Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication
| Autor: | Nick Cheney, Joseph Wills, Ali Javed, Cailin J. Gramling, David Gramling, Robert Gramling, Francesca Arnoldy, Laurence A. Clarfeld, Brigitte N. Durieux, Ann Wong, Margaret J. Eppstein, Donna M. Rizzo, Jeremy E. Matt, Jack Straton, Tess Braddish |
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| Rok vydání: | 2021 |
| Předmět: |
Self-organizing map
Computer science business.industry Communication media_common.quotation_subject Uncertainty Citizen journalism General Medicine Data science Cohort Studies Expression (architecture) Health care Humans Unsupervised learning Conversation business Delivery of Health Care Storytelling media_common Diversity (politics) |
| Zdroj: | Patient Education and Counseling. 104:2616-2621 |
| ISSN: | 0738-3991 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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