BackgroundClinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall.ObjectiveThe purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance.MethodsA critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician).ResultsFavorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs.ConclusionsThis research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients.

To characterize the variability in usability and safety of EHRs from two vendors across four healthcare systems (2 Epic and 2 Cerner). Twelve to 15 emergency medicine physicians participated from each site and completed six clinical scenarios. Keystroke, mouse click, and video data were collected. From the six scenarios, two diagnostic imaging, laboratory, and medication tasks were analyzed. There was wide variability in task completion time, clicks, and error rates. For certain tasks, there were an average of a nine-fold difference in time and eight-fold difference in clicks. Error rates varied by task (X-ray 16.7% to 25%, MRI: 0 to 10%, Lactate: 0% to 14.3%, Tylenol: 0 to 30%; Taper: 16.7% to 50%). The variability in time, clicks, and error rates highlights the need for improved implementation optimization. EHR implementation, in addition to vendor design and development, is critical to usable and safe products.

BackgroundIntensive Care Units (ICUs) in the United States admit more than 5.7 million people each year. The ICU level of care helps people with life-threatening illness or injuries and involves close, constant attention by a team of specially-trained health care providers. Delay between condition onset and implementation of necessary interventions can dramatically impact the prognosis of patients with life-threatening diagnoses. Evidence supports a connection between information overload and medical errors. A tool that improves display and retrieval of key clinical information has great potential to benefit patient outcomes. The purpose of this review is to synthesize research on the use of visualization dashboards in health care.ObjectiveThe purpose of conducting this literature review is to synthesize previous research on the use of dashboards visualizing electronic health record information for health care providers. A review of the existing literature on this subject can be used to identify gaps in prior research and to inform further research efforts on this topic. Ultimately, this evidence can be used to guide the development, testing, and implementation of a new solution to optimize the visualization of clinical information, reduce clinician cognitive overload, and improve patient outcomes.MethodsArticles were included if they addressed the development, testing, implementation, or use of a visualization dashboard solution in a health care setting. An initial search was conducted of literature on dashboards only in the intensive care unit setting, but there were not many articles found that met the inclusion criteria. A secondary follow-up search was conducted to broaden the results to any health care setting. The initial and follow-up searches returned a total of 17 articles that were analyzed for this literature review.ResultsVisualization dashboard solutions decrease time spent on data gathering, difficulty of data gathering process, cognitive load, time to task completion, errors, and improve situation awareness, compliance with evidence-based safety guidelines, usability, and navigation.ConclusionsResearchers can build on the findings, strengths, and limitations of the work identified in this literature review to bolster development, testing, and implementation of novel visualization dashboard solutions. Due to the relatively few studies conducted in this area, there is plenty of room for researchers to test their solutions and add significantly to the field of knowledge on this subject.

Background The coronavirus disease (COVID-19) pandemic is rapidly spreading across the world. As of March 26, 2020, there are more than 500,000 cases and more than 25,000 deaths related to COVID-19, and the numbers are increasing by the hour. Objective The aim of this study was to explore the trends in confirmed COVID-19 cases in North Carolina, and to understand patterns in virtual visits related to symptoms of COVID-19. Methods We conducted a cohort study of confirmed COVID-19 cases and patients using an on-demand, statewide virtual urgent care center. We collected data from February 1, 2020, to March 15, 2020. Institutional Review Board exemption was obtained prior to the study. Results As of March, 18 2020, there were 92 confirmed COVID-19 cases and 733 total virtual visits. Of the total visits, 257 (35.1%) were related to COVID-19-like symptoms. Of the COVID-19-like visits, the number of females was 178 (69.2%). People in the age groups of 30-39 years (n=67, 26.1%) and 40-49 years (n=64, 24.9%) were half of the total patients. Additionally, approximately 96.9% (n=249) of the COVID-like encounters came from within the state of North Carolina. Our study shows that virtual care can provide efficient triaging in the counties with the highest number of COVID-19 cases. We also confirmed that the largest spread of the disease occurs in areas with a high population density as well as in areas with major airports. Conclusions The use of virtual care presents promising potential in the fight against COVID-19. Virtual care is capable of reducing emergency room visits, conserving health care resources, and avoiding the spread of COVID-19 by treating patients remotely. We call for further adoption of virtual care by health systems across the United States and the world during the COVID-19 pandemic.

Introduction : The inability to achieve high COVID-19 vaccination rates can continue to have serious harm to our communities. Vaccine hesitancy is a major barrier towards high vaccination rates. We evaluated the relationship between COVID-19 vaccine uptake and vaccine hesitancy, and then examined whether community factors were associated with COVID-19 vaccine uptake and hesitancy. Methods : We constructed and evaluated a cross-sectional, county-level dataset that included the levels of vaccination uptake and vaccine hesitancy, and population characteristics based on those included in the CDC's Social Vulnerability Index. Results : Across 3142 US counties, vaccine hesitancy was significantly and negatively correlated with vaccine uptake rates(r=-0.06, p-value<0.01). The two predictors associated with a low vaccination level within highly hesitant communities were: no high school education(OR:0.70, p-value<0.001), and concern on vaccine availability and distribution (CVAC) (OR:0.00, p-value<0.001). The most common reason driving vaccine hesitancy was lack of trust in COVID-19 vaccines(55%), followed by concerns around side effects of the vaccine(48%), and lack of trust in government(46%). Conclusion : COVID-19 vaccine hesitancy is a public health threat. Our findings suggests that low education levels are a major contributor to vaccine hesitancy and ultimately vaccination levels. