Eloquent silences: A musical and lexical analysis of conversation between oncologists and their patients

An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences

Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.

Listening fast and slow

The pattern of response times in conversation can reveal a lot about how people listen to each other. Fast response times not only telegraph eagerness but provide evidence of attending in such a way as to almost finish the other's sentences. In other situations, slow response times are more appropriate, such as when listening prompts deeper reflection, or to leave space for the enjoyment of an inside joke. Here we argue that close relationships are not marked exclusively by one or the other pattern, but by the ability to toggle effortlessly between the two as the conversation demands.

"I don't need your pills, I need your attention:" Steps toward deep listening in medical encounters

Patients highly value being listened to, taken seriously, heard, and understood; indeed, listening to patients is essential to alleviate suffering. Yet listening as a clinical skill has been virtually ignored in the training of physicians. In this paper, we synthesize literature related to listening in medicine and explore the internal and external challenges and complexity of listening - including the need to listen with a diagnostic as well as a relational ear to take in physical symptoms, emotions, and contexts - often in chaotic and time-pressured environments. We suggest physicians focus on the development of "deep listening" skills, involving cultivating curiosity, openness, reflective self-questioning, and epistemic reciprocity; we also suggest how to ensure patients know they are being listened to.

Methodological innovations to strengthen evidence-based serious illness communication

BACKGROUND/OBJECTIVE

A growing population of those affected by serious illness, prognostic uncertainty, patient diversity, and healthcare digitalization pose challenges for the future of serious illness communication. Yet, there is paucity of evidence to support serious illness communication behaviors among clinicians. Herein, we propose three methodological innovations to advance the basic science of serious illness communication.

RESULTS

First, advanced computation techniques - e.g. machine-learning techniques and natural language processing - offer the possibility to measure the characteristics and complex patterns of audible serious illness communication in large datasets. Second, immersive technologies - e.g., virtual- and augmented reality - allow for experimentally manipulating and testing the effects of specific communication strategies, and interactional and environmental aspects of serious illness communication. Third, digital-health technologies - e.g., shared notes and videoconferences - can be used to unobtrusively observe and manipulate communication, and compare in-person to digitally-mediated communication elements and effects. Immersive and digital health technologies allow integration of physiological measurement (e.g. synchrony or gaze) that may advance our understanding of patient experience.

CONCLUSION/PRACTICE IMPLICATIONS

New technologies and measurement approaches, while imperfect, will help advance our understanding of the epidemiology and quality of serious illness communication in an evolving healthcare environment.

Long gaps between turns are awkward for strangers but not for friends

When people feel connected they tend to respond quickly in conversation, creating short gaps between turns. But are long gaps always a sign that things have gone awry? We analysed the frequency and impact of long gaps (greater than 2 s) in conversations between strangers and between friends. As predicted, long gaps signalled disconnection between strangers. However, long gaps between friends marked moments of increased connection and friends tended to have more of them. These differences in connection were also perceived by independent raters: only the long gaps between strangers were rated as awkward, and increasingly so the longer they lasted. Finally, we show that, compared to strangers, long gaps between friends include more genuine laughter and are less likely to precede a topic change. This suggests that the gaps of friends may not function as 'gaps' at all, but instead allow space for enjoyment and mutual reflection. Together, these findings suggest that the turn-taking dynamics of friends are meaningfully different from those of strangers and may be less bound by social conventions. More broadly, this work illustrates that samples of convenience-pairs of strangers being the modal paradigm for interaction research-may not capture the social dynamics of more familiar relationships. This article is part of a discussion meeting issue 'Face2face: advancing the science of social interaction'.

Silence in Conversations About Advancing Pediatric Cancer

Background and Objectives

Skillful use of silence by clinicians can support patient-centered communication. However, what makes a period of silence feel meaningful is not well understood. This study aimed to characterize profound, skillful silences during difficult conversations between pediatric oncologists, children with advancing cancer, and their families.

Methods

We audio-recorded serial disease reevaluation discussions between pediatric oncologists, patients with high-risk cancer, and their families across 24 months or until death, whichever occurred first. Using an inductive process, we performed content analysis across all dialogue recorded at timepoints of disease progression to examine types of silence.

