Dataset Information

Development and Usability Testing of a Web-based COVID-19 Self-triage Platform.

ABSTRACT: INTRODUCTION:The development and deployment of a web-based, self-triage tool for severe respiratory syndrome coronavirus 2 (COVID-19 disease) aimed at preventing surges in healthcare utilization could provide easily understandable health guidance with the goal of mitigating unnecessary emergency department (ED) and healthcare visits. We describe the iterative development and usability testing of such a tool. We hypothesized that adult users could understand and recall the recommendations provided by a COVID-19 web-based, self-triage tool. METHODS:We convened a multidisciplinary panel of medical experts at two academic medical schools in an iterative redesign process of a previously validated web-based, epidemic screening tool for the current COVID-19 pandemic. We then conducted a cross-sectional usability study over a 24-hour period among faculty, staff, and students at the two participating universities. Participants were randomly assigned a pre-written health script to enter into the self-triage website for testing. The primary outcome was immediate recall of website recommendations. Secondary outcomes included usability measures. We stratified outcomes by demographic characteristics. RESULTS:A final sample of 877 participants (mean age, 32 years [range, 19-84 years]; 65.3% female) was used in the analysis. We found that 79.4% of the participants accurately recalled the recommendations provided by the website. Almost all participants (96.9%) found the website easy to use and navigate. CONCLUSION:Adult users of a COVID-19 self-triage website, recruited from an academic setting, were able to successfully recall self-care instructions from the website and found it user-friendly. This website appears to be a feasible way to provide evidence-based health guidance to adult patients during a pandemic. Website guidance could be used to reduce unnecessary ED and healthcare visits.

SUBMITTER: Schrager JD

PROVIDER: S-EPMC7514387 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Development and Usability Testing of a Web-based COVID-19 Self-triage Platform.

Schrager Justin D JD Schuler Keke K Isakov Alexander P AP Wright David W DW Yaffee Anna Q AQ Jacobson Kara L KL Parker Ruth M RM Goolsby Craig C

The western journal of emergency medicine 20200819 5

<h4>Introduction</h4>The development and deployment of a web-based, self-triage tool for severe respiratory syndrome coronavirus 2 (COVID-19 disease) aimed at preventing surges in healthcare utilization could provide easily understandable health guidance with the goal of mitigating unnecessary emergency department (ED) and healthcare visits. We describe the iterative development and usability testing of such a tool. We hypothesized that adult users could understand and recall the recommendations ...[more]

PMID: 32970554

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Project description:BACKGROUND:Web-based decision aids are increasingly important in medical research and clinical care. However, few have been studied in an intensive care unit setting. The objectives of this study were to develop a Web-based decision aid for family members of patients receiving prolonged mechanical ventilation and to evaluate its usability and acceptability. METHODS:Using an iterative process involving 48 critical illness survivors, family surrogate decision makers, and intensivists, we developed a Web-based decision aid addressing goals of care preferences for surrogate decision makers of patients with prolonged mechanical ventilation that could be either administered by study staff or completed independently by family members (Development Phase). After piloting the decision aid among 13 surrogate decision makers and seven intensivists, we assessed the decision aid's usability in the Evaluation Phase among a cohort of 30 surrogate decision makers using the Systems Usability Scale (SUS). Acceptability was assessed using measures of satisfaction and preference for electronic Collaborative Decision Support (eCODES) versus the original printed decision aid. RESULTS:The final decision aid, termed 'electronic Collaborative Decision Support', provides a framework for shared decision making, elicits relevant values and preferences, incorporates clinical data to personalize prognostic estimates generated from the ProVent prediction model, generates a printable document summarizing the user's interaction with the decision aid, and can digitally archive each user session. Usability was excellent (mean SUS, 80 ± 10) overall, but lower among those 56 years and older (73 ± 7) versus those who were younger (84 ± 9); p = 0.03. A total of 93% of users reported a preference for electronic versus printed versions. CONCLUSIONS:The Web-based decision aid for ICU surrogate decision makers can facilitate highly individualized information sharing with excellent usability and acceptability. Decision aids that employ an electronic format such as eCODES represent a strategy that could enhance patient-clinician collaboration and decision making quality in intensive care.

Project description:Background:We developed a self-triage web application for COVID-19 symptoms, which was launched in France in March 2020, when French health authorities recommended all patients with suspected COVID-19 call an emergency phone number.Objective:Our objective was to determine if a self-triage tool could reduce the burden on emergency call centers and help predict increasing burden on hospitals.Methods:Users were asked questions about their underlying conditions, sociodemographic status, postal code, and main COVID-19 symptoms. Participants were advised to call an emergency call center if they reported dyspnea or complete loss of appetite for over 24 hours. Data on COVID-19-related calls were collected from 6 emergency call centers and data on COVID-19 hospitalizations were collected from Santé Publique France and the French Ministry of Health. We examined the change in the number of emergency calls before and after the launch of the web application.Results:From March 17 to April 2, 2020, 735,419 questionnaires were registered in the study area. Of these, 121,370 (16.5%) led to a recommendation to call an emergency center. The peak number of overall questionnaires and of questionnaires leading to a recommendation to call an emergency center were observed on March 22, 2020. In the 17 days preceding the launch of the web application, emergency call centers in the study area registered 66,925 COVID-19-related calls and local hospitals admitted 639 patients for COVID-19; the ratio of emergency calls to hospitalizations for COVID-19 was 104.7 to 1. In the 17 days following the launch of the web application, there were 82,347 emergency calls and 6009 new hospitalizations for COVID-19, a ratio of 13.7 calls to 1 hospitalization (chi-square test: P<.001).Conclusions:The self-triage web application launch was followed by a nearly 10-fold increase in COVID-19-related hospitalizations with only a 23% increase in emergency calls. The peak of questionnaire completions preceded the peak of COVID-19-related hospitalizations by 5 days. Although the design of this study does not allow us to conclude that the self-triage tool alone contributed to the alleviation of calls to the emergency call centers, it does suggest that it played a role, and may be used for predicting increasing burden on hospitals.Trial registration:ClinicalTrials.gov NCT04331171; https://clinicaltrials.gov/ct2/show/NCT04331171.

