Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations

Ross, Lindsay M.; Danforth, Christopher M.; Eppstein, Margaret J.; Clarfeld, Laurence A.; Durieux, Brigitte N.; Gramling, Cailin J.; Hirsch, Laura; Rizzo, Donna M.; Gramling, Robert

doi:10.1016/j.pec.2019.11.021

Cited by 20 publications

(25 citation statements)

References 20 publications

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“…We analysed the emotional sentiment of words people chose to describe feelings about death, for themselves, for perceptions of others, and for changes over the time-period during exposure to a course about death. The results demonstrated that text-sentiment analyses can provide a meaningful approach to death attitude research, consistent with previous linguistic investigations [20][21][22]29]. 'Sad' was a word that was prevalent throughout, regardless of whether referring to feelings for oneself or for others, or feelings captured at the beginning or ending of a course about death and dying.…”

Section: Plos Onesupporting

confidence: 87%

“…Until recently, word sentiment has been neglected in the context of death and dying, even though there have been calls for the use of alternative methods to investigate attitudes towards dying [7]. The recent linguistic works of Gramling and Gramling [20] on clinical cancer palliative care conversations [21], and Semino and colleagues [22] on metaphors used in cancer and end-of-life discussions are notable exceptions. For example, health professionals' metaphors for 'good' and 'bad' deaths were examined, and detected contrasting metaphors of 'peace', 'freedom', 'openness' and 'acceptance', compared to 'struggle' and 'pushing away' [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…For example, health professionals' metaphors for 'good' and 'bad' deaths were examined, and detected contrasting metaphors of 'peace', 'freedom', 'openness' and 'acceptance', compared to 'struggle' and 'pushing away' [22,23]. Also, the sentiment of words in palliative care consultations moved from sadder to happier valences through the course of the conversation arcs between dyads of cancer patients and clinicians [21].…”

Section: Introductionmentioning

confidence: 99%

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Words describing feelings about death: A comparison of sentiment for self and others and changes over time

et al. 2021

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Understanding public attitudes towards death is needed to inform health policies to foster community death awareness and preparedness. Linguistic sentiment analysis of how people describe their feelings about death can add to knowledge gained from traditional self-reports. This study provided the first description of emotive attitudes expressed towards death utilising textual sentiment analysis for the dimensions of valence, arousal and dominance. A linguistic lexicon of sentiment norms was applied to activities conducted in an online course for the general-public designed to generate discussion about death. We analysed the sentiment of words people chose to describe feelings about death, for themselves, for perceptions of the feelings of ‘others’, and for longitudinal changes over the time-period of exposure to a course about death (n = 1491). The results demonstrated that sadness pervades affective responses to death, and that inevitability, peace, and fear were also frequent reactions. However, words chosen to represent perceptions of others’ feelings towards death suggested that participants perceived others as feeling more negative about death than they do themselves. Analysis of valence, arousal and dominance dimensions of sentiment pre-to-post course participation demonstrated that participants chose significantly happier (more positive) valence words, less arousing (calmer) words, and more dominant (in-control) words to express their feelings about death by the course end. This suggests that the course may have been helpful in participants becoming more emotionally accepting in their feelings and attitude towards death. Furthermore, the change over time appeared greater for younger participants, who showed more increase in the dominance (power/control) and pleasantness (valence) in words chosen at course completion. Sentiment analysis of words to describe death usefully extended our understanding of community death attitudes and emotions. Future application of sentiment analysis to other related areas of health policy interest such as attitudes towards Advance Care Planning and palliative care may prove fruitful.

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Section: Plos Onesupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Words describing feelings about death: A comparison of sentiment for self and others and changes over time

et al. 2021

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“…7,33 Reliably tracking patient goals would provide useful context for assessing appropriateness of healthcare utilization, and characterizing narrative arcs in the disease trajectory could help frame quality improvement initiatives and psychosocial interventions during serious illness. 34 In healthcare operations, explainable AI for logistic regression or XGBoost could even be used to inform clinician-facing EHR tools at the point of care, perhaps by visualizing positive coefficients or SHAP values across terms and Empath categories.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

Davoudi

Tissot

Doucette

et al. 2021

Preprint

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One core measure of healthcare quality set forth by the Institute of Medicine is whether care decisions match patient goals. High-quality “serious illness communication” about patient goals and prognosis is required to support patient-centered decision-making, however current methods are not sensitive enough to measure the quality of this communication or determine whether care delivered matches patient priorities. Natural language processing (NLP) offers an efficient method for identification and evaluation of documented serious illness communication, which could serve as the basis for future quality metrics in oncology and other forms of serious illness. In this study, we trained NLP algorithms to identify and characterize serious illness communication with oncology patients.

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“…To assess how normative patterns in information flow may change over the course of a conversation, we divide the turns of each conversation in the corpus into sequential deciles of words (ten bins of narrative time, as in [55]), stratified by patient and clinician turns. Note that different conversations have different numbers of turns, so the number of turns per bin varies by conversation.…”

Section: Temporal Changes In Normative Patternsmentioning

confidence: 99%

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

et al. 2021

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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 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.

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Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations

Cited by 20 publications

References 20 publications

Words describing feelings about death: A comparison of sentiment for self and others and changes over time

Words describing feelings about death: A comparison of sentiment for self and others and changes over time

Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

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

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