Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication. Issue 11 (November 2021) - Gramling, Robert; Javed, Ali; Durieux, Brigitte N.; Clarfeld, Laurence A.; Matt, Jeremy E.; Rizzo, Donna M.; Wong, Ann; Braddish, Tess; Gramling, Cailin J.; Wills, Joseph; Arnoldy, Francesca; Straton, Jack; et al.
Highlights: Conversational storytelling is a scalable conceptual framework for uncertainty measurement in serious illness conversations. Natural language processing of uncertainty lexicon can identify "uncertainty story arcs" in serious illness conversations. A Self-Organizing Map provides an intuitive machine learning interface for qualitative exploration of uncertainty story types. Abstract: Background: Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations. Discussion: Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts. Using actual conversations from a large direct-observation cohort study, we demonstrate how natural language processing and unsupervised machine learning methods can reveal underlying types of uncertainty stories in serious illness conversations. Conclusions: Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.