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 We developed and implemented a customized internal clinical informatics (CI) curriculum for the UC Irvine CI Fellowship program. The goal was to transition from an externally sourced curriculum to a more focused, internally developed program, aligning with feedback from early fellows and the evolving practical needs of clinical informatics. By designing this curriculum in-house, we sought to provide a more efficient, cost-effective, and relevant educational experience for fellows.

 The main objective of this study is to evaluate the ability of the Large Language Model Chat Generative Pre-Trained Transformer (ChatGPT) to accurately answer the United States Medical Licensing Examination (USMLE) board-style medical ethics questions compared to medical knowledge-based questions. This study has the additional objectives of comparing the overall accuracy of GPT-3.5 to GPT-4 and assessing the variability of responses given by each version.

 The objective of this study was to investigate the impact of enhancing a structured-data-based suicide attempt risk prediction model with temporal Concept Unique Identifiers (CUIs) derived from clinical notes. We aimed to examine how different temporal schemes, model types, and prediction ranges influenced the model's predictive performance. This research sought to improve our understanding of how the integration of temporal information and clinical variable transformation could enhance model predictions.

The aim of this study was to investigate GPT-3.5 in generating and coding medical documents with International Classification of Diseases (ICD)-10 codes for data augmentation on low-resource labels.

During the 2-year maintenance treatment phase (MT) of acute lymphoblastic leukemia (ALL), personalized patient-specified titration of oral antimetabolite drug doses is required to ensure maximum tolerated systemic drug exposure. Drug titration is difficult to implement in practice and insufficient systemic drug exposure resulting from inadequate dose titration increases risk of ALL relapse.

This study aimed to optimize Fall Risk Appraisal (FRA) graphing for use in intervention programs tailored toward reducing the fall risk of older adults by using computing graphic functions in the R language.

Starting in 2018, the 'Women in American Medical Informatics Association (AMIA) Podcast' was women-focused, in 2021 the podcast was rebranded and relaunched as the "For Your Informatics Podcast" (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community.

To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.

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

 Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic electronic health records, supporting tailored learning activities in informatics education to enhance educational utility through [...]