Results

17 patient-parent dyads with disease progression yielded 141 recorded conversations. Inductive coding yielded a layered typology of silence, including “intentional silence” (≥5 seconds), “profound silence” (≥5 seconds following receipt of difficult information, juxtaposed with statements of shared understanding, emotion, or enlightenment), and “stacked silence” (series of silences juxtaposed within dialogue). Intentional silence lasting ≥5 seconds occurred 238 times in 35/49 “bad news” recordings; nearly half (103/238) of these silences were identified as profound silence, in which silences appeared to create space for processing, allowed for questions to emerge, and synergized with empathic and affirmational statements. In most cases, profound silences involved the juxtaposition, or stacking, of multiple silences close together.

Conclusions

Profound silences occur often during conversations about advancing pediatric cancer and share distinct characteristics. Opportunities exist to teach clinicians to use profound and stacked silences with intention during difficult conversations as a fundamental aspect of communication.

Epidemiology of Connectional Silence in specialist serious illness conversations

CONTEXT

Human connection can reduce suffering and facilitate meaningful decision-making amid the often terrifying experience of hospitalization for advanced cancer. Some conversational pauses indicate human connection, but we know little about their prevalence, distribution or association with outcomes.

PURPOSE

To describe the epidemiology of Connectional Silence during serious illness conversations in advanced cancer.

METHODS

We audio-recorded 226 inpatient palliative care consultations at two academic centers. We identified pauses lasting 2+ seconds and distinguished Connectional Silences from other pauses, sub-categorized as either Invitational (ICS) or Emotional (ECS). We identified treatment decisional status pre-consultation from medical records and post-consultation via clinicians. Patients self-reported quality-of-life before and one day after consultation.

RESULTS

Among all 6769 two-second silences, we observed 328 (4.8%) ECS and 240 (3.5%) ICS. ECS prevalence was associated with decisions favoring fewer disease-focused treatments (OR_adj: 2.12; 95% CI: 1.12, 4.06). Earlier conversational ECS was associated with improved quality-of-life (p = 0.01). ICS prevalence was associated with clinicians' prognosis expectations.

CONCLUSIONS

Connectional Silences during specialist serious illness conversations are associated with decision-making and improved patient quality-of-life. Further work is necessary to evaluate potential causal relationships.

PRACTICE IMPLICATIONS

Pauses offer important opportunities to advance the science of human connection in serious illness decision-making.

Exploratory study of language paediatricians use to promote adherence to long-term controller medication in children with asthma

INTRODUCTION AND OBJECTIVES

Although patient centred communication is associated with patients' daily medication adherence, the exact communication phenomena promoting high treatment adherence remain elusive.

PATIENTS AND METHODS

We used conversation analysis of videotaped follow-up consultations of seven outpatients (4-13 years of age) with chronic asthma and their caregivers, consulting two paediatric respiratory physicians in a practice in which high treatment adherence has been documented, to explore the language paediatricians use to promote their patients' adherence to daily controller medication.

RESULTS

Starting the consultation with the patient's (and caregivers') agenda commonly resulted in presentation of issues new to the physician. Information was mostly provided in response to patient/caregiver questions, prompting the delivery of specific information tailored to the patient's and caregivers' needs. Although patients and caregivers showed resistance in response to unsolicited information and advice, they always accepted the doctor's explicit request for agreement with proposed treatment. The doctor's description of favourable treatment results in most patients prompted caregivers' willingness to accept treatment proposals.

CONCLUSIONS

Paediatricians with a documented success in achieving adherence to controller medication in their patients with asthma tend to start consultations with the patient's agenda, provide information in response to questions, offer reassurance on overall treatment effectiveness, and seek explicit agreement with a treatment proposal.

A review and preview of developments in the measurement of sociability

Sociability is a complex, multifactorial trait. Its importance is underscored by a multitude of negative physical and mental health effects related to loneliness and social isolation. However, current measures of sociability primarily rely on subjective recall and self- report, which have inherent weaknesses and limitations. Although objective and automatic measurements could help to avoid some of these issues, they are still in early stages of development. In this article, the authors review past and present methods of measuring sociability and social interactions. This encompasses both subjective and objective subsets of qualitative and quantitative measurement modalities to gain a broader, more accurate perspective on sociability. Through an analysis of advantages and disadvantages of measurement methods within these categories, a foundational knowledge of sociability measurement can be understood. Utilizing current technology and research methods holds promise to more accurately represent individuals' social networks and social patterns.