Project description:BackgroundSupporting patients to self-manage their chronic kidney disease (CKD) has been identified as a research priority by patients with CKD and those who care for them. Self-management has been shown to slow CKD progression and improve the quality of life of individuals living with the disease. Previous work has identified a need for a person-centered, theory-informed, web-based tool for CKD self-management that can be individualized to a patient's unique situation, priorities, and preferences. We addressed this gap using an integrated knowledge translation method and patient engagement principles.ObjectiveThe aim of this study is to conduct systematic co-design and usability testing of a web-based self-management prototype for adults with CKD (nondialysis and nontransplant) and their caregivers to enhance self-management support.MethodsA multistep, iterative system development cycle was used to co-design and test the My Kidneys My Health prototype. The 3-step process included creating website features and content using 2 sequential focus groups with patients with CKD and caregivers, heuristic testing using the 10 heuristic principles by Nielsen, and usability testing through in-person 60-minute interviews with patients with CKD and their caregivers. Patients with CKD, caregivers, clinicians, researchers, software developers, graphic designers, and policy makers were involved in all steps of this study.ResultsIn step 1, 18 participants (14 patients and 4 caregivers) attended one of the 2 sequential focus groups. The participants provided specific suggestions for simplifying navigation as well as suggestions to incorporate video, text, audio, interactive components, and visuals to convey information. A total of 5 reviewers completed the heuristic analysis (step 2), identifying items mainly related to navigation and functionality. Furthermore, 5 participants completed usability testing (step 3) and provided feedback on video production, navigation, features and functionality, and branding. Participants reported visiting the website repeatedly for the following features: personalized food tool, my health care provider question list, symptom guidance based on CKD severity, and medication advice. Usability was high, with a mean system usability score of 90 out of 100.ConclusionsThe My Kidneys My Health prototype is a systematically developed, multifaceted, web-based CKD self-management support tool guided by the theory and preferences of patients with CKD and their caregivers. The website is user friendly and provides features that improve user experience by tailoring the content and resources to their needs. A feasibility study will provide insights into the acceptability of and engagement with the prototype and identify preliminary patient-reported outcomes (eg, self-efficacy) as well as potential factors related to implementation. This work is relevant given the shift to virtual care during the current pandemic times and provides patients with support when in-person care is restricted.

Project description:BACKGROUND:Workplace bullying has been measured in many studies to investigate its effects on mental health issues. However, none have used web-based computerized adaptive testing (CAT) with bully classifications and convolutional neural networks (CNN) for reporting the extent of individual bullying in the workplace. OBJECTIVE:This study aims to build a model using CNN to develop an app for automatic detection and classification of nurse bullying-levels, incorporated with online Rasch computerized adaptive testing, to help assess nurse bullying at an earlier stage. METHODS:We recruited 960 nurses working in a Taiwan Ch-Mei hospital group to fill out the 22-item Negative Acts Questionnaire-Revised (NAQ-R) in August 2012. The k-mean and the CNN were used as unsupervised and supervised learnings, respectively, for: (1) dividing nurses into three classes (n=918, 29, and 13 with suspicious mild, moderate, and severe extent of being bullied, respectively); and (2) building a bully prediction model to estimate 69 different parameters. Finally, data were separated into training and testing sets in a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve [AUC]), along with the accuracy across studies for comparison. An app predicting the respondent bullying-level was developed, involving the model's 69 estimated parameters and the online Rasch CAT module as a website assessment. RESULTS:We observed that: (1) the 22-item model yields higher accuracy rates for three categories, with an accuracy of 94% for the total 960 cases, and accuracies of 99% (AUC 0.99; 95% CI 0.99-1.00) and 83% (AUC 0.94; 95% CI 0.82-0.99) for the lower and upper groups (cutoff points at 49 and 66 points) based on the 947 cases and 42 cases, respectively; and (2) the 700-case training set, with 95% accuracy, predicts the 260-case testing set reaching an accuracy of 97. Thus, a NAQ-R app for nurses that predicts bullying-level was successfully developed and demonstrated in this study. CONCLUSIONS:The 22-item CNN model, combined with the Rasch online CAT, is recommended for improving the accuracy of the nurse NAQ-R assessment. An app developed for helping nurses self-assess workplace bullying at an early stage is required for application in the future.

Dataset Information

Development and Usability Testing of a Web-based COVID-19 Self-triage Platform.

Publications

Development and Usability Testing of a Web-based COVID-19 Self-triage Platform.

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