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To develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records.

Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

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.

Advances in health informatics rapidly expanded use of big-data analytics and electronic health records (EHR) by clinical researchers seeking to optimize interprofessional ICU team care. This study developed and validated a program for extracting interprofessional teams assigned to each patient each shift from EHR event logs.

 The Centers for Medicare & Medicaid Services (CMS) introduced changes in outpatient and inpatient evaluation and management (E/M) current procedural terminology (CPT) codes in 2021 and 2023, which were intended to streamline providers' clinical documentation.

 This study aimed to assist residents in selecting the correct Current Procedural Terminology (CPT) code for evaluation and management (E/M) services through the addition of disappearing help text into a standardized note template.

 The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities.

 Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be [...]

 Effective data management is critical in medical informatics research, ensuring the success and integrity of clinical studies. However, students often lack comprehensive training in this area, highlighting the need for tailored educational programs.

To assess the efficacy of a biomedical informatics boot camp with regards to improving the skill sets of its participants.