A Data-Driven Framework to Understand the Work of Electronic Asynchronous Clinical Communication
Asynchronous messaging through the electronic health record (EHR) provides care team members a secure means of communication to reach other care stakeholders, regardless of role or location. Despite reports that communication load contributes to professional exhaustion, little consideration has been given to understand the amount of work required to manage asynchronous messages. In this work, I developed a scalable framework of methods to support data-driven strategies to measure the scope, volume, and work of clinical asynchronous messaging on multidisciplinary healthcare teams. The framework is supported by quantitative approaches, which can be applied across patient populations, clinical settings, and institutions. This work is the first to combine secure message logs and EHR usage logs to provide direct insights into communication and information sharing patterns within an organization. Similarly, I quantify communication patterns among all healthcare team members to identify how asynchronous messaging contributes to the work of the individual, team members, and other teams. Applying this framework to understand the complexities of clinical messaging enables data-driven discovery to support actionable insights into improving the work to deliver healthcare and ultimately reducing professional exhaustion among healthcare workers.