Grant Number:	1R21CA259543-01A1 Interpret this number
Primary Investigator:	Gramling, Robert
Organization:	University Of Vermont & St Agric College
Project Title:	Resubmission Deep Neural Networks to Identify Moments of Human Connection in Cancer Communication
Fiscal Year:	2024

PROJECT SUMMARY/ABSTRACT Promoting high quality communication in serious illness is a pressing national priority. However, traditional approaches to measuring communication in the natural clinical setting are cumbersome, expensive, not confidential and time-consuming. In order to understand, disseminate, support and incentivize high quality communication, we need measurement methods that can be scaled for larger clinical studies and routine health service quality reporting. This proposal leverages extraordinary data that we have at our disposal and our team's expertise in machine learning to automatically measure features of serious illness communication quality in the natural clinical setting. We focus on episodes of Connectional Silence that occur between clinicians and patients because these moments are markers of effective listening, engaged shared decision- making and compassionate presence. The Palliative Care Communication Research Initiative (PCCRI) is a multi-site cohort study of a diverse population of 240 hospitalized patients with advanced cancer and 54 palliative care clinicians. Participants represent diversity in age, gender, racial and ethnic backgrounds, religious affiliations, levels of financial insecurity, linguistic repertoire, personality and treatment preferences. The cohort data include 363 audio-recorded palliative care inpatient consultations comprising more than 10,000 minutes of conversation, 180,000 speaker turns, and 1.4 million words. We have completed the following Preliminary Studies in preparation for this proposed work: (a) developed a supervised machine learning algorithm to identify over 28,000 conversational pauses in speaking amid the noisy hospital environment (e.g., oxygen masks, IV pumps, televisions, hallway voices, etc.),(b) developed a reliable Human Coding approach for identifying Connectional Silence as a distinct type of conversational pause, and (c) successfully trained preliminary deep neural networks with both lexical and sound data surrounding pauses in the PCCRI data. Our team is well positioned to overcome traditional barriers to direct measurement of communication quality in healthcare settings, advance machine learning technology for analysis of clinical conversations, and ultimately, improve communication for all seriously ill people. We propose to complete the following over the two-year award period: Aim One) to train a Deep Neural Network that uses lexical features of the immediate surrounding conversation to accurately distinguish moments of Connectional Silence from other types of pauses; Aim Two) Using de-identified spectral images of sound surrounding conversational pauses, to develop an integrated deep learning algorithm that uses both lexicon and prosody of speech data to distinguish moments of Connectional Silence from other types of conversational pauses. Exploratory Aim) to identify the features of communication surrounding Connectional Silence that are associated with patients feeling more Heard & Understood.





None