Methodological innovations to strengthen evidence-based serious illness communication.

Abstract

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. First, advanced computation techniques - e.g. machine-learning techniques and natural language processing - offer the possibility to measure the characteristics and complex patterns of audible serious illness communication in large datasets. Second, immersive technologies - e.g., virtual- and augmented reality - allow for experimentally manipulating and testing the effects of specific communication strategies, and interactional and environmental aspects of serious illness communication. Third, digital-health technologies - e.g., shared notes and videoconferences - can be used to unobtrusively observe and manipulate communication, and compare in-person to digitally-mediated communication elements and effects. Immersive and digital health technologies allow integration of physiological measurement (e.g. synchrony or gaze) that may advance our understanding of patient experience. New technologies and measurement approaches, while imperfect, will help advance our understanding of the epidemiology and quality of serious illness communication in an evolving healthcare environment.