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Children with a difficult airway are at high risk of decompensation in the setting of respiratory distress. Situational awareness among all team members, and a shared plan in case of an emergency, can reduce the chance of catastrophic outcomes.This study aimed to improve difficult airway situational awareness while minimizing alert burden in a quaternary care pediatric healthcare system through the application of clinical decision support (CDS).Three iterative designs were developed [...]

This study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-hospital environments. Metabolic syndrome (MetS) is susceptible to misdiagnosis by DL models due to distribution shifts. This work demonstrates the potential of continual learning (CL) to enhance model performance in MetS identification across diverse settings.

The CONCERN Early Warning System (CONCERN EWS) is an artificial intelligence-based clinical decision support system (AI-CDSS) for the prediction of clinical deterioration, leveraging signals from nursing documentation patterns. While a recent multisite randomized controlled trial (RCT) demonstrated its effectiveness in reducing inpatient mortality and length of stay, evaluating implementation outcomes is essential to ensure equitable results across patient populations.This study aims to (1) assess whether clinicians' usage of the CONCERN [...]

The digitalization of health records stands to improve decision-making at clinical, administrative, and policy level. Efforts follow various paths and are closely intertwined with health system and organizational configurations. Problems persist in both uptake and use. This study explores the digitalization trajectories of academic health centers (AHCs) to understand tensions between organizational and government strategies and their impact on digital development.

To examine the effects of message framing and visualization framing on the comprehension of personal health records and subsequent self-care behavioral intention among young and middle-older aged adults.

To conduct a scoping review (ScR) of existing approaches for synthetic Electronic Health Records (EHR) data generation, to benchmark major methods, and to provide an open-source software and offer recommendations for practitioners.

Unlocking clinical information embedded in clinical notes has been hindered to a significant degree by domain-specific and context-sensitive language. Identification of note sections and structural document elements has been shown to improve information extraction and dependent downstream clinical natural language processing (NLP) tasks and applications. This study investigates the viability of a dynamic example selection prompting method to section classification using lightweight, open-source large language models (LLMs) as a practical [...]

This study aims to review the effectiveness of electronic patient-reported outcome measures (ePROMs) to triage and schedule appointments for adult patients with chronic medical conditions.

Health-care institutions customize electronic health record (EHR) configurations to reflect their unique workflows and patient care priorities. Ensuring EHR alignment across sites facilitates seamless information exchange. We developed a pipeline for EHR flowsheet alignment between health-care organizations. The pipeline is augmented by mapping flowsheet data fields to concepts in the Clinical Care Classification (CCC) nursing terminology.