Incorporating Augmented Reality Patients Into Online Trauma Training to Support Mental Model Development: An Experimental Study.

Introduction: Medical students find translating lessons from traditional lectures to caring for trauma patients challenging. We assess whether adding video-based virtual flashcards and videos of augmented reality-based trauma patients in an online learning environment improves performance.

Methods: We performed a between-subject experimental study.

Strategies for case-based training with virtual patients: An experimental study of the impact of integrating mental model articulation and self-reflection.

Resilient system performance in high-stakes settings, which includes the ability to monitor, respond, anticipate, and learn, can be enhanced for trainees through simulation of realistic scenarios enhanced by augmented reality. Active learning strategies can enhance simulation-based training, particularly the mental model articulation principle where students are prompted to anticipate what will happen next and the reflection principle where students self-assess their performance compared to a gold standard expert model. In this paper, we compared simulation-based training for trauma care with and without active learning strategies during pauses in the simulated action for progressively deteriorating patients.

Perceptually Discriminating the Highest Priority Alarms Reduces Response Time: A Retrospective Pre-Post Study at Four Hospitals.

Objective: Reduce nurse response time for emergency and high-priority alarms by increasing discriminability between emergency and all other alarms and suppressing redundant and likely false high-priority alarms in a secondary alarm notification system (SANS).

Background: Emergency alarms are the most urgent, requiring immediate action to address a dangerous situation. They are clinician-triggered and have higher positive predictive value (PPV).

Predicting mortality with applied machine learning: Can we get there?

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics.

Method to allocate voting resources with unequal ballots and/or education.

Apportionment in election systems refers to determination of the number of voting resources (poll books, poll workers, or voting machines) needed to ensure that all voters can expect to wait no longer than an appropriate amount, even the voter who waits the longest. Apportionment is a common problem for election officials and legislatures. A related problem is "allocation," which relates to the deployment of an existing number of resources so that the longest expected wait is held to an appropritate amount.

Patient-Centered Handovers: Ethnographic Observations of Attending and Resident Physicians: Ethnographic Observations of Attending and Resident Physicians.

Handover communication improvement initiatives typically employ a "one size fits all" approach. A human factors perspective has the potential to guide how to tailor interventions to roles, levels of experience, settings, and types of patients. We conducted ethnographic observations of sign-outs by attending and resident physicians in 2 medical intensive care units at one institution.

Mitigating bias in planning two-colour microarray experiments.

Two-colour microarrays are used to study differential gene expression on a large scale. Experimental planning can help reduce the chances of wrong inferences about whether genes are differentially expressed. Previous research on this problem has focused on minimising estimation errors (according to variance-based criteria such as A-optimality) on the basis of optimistic assumptions about the system studied.