using either short turns (one unit of someone speaking) with 1 topic or long turns with multiple topics. There were 3 minor themes that were not indicative of whether or not clinicians used spoken plain language. First, clinicians regularly used absolute risk communication techniques. Second, question-asking techniques varied and included open-ended, close-ended, and comprehension checks. Third, some clinicians used imagery to describe complex topics. Conclusions Clinicians’ propensity to use medical terms with and without explanation and parse encounters into shorter or longer turns are measurable elements of spoken plain language. These findings will support further research on the development of a tool that can be used in medical education and other settings. This tool could provide direct and specific feedback to improve the plain language practices of clinicians in training and beyond.