Background: Interprofessional collaboration (IPC) is vital for high-quality patient care, and effective IPC among medical professionals, especially trainees, is crucial to supporting positive individual and team outcomes. Measuring IPC and understanding its impact on patient care remains underexplored. Understanding which IPC patterns are associated with better patient and individual outcomes will inform how to best [...]

Background: By law, patients have prompt access to electronic discharge notes in their charts. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models such as GPT-4 may have the potential to transform these notes into patient-friendly language and format. Our objective was to determine whether GPT-4 [...]

Background: Artificial intelligence (AI) is increasingly being integrated into healthcare, but there is limited data on clinicians’ perceptions and preparedness regarding AI in medical practice. This survey aimed to understand clinicians’ knowledge, attitudes, and experiences of healthcare providers with AI tools in healthcare. This is crucial as the adoption of AI in healthcare can enhance [...]

Background: Large Language Model (LLM) chatbots, like ChatGPT (from Open-AI), have received widespread attention in the last year for their ability to process large amounts of text data and produce human-like script responses on a wide array of topics. In particular, LLM chatbots have performed well in areas of patient communication, whether it’s answering cardiovascular [...]

Background: Clinicians, researchers, educators, and administrators have grappled with the implications of generative artificial intelligence (AI) in healthcare since the release of the first publicly available large language model (LLM) in late 2022. Efforts have been directed towards evaluating LLMs’ performance in common clinical and scholarly tasks and debating important considerations including patient safety, information [...]

Background: Diagnostic uncertainty, defined as the subjective perception of an inability to provide an accurate explanation of the patient’s health problem, has been implicated in diagnostic error.1 Clinician notes, such as the admission notes, often include hedging terms, uncertainty phrases, and diagnostic differentials that can be used to assess uncertainty in unstructured documentation.1,2 Quantifying diagnostic [...]

Background: There is growing interest in the use of artificial intelligence (AI) predictive models in hospital medicine. However, real-world implementation and evaluation of AI models lags the development of such models, with many such models being developed but never used in live practice. (1) Therefore, relatively less is known about the performance of these models [...]

Background: Hospital discharge requires coordination among multiple disciplines and may feel chaotic as discharge approaches. The 48-Hour Discharge Prediction Tool (48DPT) is an AI-based system developed to predict clinical readiness for discharge 48 hours beforehand, with the aim of alerting the interdisciplinary team and prompting earlier completion of preparatory procedures. This study assessed 48DPT’s impact [...]

Background: Diagnose and treat. That is the role of the physician. But the practice of medicine in recent years has been beaten hollow; its patient mission eclipsed by unwieldy technology and its practitioners so inundated with documentation and data entry, resulting in disengaged, frustrated, and burned out clinicians. The hospitalist has access to the entirety [...]

Background: The inpatient acute care environment has faced increasing capacity constraints since the COVID-19 pandemic. Factors like deferred care, nursing shortages, and a sicker population contribute to these constraints. Many systems have sought to improve patient flow to alleviate these issues. Efficient patient movement through care steps enhances patient satisfaction, reduces stay length, and minimizes [...]