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Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers.

Hospitals are increasingly replacing pagers with clinical texting systems that allow users to use smartphones to send messages while maintaining compliance for privacy and security. As more institutions adopt such systems, the need to understand the impact of such transitions on team communication becomes ever more significant.

Pediatric residency programs are required by the Accreditation Council for Graduate Medical Education to provide residents with patient-care and quality metrics to facilitate self-identification of knowledge gaps to prioritize improvement efforts. Trainees are interested in receiving this data, but this is a largely unmet need. Our objectives were to (1) design and implement an automated dashboard providing individualized data to residents, and (2) examine the usability and acceptability of the [...]

Clinical decision support systems (CDSSs) use alerts to enhance medication safety and reduce medication error rates. A major challenge of medication alerts is their low acceptance rate, limiting their potential benefit. A structured overview about modulators influencing alert acceptance is lacking. Therefore, we aimed to review and compile qualitative and quantitative modulators of alert acceptance and organize them in a comprehensive model.

BaMaRa allows the secure collection and deidentified centralization of medical data from all patients followed-up in a rare disease expert network in France, based on a minimum data set (SDM-MR). The present article describes BaMaRa information system implementation and development across the whole national territory as well as data access requests through BNDMR, the data warehouse which centralizes all BaMaRa data, during the 2015-2020 period.

The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes.

Child abuse and neglect are public health issues impacting communities throughout the United States. The broad adoption of electronic health records (EHR) in health care supports the development of machine learning-based models to help identify child abuse and neglect. Employing EHR data for child abuse and neglect detection raises several critical ethical considerations. This article applied a phenomenological approach to discuss and provide recommendations for key ethical issues related to [...]

The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect.