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Accelerating demand for patient messaging has impacted the practice of many providers. Messages are not recommended for urgent medical issues, but some do require rapid attention. This presents an opportunity for artificial intelligence (AI) methods to prioritize review of messages. Our study aimed to highlight some patient portal messages for prioritized review using a custom AI system integrated into the electronic health record (EHR).

 Efforts to reduce documentation burden (DocBurden) for all health professionals (HP) are aligned with national initiatives to improve clinician wellness and patient safety. Yet DocBurden has not been precisely defined, limiting national conversations and rigorous, reproducible, and meaningful measures. Increasing attention to DocBurden motivated this work to establish a standard definition of DocBurden, with the emergence of excessive DocBurden as a term.

Large language models (LLMs) have demonstrated remarkable success in natural language processing (NLP) tasks. This study aimed to evaluate their performances on social media-based health-related text classification tasks.

 The integration of patient-reported outcomes (PROs) into clinical care, particularly in the context of cancer and multimorbidity, is crucial. While PROs have the potential to enhance patient-centered care and improve health outcomes through improved symptom assessment, they are not always adequately documented by the health care team.

 Health-related social needs (HRSNs) are the unmet social and economic needs (e.g., housing instability) that affect individuals' health and well-being. HRSNs are associated with more emergency department (ED) visits, longer stays, and worse health outcomes. More than a third of ED patients have at least one HRSN, yet patients are rarely screened for HRSNs in the ED. A clinical decision support (CDS) system with predictive modeling offers a promising approach [...]

 Treatment of depressive symptoms in older adults is a growing public health concern. Collecting patient-reported outcomes (PROs) may facilitate efficiently scaling psychotherapy for older adults but user-specific tailoring is needed to improve completion.

To enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses.

Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU).

This article focuses on the role of the electronic health record (EHR) to generate meaningful formative feedback for medical students in the clinical setting. Despite the scores of clinical data housed within the EHR, medical educators have only just begun to tap into this data to enhance student learning. Literature to-date has focused almost exclusively on resident education.

We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).