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To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement.

Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records.

Health and healthcare are increasingly dependent on internet and digital solutions. Medically underserved communities that experience health disparities are often those who are burdened by digital disparities. While digital equity and digital health equity are national priorities, there is limited evidence about how community-based organizations (CBOs) consider and develop interventions.

Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata.

Understand public comfort with the use of different data types for predictive models.

To report on clinical informatics (CI) fellows' job search and early careers.

The aim of this study was to investigate how healthcare staff intermediaries support Federally Qualified Health Center (FQHC) patients' access to telehealth, how their approaches reflect cognitive load theory (CLT) and determine which approaches FQHC patients find helpful and whether their perceptions suggest cognitive load (CL) reduction.