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.

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

Background: 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. Objective: To determine the feasibility and acceptability of the brief, remotely delivered StoryListening storytelling intervention for individuals experiencing grief during the COVID pandemic. Setting/Subjects: A single-arm pilot study was conducted in the United States. Participants included adult English-speaking family members, friends, or clinicians of individuals who died during the COVID-19 pandemic. All participants engaged in a televideo StoryListening session with a trained StoryListening doula. Measurements: Participants completed a brief follow-up telephone interview two weeks after the StoryListening session. We describe enrollment and retention data to assess feasibility and conducted a deductive thematic analysis of the follow-up interview data to assess acceptability. Results: Sixteen clinicians and 48 friends/family members enrolled in the study (n = 64; 75% enrollment), 62 completed a StoryListening session; 60 completed the follow-up interview. Participants reported that the intervention was useful and offered a valuable opportunity to process their grief experience. Conclusions: The StoryListening intervention is feasible and acceptable for friends/family members and clinicians who have experienced grief during COVID. Our intervention may offer an accessible first-line option to address the increasing wave of bereavement-related distress and clinician burnout in the United States.