Computational Use of Patient-Provider Secure Messaging Data to Achieve Better Clinical Efficiency and Quality of...

Secure messaging (SM) between patients and providers has seen increasing adoption over the past decade, prompting development of computational methods to enable automation and research to enhance clinical efficiency and quality of communication. Through a systematic review, we examined the extant literature to investigate how previous studies had applied computational analyses to SM data. After screening 1,374 papers, we identified 19 relevant studies published between 2017 and 2024, most of which focused on applications for streamlining clinical workflows, facilitating early disease detection, supporting personalized decision making, and enhancing patient health literacy. Among the computational methods used, BERT was consistently shown to deliver best performance. However, all existing studies were constrained by small-size datasets and limited healthcare settings, leading to inadequate validation and poor generalizability. The results of this review highlight key research gaps, particularly the need for more robust computational approaches that ensure scalability, fairness, and clinical applicability.