Improving Emergency Department Visit Risk Prediction: Exploring the Operational Utility of Applied Patient Portal Messages.

Patient portal messages represent a unique source of clinical data due to how they represent the voice of the patient, provide a glimpse into care delivery between episodic synchronous appointments, and capture variations in patient behavior and health literacy. There is little understanding of how to best apply modern natural language processing (NLP) approaches, such as large, pre-trained language models (LLMs), to patient messages. In this study, we aim to explore different approaches in incorporating patient messages into an existing Emergency Departments (ED) visit risk prediction model currently deployed at Stanford Health Care. With the addition of patient message frequencies to the baseline we were able to achieve an improved AUC of .77 and a jump in the F1 score. In future work, we aim to build upon these findings and further test combination models to incorporate features around patient message content, in addition to message frequencies.