Health-related virtual reality (VR) applications for patient treatment, rehabilitation, and medical professional training are on the rise. However, there is little guidance on how to select and perform usability evaluations for VR health interventions compared to the supports that exist for other digital health technologies. The purpose of this viewpoint paper is to present an introductory summary of various usability testing approaches or methods that can be used for VR applications. Along with an overview of each, a list of resources is provided for readers to obtain additionally relevant information. Six categories of VR usability evaluations are described using a previously developed classification taxonomy specific to VR environments: (1) cognitive or task walkthrough, (2) graphical evaluation, (3) post hoc questionnaires or interviews, (4) physical performance evaluation, (5) user interface evaluation, and (6) heuristic evaluation. Given the growth of VR in health care, rigorous evaluation and usability testing is crucial in the development and implementation of novel VR interventions. The approaches outlined in this paper provide a starting point for conducting usability assessments for health-related VR applications; however, there is a need to also move beyond these to adopt those from the gaming industry, where assessments for both usability and user experience are routinely conducted.

Background Characteristics of critically ill adults with coronavirus disease 2019 (COVID-19) in an academic safety net hospital and the effect of evidence-based practices in these patients are unknown. Research Question What are the outcomes of critically ill adults with COVID-19 admitted to a network of hospitals in New Orleans, LA and is an evidence-based protocol for care associated with improved outcomes? Study Design and Methods: In this multi-center, retrospective, observational cohort study of intensive care units in four hospitals in New Orleans, LA, we collected data on adults admitted to an intensive care unit (ICU) and tested for SARS-CoV-2 between March 9, 2020 and April 14, 2020. The exposure of interest was admission to an ICU which implemented an evidence-based protocol for COVID-19 care. The primary outcome was ventilator-free days. Results The initial 147 patients admitted to any ICU and tested positive for SARS-CoV-2 comprised the cohort for this study. In the entire network, exposure to an evidence-based protocol was associated with more ventilator-free days (25 days, 0 – 28) compared with non-protocolized ICUs (0 days, 0 – 23, p = 0.005), including in adjusted analyses (p = 0.02). Twenty patients (37%) admitted to protocolized ICUs died compared with 51 (56%, p = 0.02) in non-protocolized ICUs. Among 82 patients admitted to the academic safety net hospital’s ICUs, the median number of ventilator-free days was 22 (IQR 0 – 27) and mortality rate was 39%. Interpretation Care of critically ill COVID-19 patients with an evidence-based protocol is associated with increased time alive and free of invasive mechanical ventilation. In-hospital survival occurred in the majority of critically ill adults with COVID-19 admitted to an academic safety net hospital’s ICUs despite a high rate of co-morbidities.

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BackgroundAlthough electronic medication administration record systems have been implemented in settings where nurses work, nursing students commonly lack robust learning opportunities to practice the skills and workflow of digitalized medication administration during their formative education. As a result, nursing students’ performance in administering medication facilitated by technology is often poor. Serious gaming has been recommended as a possible intervention to improve nursing students’ performance with electronic medication administration in nursing education.ObjectiveThe objectives of this study are to examine whether the use of a gamified electronic medication administration simulator (1) improves nursing students’ attention to medication administration safety within simulated practice, (2) increases student self-efficacy and knowledge of the medication administration process, and (3) improves motivational and cognitive processing attributes related to student learning in a technology-enabled environment.MethodsThis study comprised the development of a gamified electronic medication administration record simulator and its evaluation in 2 phases. Phase 1 consists of a prospective, pragmatic randomized controlled trial with second-year baccalaureate nursing students at a Canadian university. Phase 2 consists of qualitative focus group interviews with a cross-section of nursing student participants.ResultsThe gamified medication administration simulator has been developed, and data collection is currently under way.ConclusionsIf the gamified electronic medication administration simulator is found to be effective, it could be used to support other health professional simulated education and scaled more widely in nursing education programs.Trial RegistrationClinicalTrials.gov NCT03219151; https://clinicaltrials.gov/show/NCT03219151 (Archived by WebCite at http://www.webcitation.org/6yjBROoDt)Registered Report IdentifierRR1-10.2196/9601

Medication errors continue to be a significant issue, posing substantial threats to the safety and well-being of patients. Through Bandura’s theory of self-efficacy, nursing students’ self-efficacy (confidence) related to medication administration was examined to investigate its influence on the generation of medication errors with the use of an Electronic Medication Administration Record (eMAR) in clinical simulation. This study examined the generation of medication errors and the differences that may exist based on nursing students’ perceived confidence. The findings of this study demonstrated that nursing students continue to generate medication errors within clinical simulation. No differences in the generation of medication errors were found between nursing students with perceived high levels of confidence and those with perceived low levels of confidence (one exception noted). Further examination of the variables and contextual factors related to safe medication administration practices is required to inform nursing education and practice.

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The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML based tool for predicting adverse outcomes based on EHR data to optimize clinical utility under a given cost structure. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Healthcare in northwest Ohio and southeastern Michigan. Methods: We tested performance of various ML approaches for predicting either increasing ventilatory support or mortality and the set of model features under a budget constraint was optimized via exhaustive search across all combinations of features. Results: The optimal sets of features for predicting ventilation under any budget constraint included demographics and comorbidities (DCM), basic metabolic panel (BMP), D-dimer, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), CRP, brain natriuretic peptide (BNP), and procalcitonin and for mortality included DCM, BMP, complete blood count, D-dimer, LDH, CRP, BNP, procalcitonin and ferritin. Conclusions: This study presents a quick, accurate and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.