OBJECTIVE: Risk prediction models are used in hospitals to identify pediatric patients at risk of clinical deterioration, enabling timely interventions and rescue. The objective of this study was to develop a new explainer algorithm that uses a patient's clinical notes to generate text-based explanations for risk prediction alerts. MATERIALS AND METHODS: We conducted a retrospective study of 39 406 patient admissions to the American Family Children's Hospital at the University of Wisconsin-Madison (2009-2020). The pediatric Calculated Assessment of Risk and Triage (pCART) validated risk prediction model was used to identify children at risk for deterioration. A transformer model was trained to use clinical notes from the 12-hour period preceding each pCART score to predict whether a patient was flagged as at risk. Then, label-aware attention highlighted text phrases most important to an at-risk alert. The study cohort was randomly split into derivation (60%) and validation (20%) data, and a separate test (20%) was used to evaluate the explainer's performance. RESULTS: Our pCART Explainer algorithm performed well in discriminating at-risk pCART alert vs no alert (c-statistic 0.805). Sample explanations from pCART Explainer revealed clinically important phrases such as "rapid breathing," "fall risk," "distension," and "grunting," thereby demonstrating excellent face validity. DISCUSSION: The pCART Explainer could quickly orient clinicians to the patient's condition by drawing attention to key phrases in notes, potentially enhancing situational awareness and guiding decision-making. CONCLUSION: We developed pCART Explainer, a novel algorithm that highlights text within clinical notes to provide medically relevant context about deterioration alerts, thereby improving the explainability of the pCART model.

Endotracheal Intubation is a lifesaving procedure in which a tube is passed through the mouth into the trachea (windpipe) to maintain an open airway and facilitate artificial respiration. It is a complex psychomotor skill, which requires significant training and experience to prevent complications. The current methods of training, including manikins and cadaver, have limitations in terms of their availability for early medical professionals to learn and practice. These training options also have limitations in terms of presenting high risk/ difficult intubation cases for experts to mentally plan their approach in high-risk scenarios prior to the procedure. In this paper, we present the design considerations and challenges of AirwayVR: virtual reality-based simulation trainer for intubation training for two different target audience (medical professionals) with two different objectives. The first one is to use AirwayVR as an introductory platform to learn and practice intubation in virtual reality for novice learners (Medical students and residents). The second objective is to utilize this technology as a Just-in-time training platform for experts to mentally prepare for a complex case prior to the procedure.