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. The training was conducted online and real-time without a facilitator, with 42 medical students viewing training materials and then immediately taking an online quiz for three types of trauma cases: hemorrhage, airway obstruction, and tension pneumothorax. Participants were randomly assigned to either the experimental or control condition in a between-subjects design. We compared performance in the control and experimental conditions based on: A) the proportion of cues correctly recognized, B) the proportion of accurate diagnoses, C) the proportion of appropriate treatment interventions, and D) verbal briefing quality on a 1-5 scale. We found that the training intervention increased recognition of subtle cues critical for accurate diagnosis and appropriate treatment interventions; the training did not improve the accuracy of diagnoses or the quality of the verbal briefing. We conclude that incorporating active learning strategies in simulation-based training improved foundational capabilities in detecting subtle cues and intervening to rescue deteriorating patients that can increase the readiness for trainees to contribute to resilient system performance in the high-stakes setting of emergency care in hospitals.

Heat stress in the Caribbean: Climatology, drivers, and trends of human biometeorology indices.

Forty years (1980-2019) of reanalysis data were used to investigate climatology and trends of heat stress in the Caribbean region. Represented via the Universal Thermal Climate Index (UTCI), a multivariate thermophysiological-relevant parameter, the highest heat stress is found to be most frequent and geographically widespread during the rainy season (August, September, and October). UTCI trends indicate an increase of more than 0.2°C·decade-1, with southern Florida and the Lesser Antilles witnessing the greatest upward rates (0.45°C·decade-1). Correlations with climate variables known to induce heat stress reveal that the increase in heat stress is driven by increases in air temperature and radiation, and decreases in wind speed. Conditions of heat danger, as depicted by the heat index (HI), have intensified since 1980 (+1.2°C) and are found to occur simultaneously to conditions of heat stress suggesting a synergy between heat illnesses and physiological responses to heat. This work also includes the analysis of the record-breaking 2020 heat season during which the UTCI and HI achieved above average values, indicating that local populations most likely experienced heat stress and danger higher than the ones they are used to. These findings confirm the gradual intensification of heat stress in the Caribbean and aim to provide a guidance for heat-related policies in the region.

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). High-priority alarms are automatically triggered and have lower PPV. METHOD: We performed a retrospective pre-post study, analyzing data 15 months before and 25 months after a SANS redesign was implemented in four hospitals. For emergency alarms, we incorporated digitized human speech to distinguish them from automatically triggered alarms, leaving their onset and escalation pathways unchanged. For automatically triggered alarms, we suppressed some by delaying initial onset and escalation by 20 s. We used linear mixed models to assess the change in response time, Fisher's exact test for the proportion of response times longer than 120 s, and control charts for process stability. RESULTS: Response time for emergency alarms decreased at all hospitals (main, from 26.91 s to 22.32 s, p < .001; cardiac, from 127.10 s to 52.43 s, p < .001; cancer, from 18.03 s to 15.39 s, p < .001). Improvements were sustained. Automatically triggered alarms decreased 25.0%. Response time for the three automatically triggered cardiac alarms increased at the four hospitals. CONCLUSION: Auditory sound disambiguation was associated with a sustained reduced nurse response time for emergency alarms, but suppressing some high-priority automatically triggered alarms was not. APPLICATION: Distinguishing and escalating urgent, actionable alarms with higher PPV improves response time.

EEG Connectivity in ADHD Compared to a Normative Database: A Cohort Analysis of 120 Subjects from the ICAN Study.

This study explores how EEG connectivity measures in children with ADHD ages 7-10 (n = 140) differ from an age-matched nonclinical database. We differentiated connectivity in networks, Brodmann area pairs, and frequencies. Subjects were in the International Collaborative ADHD Neurofeedback study, which explored neurofeedback for ADHD. Inclusion criteria were mainly rigorously diagnosed ADHD and a theta/beta power ratio (TBR) ≤ 4.5. Using statistical and machine learning algorithms, connectivity values were extracted in coherence, phase, and lag coherence at all Brodmann, subcortical, and cerebellar areas within the main networks in all EEG frequencies and then compared with a normative database. There is a higher rate of dysregulation (more than ± 1.97SD), in some cases as much as 75%, of the Brodmann pairs observed in coherence and phase between BAs 7, 10, and 11 with secondary connections from these areas to BAs 21, 30, 35, 37, 39, and 40 in the ADHD children as compared to the normative database. Left and right Brodmann areas 10 and 11 are highly disconnected to each other. The most dysregulated Brodmann Areas in ADHD are 7, 10, and 11, relevant to ADHD executive-function deficits and provide important considerations when developing interventions for ADHD children.

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. Provisioning and allocation are difficult because the numbers of expected voters, the ballot lengths, and the education levels of voters may all differ significantly from precinct-to-precinct in a county. Consider that predicting the waiting time of the voter who waits the longest generally requires discrete event simulation.•The methods here rigorously guarantee that all voters expect to wait a prescribed time with a bounded probability, e.g., everyone expects to wait less than thirty minutes with probability greater than 95%.•The methods here can handle both a single type of resource (e.g., voting machines or scan machines) and multiple resource types (e.g., voting machines and poll books).•The methods are provided in a freely available, easy-to-use Excel software program.

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. In this paper, we debate opposing positions regarding whether these algorithms are 'ready yet' for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.

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. Digitally audiotaped data were manually analyzed for content using codes and time spent using box plots for emergent categories. A total of 34 attending and 58 resident physician handovers were observed. Resident physicians spent more time for "soon to be discharged" and "higher concern" patients than attending physicians. Resident physicians spent less time discussing patients which they had provided care for within the last 3 days ("handbacks"). The study suggested differences for how handovers were conducted for attending and resident physicians for 3 categories of patients; handovers differ on the basis of role or level of expertise, patient type, and amount of prior knowledge of the patient. The findings have implications for new directions for subsequent research and for how to tailor quality improvement interventions based upon the role, level of experience, level of prior knowledge, and patient categories.

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. In this paper, we propose a novel planning criterion to evaluate existing plans for microarray experiments. The proposed criterion is 'Generalised-A Optimality' that is based on realistic assumptions that include bias errors. Using Generalised-A Optimality, the reference-design approach is likely to yield greater estimation accuracy in specific situations in which loop designs had previously seemed superior. However, hybrid designs are likely to offer higher estimation accuracy than reference, loop and interwoven designs having the same number of samples and slides. These findings are supported by data from both simulated and real microarray experiments.