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Clinical informatics (CI) fellowship training equips physicians to design, implement, and evaluate health information systems in support of patient care. While core curricula emphasize academic health system experiences, fellows may benefit from exposure to industry settings where much health technology innovation originates.This study aimed to characterize the structure, perceived value, and logistical challenges of industry electives among CI fellowship programs and to synthesize best practices for integrating these experiences into [...]

This study aimed to explore the performance of ChatGPT version 4.0 (GPT-4) and Gemini Advanced (Gemini) large language models (LLMs) in addressing common patient questions after gynecology surgery with regards to accuracy, relevance, helpfulness to the average patient, and readability.In this cross-sectional study, the two LLMs were prompted to generate answers to postoperative patient questions after gynecologic surgery. Postoperative patient questions were developed to simulate common patient questions after gynecologic [...]

This scoping review aimed to (1) map current applications of transformers and large language models (LLMs) for extracting social drivers of health (SDOH) from clinical text, (2) benchmark model performance across SDOH domains, and (3) evaluate methodological rigor to identify research gaps and inform clinical deployment.

To assess the performance, generalizability, and computational efficiency of instruction-tuned Large Language Model Meta AI (LLaMA)-2 and LLaMA-3 models compared to bidirectional encoder representations from transformers (BERT) for clinical information extraction (IE) tasks, specifically named entity recognition (NER) and relation extraction (RE).

Accurate phenotyping is an essential task for researchers utilizing electronic health record (EHR)-linked biobank programs like the All of Us Research Program to study human genetics. However, little guidance is available on how to select an EHR-based phenotyping procedure that maximizes downstream statistical power. This study aims to estimate accuracy of three phenotype definitions of ovarian, female breast, and colorectal cancers in All of Us (v7 release) and determine which [...]

To characterize the nature and consequence(s) of interdependent physician electronic health record (EHR) work across inpatient shifts.

Healthcare decisions are increasingly made with the assistance of machine learning (ML). ML has been known to have unfairness-inconsistent outcomes across subpopulations. Clinicians interacting with these systems can perpetuate such unfairness by overreliance. Recent work exploring ML suppression-silencing predictions based on auditing the ML-shows promise in mitigating performance issues originating from overreliance. This study aims to evaluate the impact of suppression on collaboration fairness and evaluate ML uncertainty as desiderata [...]

The use of generative large language models (LLMs) with electronic health record (EHR) data is rapidly expanding to support clinical and research tasks. This systematic review characterizes the clinical fields and use cases that have been studied and evaluated to date.

Using 2023-2024 U.S. National Health Interview Survey data, we found that digital health literacy (dHL) mediated nearly half of the difference in telehealth use between Latino adults with non-English and English language preference. These findings identify dHL as a modifiable mechanism linking linguistic and digital access barriers, underscoring the need for multilingual, inclusive, and equitable telehealth design.