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This study investigates the feasibility of using large language models (LLMs) to automate procedural case log documentation in radiology training. We evaluate whether AI can replace manual logging, identify which procedure types are most challenging for extraction, and assess integration into clinical workflows.

Although machine learning (ML) holds significant potential to transform healthcare, there has been a recent surge in research output that often lacks methodological rigor, contributing to a reproducibility crisis. Additionally, the growing reliance on electronic health records (EHR) for developing ML models has heightened concerns about patient data privacy. To tackle these challenges, we have extended the open-source i2b2 (Informatics for Integrating Biology and the Bedside) platform to allow researchers [...]

The objective of this research was to examine the content and context-specific information diffusion patterns underlying communication pertaining to tobacco use from online health communities (OHCs).

Electronic health record (EHR) transitions disrupt health system workforces. Health professions trainees (HPTs) play a critical role in providing care yet are under-addressed during transitions. We apply learning health system (LHS) principles to improve HPTs' training experiences during the Veterans Affairs nationwide EHR transition.

The integration of artificial intelligence (AI) into health care is critically dependent on vast quantities of patient data, igniting an urgent global debate on data ownership, privacy, and governance. While numerous perspectives exist, the empirical structure and evolution of this scholarly discourse remain uncharacterized. We aimed to systematically map the conceptual landscape of research on AI and health data governance to identify its core themes, temporal trends, and key focus [...]

Early warning systems (EWSs) help clinicians identify deteriorating patients using clinical data, such as vital signs. However, standard systems struggle to capture nuanced nursing concerns. The Healthcare Process Model-ExpertSignals (HPM-ExpertSignals) framework describes how nurses' concerns are reflected in their documentation patterns. While a recent trial showed positive outcomes, the predictive gain of combining both data types remains unquantified.

With the rapid growth of unstructured clinical narratives in electronic health records (EHRs), clinical named entity recognition (NER) has become a crucial technique for extracting structured medical information. However, traditional supervised models such as CRF and BioClinicalBERT rely on costly manual annotations. Although large language model (LLM)-based zero-shot NER reduces the dependency on labeled data, challenges remain in aligning example selection with task granularity and in effectively integrating prompt design [...]

Characterise longitudinal patterns of chiropractic visits for neck pain or low back pain by using machine learning (ML) methods and explainable models.