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 This study aimed to evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision support system (DSS).

 While necessary and educationally beneficial, administrative tasks such as case and patient tracking may carry additional burden for surgical trainees. Automated systems targeting these tasks are scarce, leading to manual and inefficient workflows.

To understand barriers to obtaining and using interoperable information at US hospitals.

The proliferation of electronic health record (EHR) alerts has led to widespread alert fatigue and clinician burnout, undermining the effectiveness of clinical decision support and compromising patient safety.

The COVID pandemic accelerated the adoption of telehealth. Long-term implications for continued telehealth use on a large scale in primary care are still emerging, and the key to this understanding is how telehealth adoption impacts the delivery of health care.

We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative [...]

The aim of this study was to describe opportunities and challenges associated with the development and implementation of a program for supporting researchers underrepresented in biomedical research.

With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment.

This study aims to investigate whether different types of electronic health record (EHR) users have distinct preferences for data quality assessment indicators (DQAI) and explore how these preferences can guide the enhancement of EHR systems and the optimization of related policies.