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 Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.

Post-discharge adverse events (AEs) are common and heralded by new and worsening symptoms (NWS). We evaluated the effect of electronic health record (EHR)-integrated digital tools designed to promote quality and safety in hospitalized patients on NWS and AEs after discharge.

Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical [...]

To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities.

To improve firearm injury encounter classification (new vs follow-up) using machine learning (ML) and compare our ML model to other common approaches.

To evaluate the use of patient portal messaging to recruit individuals historically underrepresented in biomedical research (UBR) to the All of Us Research Program (AoURP) at a single recruitment site.

Conduct a scoping review of research studies that describe rule-based clinical decision support (CDS) malfunctions.

 Clinical data sharing is common and necessary for patient care, research, public health, and innovation. However, the term "data sharing" is often ambiguous in its many facets and complexities-each of which involves ethical, legal, and social issues. To our knowledge, there is no extant hierarchy of data sharing that assesses these issues.

Outpatient rehabilitation (rehab) physical, occupational, and speech therapists use electronic health records (EHR), yet their documentation experiences, including any documentation burden, are not well researched. Therapists are a growing portion of the U.S. healthcare workforce, whose need is critical to the health of an aging population. We aimed to describe outpatient rehab therapists' documentation experiences and identify strategies for mitigating any documentation burden.

Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering.