Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication.

Cohort Studies Communication Delivery of Health Care Humans Uncertainty

Journal

Patient education and counseling
ISSN: 1873-5134
Titre abrégé: Patient Educ Couns
Pays: Ireland
ID NLM: 8406280

Informations de publication

Date de publication:
11 2021
Historique:
received: 24 07 2021
accepted: 27 07 2021
pubmed: 7 8 2021
medline: 8 1 2022
entrez: 6 8 2021
Statut: ppublish

Résumé

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. 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. Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.

Sections du résumé

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.

Identifiants

DOI: 10.1016/j.pec.2021.07.043 PMID: 34353689
pubmed: 34353689
pii: S0738-3991(21)00503-6
doi: 10.1016/j.pec.2021.07.043
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Pagination

2616-2621

Informations de copyright

Copyright © 2021. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interest No authors have competing interests to declare.

Auteurs

Robert Gramling (R)

University of Vermont, Department of Family Medicine, Burlington, VT, USA. Electronic address: robert.gramling@uvm.edu.

Ali Javed (A)

University of Vermont, Department of Computer Science, Burlington, VT, USA.

Brigitte N Durieux (BN)

University of Vermont, Burlington, VT, USA.

Laurence A Clarfeld (LA)

University of Vermont, Department of Computer Science, Burlington, VT, USA.

Jeremy E Matt (JE)

University of Vermont, Complex Systems & Data Science, USA.

Donna M Rizzo (DM)

University of Vermont, Department of Civil & Environmental Engineering, Burlington, VT, USA.

Ann Wong (A)

University of Vermont, Burlington, VT, USA.

Tess Braddish (T)

University of Vermont, Department of Family Medicine, Burlington, VT, USA.

Cailin J Gramling (CJ)

University of Vermont, Burlington, VT, USA.

Joseph Wills (J)

University of Vermont, Burlington, VT, USA.

Francesca Arnoldy (F)

University of Vermont, Continuing and Distance Education, Burlington, VT, USA.

Jack Straton (J)

University of Vermont, Burlington, VT, USA.

Nicholas Cheney (N)

University of Vermont, Department of Computer Science, Burlington, VT, USA.

Margaret J Eppstein (MJ)

University of Vermont, Department of Computer Science, Burlington, VT, USA.

David Gramling (D)

University of British Columbia, Department of Central, Eastern and Northern European Studies, Vancouver, BC, Canada.

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Classifications MeSH

questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Conversational stories & self organizing maps:...
questionsmedicales.fr › Sciences de l'information › Communication › Conversational stories & self organizing maps:...
questionsmedicales.fr › Qualité, accès, évaluation des soins de santé › Prestations des soins de santé › Conversational stories & self organizing maps:...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Conversational stories & self organizing maps:...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Incertitude › Conversational stories & self organizing maps:...