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To develop and validate machine learning (ML) models that predict probable cause of death (CoD) using structured electronic health record (EHR) data, unstructured clinical notes, and publicly available sources.

The expansion of artificial intelligence (AI)-enabled clinical decision support (CDS) requires nurses to interpret complex model outputs. However, their cognitive readiness remains underexplored, particularly in terms of their understanding of statistics. To assess nurses' understanding of key statistical concepts underlying AI predictions and their relationship to health numeracy.

The COVID-19 pandemic caused a major health crisis worldwide significantly impacting mental well-being. In this study, our objective is to assess the resilience of pre-pandemic depression level prediction models when applied to COVID-19 era data. We leverage advanced Machine Learning (ML) and Explainable Artificial Intelligence (XAI) techniques to identify the key factors impacting the shifts in depression levels during the pandemic. We aim to align the later identification with interventions [...]

This study aims to develop a detailed understanding of provider and Information Technology (IT) operations staff experiences and attitudes regarding patients' ability to edit their data. This includes understanding barriers to developing a process to write back data into the electronic health record (EHR) as well as a concrete set of recommendations on incorporating patient-generated data into the EHR.

To develop a multimodal pediatric critical care datamart supporting predictive modeling and decision support tool development, integrating high-resolution physiologic and clinical data and future clinical deployment.

Electronic health record (EHR) order preference lists and order sets potentially improve efficiency but have limited utility in complex primary care settings. We assessed adoption, impact on ordering efficiency, and clinician perceptions of a comprehensive set of nested order panels (xOrders) for adult primary care.

This study explores the capability of passive digital sensor data from smartphones and smartwatches to predict self-reported ecological momentary assessments (EMA) of affect, motivation, interest, and pleasure in activities in an unseen test sample.

To explore how Spanish-speaking caregivers navigate translation barriers in patient portals and to assess their perspectives on improving language accessibility.

The study aimed to develop a deep learning-based model, using global and local explainability methods, to process clinical data collected in community pharmacies and identify the key variables influence health-related quality of life in patients with chronic diseases.

To evaluate how electronic health records (EHR) optimization and data analytics supported a large rural health system action plan to mitigate the effects of a nation-wide blood culture bottle shortage.