Language of Uncertainty: the Expression of Decisional Conflict Related to Skin Cancer Prevention Recommendations

Strekalova, Yulia A.; James, Vaughan

doi:10.1007/s13187-016-0985-6

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In this work, we focus on two such lists of interest in serious illness conversations: hedging terms and treatment terms. Frequency (%) There are many definitions of uncertainty terms and subtypes used in clinical conversations (e.g., [47,48,49,50]).…”

Section: Contextualization Of Codymsmentioning

confidence: 99%

A General Model of Conversational Dynamics and an Example Application in Serious Illness Communication

Clarfeld,

Gramling,

Rizzo

et al. 2020

Preprint

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Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations. CODYMs are Markov Models that capture sequential dependencies in the lengths of speaker turns. The proposed method is automated and scalable, and preserves the privacy of the conversational participants. The primary function of CODYM analysis is to quantify and visualize patterns of information flow, concisely summarized over sequential turns from one or more conversations. Our approach is general and complements existing methods, providing a new tool for use in the analysis of any type of conversation. As an important first application, we demonstrate the model on transcribed conversations between palliative care clinicians and seriously ill patients. These conversations are dynamic and complex, taking place amidst heavy emotions, and include difficult topics such as end-of-life preferences and patient values. We perform a versatile set of CODYM analyses that (a) establish the validity of the model by confirming known patterns of conversational turn-taking and word usage, (b) identify normative patterns of information flow in serious illness conversations, and (c) show how these patterns vary across narrative time and differ under expressions of anger, fear and sadness. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across language and culture, with the prospect of identifying universal similarities and unique "fingerprints" of information flow.

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Section: Contextualization Of Codymsmentioning

confidence: 99%

A General Model of Conversational Dynamics and an Example Application in Serious Illness Communication

Clarfeld,

Gramling,

Rizzo

et al. 2020

Preprint

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show abstract

“…Conversely, some efforts seek insight from patients' experiences to improve the delivery of healthcare. For instance, Hawkins et al, and Ranard et al, automatically extracted patient-perceived quality of care in hospitals [22][23], while Bahk et al, [24] and Strekalova et al, [25], respectively, analyzed patient sentiment on vaccination and cancer care options. Assessing the quality of EHR content is another topic where NLP plays a role.…”

Section: Applications Of Clinical Nlp Are Increasing In Number and DImentioning

confidence: 99%

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Névéol¹,

Zweigenbaum²

2017

Yearb Med Inform

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SummaryObjectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

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“…Common non-numeric representations include probability terms (e.g., likely) as well as words such as slight hope [2]. These natural language expressions may be used with modifiers (e.g., very) and hedges (e.g., almost) [3,4].…”

mentioning

confidence: 99%

Communicating uncertainty using words and numbers

Dhami

Mandel

2022

Trends in Cognitive Sciences

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Language of Uncertainty: the Expression of Decisional Conflict Related to Skin Cancer Prevention Recommendations

Cited by 5 publications

References 15 publications

A General Model of Conversational Dynamics and an Example Application in Serious Illness Communication

A General Model of Conversational Dynamics and an Example Application in Serious Illness Communication

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Communicating uncertainty using words and numbers

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