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.
Highlights
Conversation is a fundamental form of human communication
Are there normative information flow patterns in serious illness conversations? If so, how do these differ between patients and clinicians, and differ from what would be expected if there were no sequential dependencies in turn lengths? In what ways do those patterns change during the course of a conversation? How does the expression of distressing emotions such as anger or fear impact patterns of information flow? We show that COnversational DYnamics Model (CODYM) analysis provides a quantitative approach, with an intuitive interpretation, that helps to answer these questions
We have presented a novel approach to quantify and visualize overall patterns in the dynamics of information flow in conversations with CODYMs (COnversational DYnamics Models)
Summary
Conversational dynamics modelWe use the length of a speaker turn as a simple proxy for the capacity of information that the turn can convey. We define a COnversational DYnamics Model (CODYM) to be a Markov Model (MM) where each event is a speaker turn of a given discretized length and states consist of some predefined number (defined by the order of the model) of the immediately preceding events. Using the median turn length as the maximum length of short turns (a) minimized the disparity between the number of short vs long turns, and (b) maximized the Shannon entropy (a measure of information content) for the distribution of states in a 3rd-order CODYM (S2 Fig). The most appropriate discretization of turn lengths depends on the nature of the data being analyzed and the questions being asked
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