Evidence-based approach to designing wearable technology to improve patient-clinician communication: Presenter(s): David Lee, School of Medicine, University of Virginia, United States. (April 2023) - Flickinger, Tabor; LeBaron, Virginia; Boukhechba, Mehdi; Edwards, James; Wang, Zhiyuan; Ling, David; Wilson, Daniel; Barnes, Laura
Abstract : Background: While progress has been made on communication skills training for clinicians, gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting and provide timely feedback about effectiveness and quality. Methods: This 1-year, multi-phase, proof-of-concept study aims to design and pilot test 'CommSense, ' a novel wearable sensing system and associated natural language processing algorithms. Our team leverages disciplinary expertise from nursing, engineering, medicine, and library science. CommSense will be deployed on mobile devices (e.g., smartwatches) with the goal to reliably extract key markers of communication quality from real-time patient-clinician palliative care interactions. In Phase 1 (completed) we conducted a thorough review of the literature to develop an evidence-based list of quality communication metrics and determined how these metrics could be best operationalized. Phases 2 and 3 (in-progress) will involve design of the CommSense system and pilot testing with simulated clinical scenarios to assess fidelity of data capture and extraction of desired metrics. Findings: We identified 96 articles from relevant databases in consultation with a health sciences librarian; 18 articles were added based on expert opinion. After screening article titles and abstracts (n=114) for relevance, 72 articles were selected for full-text extraction and independently reviewed by 2 members of our clinical team. We used a 'talk-aloud' thematic analysis approach to develop a list of 7 core communication metrics (5 verbal; 2 non-verbal) with associated strategies for operationalization. Discussion: Clinician-patient communication is complex and determining appropriate metrics for evaluation is challenging. We propose a core set of communication 'best practices' that can be extracted from conversations using the CommSense technology. The long-term goal of this research is to deploy CommSense in a variety of healthcare contexts to provide real-time feedback to end-users to improve communication and patient-centered health outcomes.