Epidemiology of Fear, Sadness, and Anger Expression in Palliative Care Conversations

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.

Emotional distress predicts palliative cancer care attitudes: The unique role of anger

OBJECTIVE

Although palliative care can mitigate emotional distress, distressed patients may be less likely to engage in timely palliative care. This study aims to investigate the role of emotional distress in palliative care avoidance by examining the associations of anger, anxiety, and depression with palliative care attitudes.

METHODS

Patients (N = 454) with heterogeneous cancer diagnoses completed an online survey on emotional distress and palliative care attitudes. Emotional distress was measured using the Patient-Reported Outcomes Measurement Information System anger, anxiety, and depression scales. The Palliative Care Attitudes Scale was used to measure palliative care attitudes. Regression models tested the impact of a composite emotional distress score calculated from all three symptom measures, as well as individual anger, anxiety, and depression scores, on palliative care attitudes. All models controlled for relevant demographic and clinical covariates.

RESULTS

Regression results revealed that patients who were more emotionally distressed had less favorable attitudes toward palliative care (p < 0.001). In particular, patients who were angrier had less favorable attitudes toward palliative care (p = 0.013) while accounting for depression, anxiety, and covariates. Across analyses, women had more favorable attitudes toward palliative care than men, especially with regard to beliefs about palliative care effectiveness.

CONCLUSIONS

Anger is a key element of emotional distress and may lead patients to be more reluctant toward timely utilization of palliative care. Although psycho-oncology studies routinely assess depression or anxiety, more attention to anger is warranted. More research is needed on how best to address anger and increase timely utilization of palliative cancer care.

Content of Tele-Palliative Care Consultations with Patients Receiving Dialysis

Background: Little is known about the content of communication in palliative care telehealth conversations in the dialysis population. Understanding the content and process of these conversations may lead to insights about how palliative care improves quality of life. Methods: We conducted a qualitative analysis of video recordings obtained during a pilot palliative teleconsultation program. We recruited patients receiving dialysis from five facilities affiliated with an academic medical center. Palliative care clinicians conducted teleconsultation using a wall-mounted screen with a camera mounted on a pole and positioned mid-screen in the line of sight to facilitate direct eye contact. Patients used an iPad that was attached to an IV pole positioned next to the dialysis chair. Conversations were coded using a preexisting framework of themes and content from the Serious Illness Conversation Guide (SICG) and revised Edmonton Symptom Assessment System-Renal. Results: We recruited 39 patients to undergo a telepalliative care consultation while receiving dialysis, 34 of whom completed the teleconsultation. Specialty palliative care clinicians (3 physicians and 1 nurse practitioner) conducted 35 visits with 34 patients. Median (interquartile range) duration of conversation was 42 (28-57) minutes. Most frequently discussed content included sources of strength (91%), critical abilities (88%), illness understanding (85%), fears and worries (85%), what family knows (85%), fatigue (77%), and pain (65%). Process features such as summarizing statements (85%) and making a recommendation (82%) were common, whereas connectional silence (56%), and emotion expression (21%) occurred less often. Conclusions: Unscripted palliative care conversations in outpatient dialysis units through telemedicine exhibited many domains recommended by the SICG, with less frequent discussion of symptoms. Emotion expression was uncommon for these conversations that occurred in an open setting.

Toward a basic science of communication in serious illness

High-quality communication can mitigate suffering during serious illness. Innovations in theory and technology present the opportunity to advance serious illness communication research, moving beyond inquiry that links broad communication constructs to health outcomes toward operationalizing and understanding the impact of discrete communication functions on human experience. Given the high stakes of communication during serious illness, we see a critical need to develop a basic science approach to serious illness communication research. Such an approach seeks to link "what actually happens during a conversation" - the lexical and non-lexical communication content elements, as well as contextual factors - with the emotional and cognitive experiences of patients, caregivers, and clinicians. This paper defines and justifies a basic science approach to serious illness communication research and outlines investigative and methodological opportunities in this area. A systematic understanding of the building blocks of serious illness communication can help identify evidence-informed communication strategies that promote positive patient outcomes, shape more targeted communication skills training for clinicians, and lead to more tailored and meaningful serious illness care.

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.

A novel Scale to Assess Palliative Care Patients' Experience of Feeling Heard and Understood

CONTEXT

Patient experience of palliative care serves as an important indicator of quality and patient-centeredness.

OBJECTIVES

To develop a novel patient-reported scale measuring ambulatory palliative care patients' experience of feeling heard and understood by their providers.

METHODS

We used self-reported patient experience data collected via mixed-mode survey administration. We conducted an exploratory factor analysis (EFA) and an expert panel ranking exercise to reduce the 10-item set based on underlying dimensionality. We then used item response theory (IRT) to calibrate remaining items based on psychometric properties and test information and precision. We considered item-level fit and examined the standardized local dependence chi-square statistics. We evaluated candidate items for differential item functioning by survey mode. We evaluated the test-retest reliability and validity of the final scale.

RESULTS

The EFA yielded a single factor (9/10 items had loadings > 0.80 on the single factor). We removed two items with the lowest factor loadings and ranked by the expert panel as being least reflective of the overall construct. IRT calibration of the remaining eight items showed high slopes (range 2.66 - 5.18); location parameters were all negative (range -0.90 - -0.36). We removed two more items based on local dependence indices and item-level fit. Combining psychometric information with the expert ratings we established the final 4-item scale, which was reliable (Cronbach's alpha = 0.84; polychoric correlation coefficient = 0.72) and had good convergent validity.

CONCLUSIONS

This novel multi-item Feeling Heard and Understood scale can be used to measure and improve ambulatory palliative care patient experience.

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

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.