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In medical research and education, generative artificial intelligence/machine learning (AI/ML) models to synthesize artificial medical data can enable the sharing of high-quality data while preserving the privacy of patients. Given that such data is often high-dimensional, a relevant consideration is whether to synthesize the entire dataset when only a task-relevant subset is needed. This study evaluates how the number of variables in training impacts fidelity, utility, and privacy of the [...]

This study evaluates and enhances language access services for Limited English Proficiency (LEP) patients in a large urban health system by integrating interpreter services into the Electronic Health Record (EHR), aiming to reduce care disparities and improve the digital experience for both patients and clinicians.

In a sample of patients with diagnosed alcohol use disorder (AUD), we examined the prevalence of both stigmatizing (eg, "alcohol abuse") and highly stigmatizing, non-medical descriptors (eg, "alcoholic") documented in electronic health records (EHRs). We also assessed whether the use of these terms-entered by clinicians when documenting AUD using search tools and picklists-varied across patient subgroups within a regional health system.

Significant opportunities for understanding the co-occurrence of conditions across species in coincident households remain untapped. We determined the feasibility of creating a Companion Care Registry (CCR) for analysis of health data from the University of Colorado Health (UCHealth) patients and their companion animals who received veterinary care at the geographically-adjacent Colorado State University Veterinary Health System (CSU-VHS).

Different natural language processing (NLP) techniques have demonstrated promising results for data extraction from radiological reports. Both traditional rule-based methods like regular expressions (Regex) and modern large language models (LLMs) can extract structured information. However, comparison between these approaches for extraction of specific radiological data elements has not been widely conducted.

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.

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.

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.

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 [...]