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

Unlabelled: There is demand for scalable algorithms capable of clustering and analyzing large time series data. The Kohonen self-organizing map (SOM) is an unsupervised artificial neural network for clustering, visualizing, and reducing the dimensionality of complex data. Like all clustering methods, it requires a measure of similarity between input data (in this work time series).

Patient-Caregiver Dyads & End-of-Life Care: Caregiver Personality Disrupts Gender-Based Norms.

Context: Gender and personality may individually impact end-of-life care. Men often receive more aggressive treatments than women near death, and personality - particularly openness - may be associated with increased care utilization when it diverges from traditional treatment norms. However, research has not examined the interaction of these variables in a dyadic context.

Protocol for a Scalable StoryListening Intervention for Grief-Related Loneliness During COVID-19.

Background: Social distancing during the COVID-19 pandemic limited how family, friends, and clinicians physically interacted with people who were dying and decreased communal opportunities for processing grief. These barriers can cause or exacerbate suffering due to loneliness while grieving.

Purpose: In this article, we describe the protocol for a brief storytelling intervention designed to reduce loneliness among families, friends, and clinicians grieving the death of a person during the time of COVID-19.

Developing a Direct Observation Measure of Therapeutic Connection in Psilocybin-Assisted Therapy: A Feasibility Study.

Measuring therapeutic connection during psilocybin-assisted therapy is essential to understand underlying mechanisms, inform training, and guide quality improvement. To evaluate the feasibility of directly observing indicators of therapeutic connection during psilocybin administration encounters. We evaluated audio and video data from a recent clinical trial for observable expressions of therapeutic connection as defined in proposed best-practice competencies (i.

An Acoustical and Lexical Machine-Learning Pipeline to Identify .

Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. To assess the feasibility of automating the identification of a conversational feature, which is associated with important patient outcomes. 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.

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.

Integration of palliative care consultation into the management of patients with chronic limb-threatening ischemia.

Objective: We assessed the feasibility of integrating palliative care consultation into the routine management of patients with chronic limb-threatening ischemia (CLTI). Additionally, we sought to describe patient-reported outcomes from the palliative care and vascular literature in patients with CLTI receiving a palliative care consultation at our institution.

Methods: This was a single-institution, prospective, observational study that aimed to assess feasibility of incorporating palliative care consultation into the management of patients admitted to our tertiary academic medical center with CLTI by looking at utilization of palliative care before and after implementation of a protocol-based palliative care referral system.

Sentiment analysis of medical record notes for lung cancer patients at the Department of Veterans Affairs.

Natural language processing of medical records offers tremendous potential to improve the patient experience. Sentiment analysis of clinical notes has been performed with mixed results, often highlighting the issue that dictionary ratings are not domain specific. Here, for the first time, we re-calibrate the labMT sentiment dictionary on 3.

The Project: Feasibility and Acceptability of a Remotely Delivered Intervention to Alleviate Grief during the COVID-19 Pandemic.

The events surrounding the COVID-19 pandemic have created heightened challenges to coping with loss and grief for family and friends of deceased individuals, as well as clinicians who experience loss of their patients. There is an urgent need for remotely delivered interventions to support those experiencing grief, particularly due to growing numbers of bereaved individuals during the COVID-19 pandemic. To determine the feasibility and acceptability of the brief, remotely delivered StoryListening storytelling intervention for individuals experiencing grief during the COVID pandemic.

A Formative Mixed-Methods Study of Emotional Responsiveness in Telepalliative Care.

It is unknown whether telemedicine-delivered palliative care (tele-PC) supports emotionally responsive patient-clinician interactions. We conducted a mixed-methods formative study at two academic medical centers in rural U.S.

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.

Content of Tele-Palliative Care Consultations with Patients Receiving Dialysis.

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. We conducted a qualitative analysis of video recordings obtained during a pilot palliative teleconsultation program.

Patient, caregiver, and oncologist predictions of quality of life in advanced cancer: Accuracy and associations with end-of-life care and caregiver bereavement.

Background: Informed treatment decision-making necessitates accurate prognostication, including predictions about quality of life.

Aims: We examined whether oncologists, patients with advanced cancer, and caregivers accurately predict patients' future quality of life and whether these predictions are prospectively associated with end-of-life care and bereavement.

Materials & Methods: We conducted secondary analyses of clinical trial data.

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.

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.

Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication.

Background: Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations.

Discussion: Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts.

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.

"If it's the time, it's the time": Existential communication in naturally-occurring palliative care conversations with individuals with advanced cancer, their families, and clinicians.

Objective: To explore how patients with advanced cancer, their families, and palliative care clinicians communicate about existential experience during palliative care conversations.

Methods: We analyzed data from the Palliative Care Communication Research Initiative (PCCRI) - a multisite cohort study conducted between 2014 and 2016 involving hospitalized adults with advanced cancer who were referred for inpatient palliative care consultations at two academic medical centers. We used a qualitative descriptive approach paired with inductive content analysis to analyze a random subsample of 30 patients from the PCCRI study (contributing to 38 palliative care conversations).

"I Had a Lot More Planned": The Existential Dimensions of Prognosis Communication with Adults with Advanced Cancer.

Communication about prognosis is a key ingredient of effective palliative care. When patients with advanced cancer develop increased prognostic understanding, there is potential for existential distress to occur. However, the existential dimensions of prognosis communication are underexplored.

Using natural language processing to explore heterogeneity in moral terminology in palliative care consultations.

Background: High quality serious illness communication requires good understanding of patients' values and beliefs for their treatment at end of life. Natural Language Processing (NLP) offers a reliable and scalable method for measuring and analyzing value- and belief-related features of conversations in the natural clinical setting. We use a validated NLP corpus and a series of statistical analyses to capture and explain conversation features that characterize the complex domain of moral values and beliefs.

Feasibility and Acceptability of Telemedicine-Facilitated Palliative Care Consultations in Rural Dialysis Units.

Patients receiving dialysis have unmet palliative care needs. Limited access to palliative care is a key barrier to its integration into routine dialysis care. To determine the feasibility and acceptability of telepalliative care in rural dialysis units.

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

Context: Advancing the science of serious illness communication requires methods for measuring characteristics of conversations in large studies. Understanding which characteristics predict clinically important outcomes can help prioritize attention to scalable measure development.

Objectives: To understand whether audibly recognizable expressions of distressing emotion during palliative care serious illness conversations are associated with ratings of patient experience or six-month enrollment in hospice.

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

Objective: Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights about the taxonomy of serious illness conversations.

Methods: We analyzed verbatim transcripts from 354 consultations involving 231 patients and 45 palliative care clinicians from the Palliative Care Communication Research Initiative.