A general model of conversational dynamics and an example application in serious illness communication

Abstract

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 use CODYMs to identify normative patterns of information flow in serious illness conversations, show how these normative patterns change over the course of the conversations, and show how they differ in conversations where the patient does or doesn’t audibly express anger or fear. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across languages, cultures, and contexts with the prospect of identifying universal similarities and unique “fingerprints” of information flow.

The use of natural language processing in palliative care research: A scoping review

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

“…We identified a trend in recent years where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings, particularly by the same research group (Gramling et al). 6,28,32,38,39,65 These in-depth analyses included concepts like conversational stories, conversational pauses, shapes of stories, connectional silence, analysis of prosody, turn length, turn-taking, and analyses of the lexicon of uncertainty.…”

“…Brizzi et al, 25 Kern et al, 49 Lau et al, 53 Lee et al, 54,55 Lindvall et al, 58 Poort et al, 71 Udelsman et al 84 Conversation analysis/conversational dynamics during serious illness conversations 7 (8.5) van den Broek-Altenburg et al, 6 Clarfeld et al, 28 Durieux et al, 32 Gramling et al, 38,39 Manukyan et al, 65 Ross et al 72 Symptom identification 6 (7.3) DiMartino et al, 30 Forsyth et al, 35 Heintzelman et al, 43 López-Torrecilla et al, 60 Taggart et al, 83 Yang et al 91 Decision-making 4 (4.8) Barrett et al, 24 Ouchi et al, 70 Saeed et al, 73 Almasalha et al 85 Identification of patients likely to benefit from palliative care or palliative needs or palliative status 4 (4.8) Murphree et al, 68 Sandham et al, 75 Song et al, 79 Zhang et al 97 Clinical phenotypes identification 3 (3.6) Ernecoff et al, 34 Jay et al, 45 natural language processing software to complete data analysis. This suggests that each software has its own strengths, and that weaknesses may be overcome by the combined use of more than one software solution to achieve the desired goal of the analyses.…”

Somtimes: self organizing maps for time series clustering and its application to serious illness conversations

“… 2022 ; Clarfeld et al. 2021 ). Unfortunately, historical approaches to conversation analyses are too cumbersome, costly, and time-intensive to achieve this scalability, thus limiting our existing empirical knowledge about serious illness communication (Tulsky et al.…”

Toward a basic science of communication in serious illness

“…Using network analysis methods, they examined how these communication behaviors of patients and clinicians mutually influenced one another. New artificial intelligence-assisted approaches to analyzing conversation recordings has enabled study of communication delivery features such as use of silence [39], turn-taking [40], and vocal tone [41,42].…”