Emotions in the room: common emotional reactions to discussions of poor prognosis and tools to address them

Introduction: Advanced cancer patients often want prognostic information, and discussions of prognosis have been shown to enhance patient understanding of their illness. Such discussions can lead to high-quality, value-consistent care at the end of life, yet they are also often emotionally challenging. Despite how common and normal it is for patients to experience transient emotional distress when receiving 'bad news' about prognosis, emotional responses have been under-addressed in existing literature on prognostic discussions. Areas covered: Drawing upon psychology research, principles of skilled clinical communication, and published approaches to discussions of serious illness, we summarize patients' common emotional reactions and coping strategies. We then provide suggestions for how to respond to them in clinic. Expert opinion: Ultimately, effective management of emotional reactions to bad news may lead to earlier, more frequent, and more transparent discussions of prognosis, thus promoting cancer patients' understanding of, and adjustment to, their illness and improving the quality of their end-of-life care.

Understanding User Experience: Exploring Participants' Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain

BACKGROUND

Delivery of behavioral health interventions on the internet offers many benefits, including accessibility, cost-effectiveness, convenience, and anonymity. In recent years, an increased number of internet interventions have been developed, targeting a range of conditions and behaviors, including depression, pain, anxiety, sleep disturbance, and eating disorders. Human support (coaching) is a common component of internet interventions that is intended to boost engagement; however, little is known about how participants interact with coaches and how this may relate to their experience with the intervention. By examining the data that participants produce during an intervention, we can characterize their interaction patterns and refine treatments to address different needs.

OBJECTIVE

In this study, we employed text mining and visual analytics techniques to analyze messages exchanged between coaches and participants in an internet-delivered pain management intervention for adolescents with chronic pain and their parents.

METHODS

We explored the main themes in coaches' and participants' messages using an automated textual analysis method, topic modeling. We then clustered participants' messages to identify subgroups of participants with similar engagement patterns.

RESULTS

First, we performed topic modeling on coaches' messages. The themes in coaches' messages fell into 3 categories: Treatment Content, Administrative and Technical, and Rapport Building. Next, we employed topic modeling to identify topics from participants' message histories. Similar to the coaches' topics, these were subsumed under 3 high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Finally, the cluster analysis identified 4 clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The name of each cluster exemplifies the main engagement patterns of that cluster.

CONCLUSIONS

In this secondary data analysis, we demonstrated how automated text analysis techniques could be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis could be used to identify subgroups of individuals who share communication and engagement patterns, and in turn facilitate personalization of interventions for different subgroups of patients. This work makes 2 key methodological contributions. First, this study is innovative in its use of topic modeling to provide a rich characterization of the textual content produced by coaches and participants in an internet-delivered behavioral health intervention. Second, to our knowledge, this is the first example of the use of a visual analysis method to cluster participants and identify similar patterns of behavior based on intervention message content.

Re-enacting the Bodily Self on Stage: Embodied Cognition Meets Psychoanalysis

The embodied approach to cognition consists in a range of theoretical proposals sharing the idea that our concepts are constitutively shaped by the physical and social constraints of our body and environment. Still far from a mutually enriching interplay, in recent years embodied and psychoanalytic approaches are converging on similar constructs as the ones of intersubjectivity, bodily self, and affective quality of verbal communication. Some efforts to cope with the sentient subject were already present in classical cognitivism: having expunged desires and conflicts from the cognitive harmony, bodily emotions re-emerged but only as a noisy dynamic friction. In contrast, the new, neural, embodied cognitive science with its focus on bodily effects/affects has enabled a dialogue between neuro-cognitive perspectives and clinic-psychological ones, through shared conceptual frameworks. I will address crucial issues that should be faced on this reconciling path. With reference to two kinds of contemporary addictions - internet addiction disorder and eating disorders - I will introduce a possible therapeutic approach that is built upon the core role of the acting-sentient bodily self in a dynamic-social and affective environment. In Psychoanalytic Psychodrama, the spontaneous re-enactment of a past (socially and physically constrained) experience is actualized by means of the other, the Auxiliary Ego. This allows homeostatic and social-emotional affects, i.e., drives and instincts, to be re-experienced by the agent, the Protagonist, in a safe scenario. The director-psychoanalyst smoothly traces back this simulation to the motivated, and constrained, early proximal embodied interactions with significant others, and to the related instinctual conflicting aims. The psychoanalytic reframing of classical psychodrama does not merely exploit its original cathartic function, rather stands out for exploring the interpersonal constitution of the self, through an actual "re-somatization" of psychoanalytic therapy. Unspoken/unspeakable feelings pop up on stage: the strength of this treatment mainly rests on re-establishing the priority of the embodied Self over the narrative Self. By pointing out the possible conflicts between these two selves, this method can broaden the embodied cognition perspective. The psychodramatic approach will be briefly discussed in light of connectionist models, to finally address linguistic and methodological pivotal issues.

