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 Telemetry monitoring is crucial for high-risk patients but excessive use beyond practice standards increases costs. Prior studies have shown that electronic health record (EHR) alerts reduce low-value telemetry monitoring. However, specific components of these alerts that contribute to effectiveness are unknown.

 The electronic health record (EHR) has been associated with provider burnout, exacerbated by increasing In-Basket burden.

 Experiential learning through simulation allows students to apply didactic knowledge to real-world situations. Tabletop simulation allows for the exploration of a variety of topics, including cybersecurity in health care. Due to its low frequency, yet high-risk nature, simulation is a perfect educational modality to practice responding to a cybersecurity attack. As such, the authors designed and executed a tabletop cybersecurity simulation consisting of a prebriefing, four rounds of injects detailing [...]

 Evidence supports using patient-generated blood pressure data for better outcomes in hypertension management. However, obstacles like dealing with home-generated paper data sets and questions of validity slowed the meaningful incorporation of home blood pressure into clinical care. As clinicians value patient data more, reliance on digital health solutions for data collection and shared decision-making grows.

 Treatment of depressive symptoms in older adults is a growing public health concern. Collecting patient-reported outcomes (PROs) may facilitate efficiently scaling psychotherapy for older adults but user-specific tailoring is needed to improve completion.

 This study aimed to highlight the necessity of developing and implementing appropriate reference ranges for transgender and nonbinary (TGNB) patient populations to minimize misinterpretation of laboratory results and ensure equitable health care.

 In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.

 The main objective of this study is to evaluate the ability of the Large Language Model Chat Generative Pre-Trained Transformer (ChatGPT) to accurately answer the United States Medical Licensing Examination (USMLE) board-style medical ethics questions compared to medical knowledge-based questions. This study has the additional objectives of comparing the overall accuracy of GPT-3.5 to GPT-4 and assessing the variability of responses given by each version.

 Clinical Decision Support (CDS) tools have a mixed record of effectiveness, often due to inadequate alignment with clinical workflows and poor usability. While there is a consensus that usability testing methods address these issues, in practice, usability testing is generally only used for selected projects (such as funded research studies). There is a critical need for CDS operations to apply usability testing to all CDS implementations.

 N-of-1 trials have emerged as a personalized approach to patient-centered care, where patients can compare evidence-based treatments using their own data. However, little is known about optimal methods to present individual-level data from medication-related N-of-1 trials to patients to promote decision-making.