Journal of medical Internet research
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J. Med. Internet Res. · Apr 2021
"Doc McStuffins: Doctor for a Day" Virtual Reality (DocVR) for Pediatric Preoperative Anxiety and Satisfaction: Pediatric Medical Technology Feasibility Study.
Preoperative anxiety is a common occurrence among children and is associated with a host of maladaptive postoperative behaviors. Consequently, increased attention has been placed on interventions to reduce preoperative anxiety and its associated outcomes. Child Life preparation prior to surgery includes evidence-based practices such as age-appropriate distraction and therapeutic play. Virtual reality (VR) is a promising addition to the Child Life toolbox to address anxiety prior to surgery. The current study evaluates the implementation and feasibility of a VR experience, "Doc McStuffins: Doctor for a Day Virtual Reality Experience" (DocVR), developed by Disney Junior in collaboration with Children's Hospital Los Angeles, to target pediatric preoperative anxiety. ⋯ DocVR is a feasible and beneficial VR experience to relieve pediatric preoperative anxiety and improve satisfaction in the preoperative area. The VR experience resulted in a decrease in overall anxiety and an increase in overall positive affect during the preoperative time. Patients also responded positively to the game, confirming their interest in the content and affirming the quality of the DocVR experience. The positive response to the game indicates that DocVR has the potential to make the overall preoperative experience less anxiety-producing and more comfortable, which leads to improved patient satisfaction. Naturally, improved patient outcomes lead to improved caregiver and health care provider satisfaction.
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J. Med. Internet Res. · Apr 2021
Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation.
The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery. ⋯ Our proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application so that anyone can access the model. We believe that sharing the AI model with the public will be helpful in validating and improving its performance.
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J. Med. Internet Res. · Apr 2021
Multicenter StudyPrediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study.
Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. ⋯ Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.
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J. Med. Internet Res. · Apr 2021
Evolving Epidemiological Characteristics of COVID-19 in Hong Kong From January to August 2020: Retrospective Study.
COVID-19 has plagued the globe, with multiple SARS-CoV-2 clusters hinting at its evolving epidemiology. Since the disease course is governed by important epidemiological parameters, including containment delays (time between symptom onset and mandatory isolation) and serial intervals (time between symptom onsets of infector-infectee pairs), understanding their temporal changes helps to guide interventions. ⋯ First, the factors associated with reduced containment delays suggested government-enacted interventions were useful for achieving outbreak control and should be further encouraged. Second, the shorter serial intervals associated with the composite mobility index calls for empirical surveys to disentangle the role of different contact dimensions in disease transmission. Third, the presymptomatic transmission and asymptomatic cases underscore the importance of remaining vigilant about COVID-19. Fourth, the time-varying epidemiological parameters suggest the need to incorporate their temporal variations when depicting the epidemic trajectory. Fifth, the high proportion of transmission events occurring within the same age group supports the ban on gatherings outside of households, and underscores the need for residence-centered preventive measures.
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J. Med. Internet Res. · Apr 2021
Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation.
Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models. ⋯ We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI.