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 This study aimed to explore clinicians' perceptions and preferences of prototype intelligence augmentation (IA)-based visualization displays of in-hospital deterioration risk scores to inform future user interface design and implementation in clinical care.

 Automation of test follow-up offers potential reductions in workload for clinicians. The primary objective of the study was to evaluate the performance of MicrobEx, a regular expression-based algorithm in classifying urine culture reports in primary care.

 Patients with cirrhosis are at high risk for developing hepatocellular carcinoma (HCC), warranting receipt of semiannual surveillance imaging with the potential to identify abnormal liver lesions. Since the implementation of the 21st Century Cures Act's Information Blocking provision, test results are immediately released to patients through the patient portal. There is an increasing trend of patients reviewing their results before their providers.

The degree to which deployed artificial intelligence-based deterioration prediction algorithms (AI-DPA) differ in their development, the reasons for these differences, and how this may impact their performance remains unclear. Our primary objective was to identify design factors and associated decisions related to the development of AI-DPA and highlight deficits that require further research.

 This study seeks to understand pediatric rheumatology patients' and caregivers' utilization and perceptions of immediate access to their electronic health information (EHI) via patient portals.

 This study aimed to demonstrate real-world use of the Making Electronic Data More Available for Research and Public Health (MedMorph) Reference Architecture (RA) for automated exchange of hepatitis C-related data for public health surveillance and research using Fast Healthcare Interoperability Resources (FHIR).

Generative AI, particularly large language models (LLMs), holds great potential for improving patient care and operational efficiency in healthcare. However, the use of LLMs is complicated by regulatory concerns around data security and patient privacy. This study aimed to develop and evaluate a secure infrastructure that allows researchers to safely leverage LLMs in healthcare while ensuring HIPAA compliance and promoting equitable AI.

Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognition (NER) model to extract PICO entities with fine granularities.

Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis. Furthermore, we introduce a benchmark of the summarization [...]

We aim to demonstrate the versatility of the All of Us database as an important source of rare and undiagnosed disease (RUD) data, because of its large size and range of data types.