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 Clinicians play an important role in addressing pediatric and adolescent obesity, but their effectiveness is restricted by time constraints, competing clinical demands, and the lack of effective electronic health record (EHR) tools. EHR tools are rarely developed with provider input.

 Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge.

 Electronic health record (EHR) user interface event logs are fast providing another perspective on the value and efficiency EHR technology brings to health care. Analysis of these detailed usage data has demonstrated their potential to identify EHR and clinical process design factors related to user efficiency, satisfaction, and burnout.

 Large language models (LLMs) like Generative pre-trained transformer (ChatGPT) are powerful algorithms that have been shown to produce human-like text from input data. Several potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed health care providers for their opinions about whether the technology is fit for use.

 Our objective was to evaluate the usability of an automated clinical decision support (CDS) tool previously implemented in the pediatric intensive care unit (PICU) to promote shared situation awareness among the medical team to prevent serious safety events within children's hospitals.

 Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience.

 Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training.

 Antimicrobial dosing in critically ill patients is challenging and model-informed precision dosing (MIPD) software may be used to optimize dosing in these patients. However, few intensive care units (ICU) currently adopt MIPD software use.

 Nurses are at the frontline of detecting patient deterioration. We developed Communicating Narrative Concerns Entered by Registered Nurses (CONCERN), an early warning system for clinical deterioration that generates a risk prediction score utilizing nursing data. CONCERN was implemented as a randomized clinical trial at two health systems in the Northeastern United States. Following the implementation of CONCERN, our team sought to develop the CONCERN Implementation Toolkit to enable other hospital [...]

 We conducted a focus group to assess the attitudes of primary care physicians (PCPs) toward prostate-specific antigen (PSA)-screening algorithms, perceptions of using decision support tools, and features that would make such tools feasible to implement.