Understanding Primary and Secondary Concerns from Patient Portal Messages through Clinical Data Annotation, Analysis,...

Efficient triage and response to patient portal messages (PPMs) are critical for enhancing patient-centered care. To improve the understanding of primary and secondary concerns expressed by patients, this study annotated and analyzed a set of 2,239 PPMs. We also automated the patient concern identification and analysis by leveraging pretrained language models with binary classification to discern all patient concerns and with multi-class classification to identify primary patient concerns. These multi-class classifications were further enhanced by integrating convolutional neural networks that utilize embeddings from the binary classification. This approach demonstrated significant potential of AI in managing the growing volume of PPMs and promptly addressing the healthcare needs of patients, thereby facilitating more effective and timely medical interventions.