Identifying Connectional Silence in Palliative Care Consultations: A Tandem Machine-Learning and Human Coding Method

Background: Systematic measurement of conversational features in the natural clinical setting is essential to better understand, disseminate, and incentivize high quality serious illness communication. Advances in machine-learning (ML) classification of human speech offer exceptional opportunity to complement human coding (HC) methods for measurement in large scale studies. Objectives: To test the reliability, efficiency, and sensitivity of a tandem ML-HC method for identifying one feature of clinical importance in serious illness conversations: Connectional Silence. Design: This was a cross-sectional analysis of 354 audio-recorded inpatient palliative care consultations from the Palliative Care Communication Research Initiative multisite cohort study. Setting/Subjects: Hospitalized people with advanced cancer. Measurements: We created 1000 brief audio "clips" of randomly selected moments predicted by a screening ML algorithm to be two-second or longer pauses in conversation. Each clip included 10 seconds of speaking before and 5 seconds after each pause. Two HCs independently evaluated each clip for Connectional Silence as operationalized from conceptual taxonomies of silence in serious illness conversations. HCs also evaluated 100 minutes from 10 additional conversations having unique speakers to identify how frequently the ML screening algorithm missed episodes of Connectional Silence. Results: Connectional Silences were rare (5.5%) among all two-second or longer pauses in palliative care conversations. Tandem ML-HC demonstrated strong reliability (kappa 0.62; 95% confidence interval: 0.47-0.76). HC alone required 61% more time than the Tandem ML-HC method. No Connectional Silences were missed by the ML screening algorithm. Conclusions: Tandem ML-HC methods are reliable, efficient, and sensitive for identifying Connectional Silence in serious illness conversations.

Automated Detection of Conversational Pauses from Audio Recordings of Serious Illness Conversations in Natural Hospital Settings

OBJECTIVE

Automating conversation analysis in the natural clinical setting is essential to scale serious illness communication research to samples that are large enough for traditional epidemiological studies. Our objective is to automate the identification of pauses in conversations because these are important linguistic targets for evaluating dynamics of speaker involvement and turn-taking, listening and human connection, or distraction and disengagement.

DESIGN

We used 354 audio recordings of serious illness conversations from the multisite Palliative Care Communication Research Initiative cohort study.

SETTING/SUBJECTS

Hospitalized people with advanced cancer seen by the palliative care team.

MEASUREMENTS

We developed a Random Forest machine learning (ML) algorithm to detect Conversational Pauses of two seconds or longer. We triple-coded 261 minutes of audio with human coders to establish a gold standard for evaluating ML performance characteristics.

RESULTS

ML automatically identified Conversational Pauses with a sensitivity of 90.5 and a specificity of 94.5.

CONCLUSIONS

ML is a valid method for automatically identifying Conversational Pauses in the natural acoustic setting of inpatient serious illness conversations.

Using lexical analysis to identify emotional distress in psychometric schizotypy

Through the use of lexical analysis software, researchers have demonstrated a greater frequency of negative affect word use in those with schizophrenia and schizotypy compared to the general population. In addition, those with schizotypy endorse greater emotional distress than healthy controls. In this study, our aim was to expand on previous findings in schizotypy to determine whether negative affect word use could be linked to emotional distress. Schizotypy (n=33) and non-schizotypy groups (n=33) completed an open-ended, semi-structured interview and negative affect word use was analyzed using a validated lexical analysis instrument. Emotional distress was assessed using subjective questionnaires of depression and psychological quality of life (QOL). When groups were compared, those with schizotypy used significantly more negative affect words; endorsed greater depression; and reported lower QOL. Within schizotypy, a trend level association between depression and negative affect word use was observed; QOL and negative affect word use showed a significant inverse association. Our findings offer preliminary evidence of the potential effectiveness of lexical analysis as an objective, behavior-based method for identifying emotional distress throughout the schizophrenia-spectrum. Utilizing lexical analysis in schizotypy offers promise for providing researchers with an assessment capable of objectively detecting emotional distress.