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To evaluate whether large language models (LLMs) can automate chart review to identify tumor necrosis factor inhibitor (TNFi) switching patterns and reasons for switching in a large real-world cohort.

Clinician perspectives on machine learning and artificial intelligence (ML/AI) vary with discipline. However, fewer studies describe patient perspectives and address medical situations that require rapid decisions with durable consequences for patient outcomes. This study characterized perspectives (qualitatively) and sentiment (quantitatively) regarding the use of ML/AI for patient management in neurological emergencies.

To address algorithmic bias in clinical prediction models related to the timing of diagnosis, we evaluated the efficacy of mixture cure models that integrate time-to-event and binary classification frameworks to predict diagnoses.

To identify causal mechanisms driving variations in the impact of rehabilitation treatments on stroke survivors' independence improvement during rehabilitation inpatient stays.

Sharing clinical data for research that is both collaborative and privacy-preserving remains a challenge. Differential privacy (DP) offers a solution by introducing noise to query results. Using the PrivateSQL DP platform, this study assesses the resulting utility of differentially private data at different levels of aggregation through analyses of COVID-19 pandemic associations with new cancer diagnosis counts (NCCs).

A point-of-care resource is required to support Registered Dietitian Nutritionists (RDNs) in providing comprehensive culturally-tailored nutrition services. This study aims to describe the iterative development and testing of a comprehensive nutrition assessment tool as part of a novel web-based platform to Support Personalized and Inclusive Cuisines in Environments for Healthcare (SPICE-Healthcare).

Critically ill patients are managed with complex medication regimens that require medication management to optimize safety and efficacy. When performed by a critical care pharmacist (CCP), discrete medication management activities are termed medication interventions. The ability to define CCP workflow and intervention timeliness depends on the ability to predict the medication management needs of individual intensive care unit (ICU) patients. The purpose of this study was to develop prediction models [...]

Systemic mastocytosis (SM) is a rare disorder with heterogeneous, multisystem symptoms that often lead to diagnostic delays. Real-world symptom documentation in unstructured clinical notes may hold untapped potential for earlier recognition, but scalable extraction methods are lacking.

The Bridge2AI program is establishing rules of practice for creating ethically sourced health data repositories to support the effective use of ML/AI in biomedical and behavioral research. Given the initially undefined nature of ethically sourced data, this work concurrently developed definitions and guidelines alongside repository creation, grounded in a practical, operational framework.

To develop and evaluate a patient-centric medication module within a personal health record (PHR) app for capturing medication use, focusing on accuracy, usability, and concordance.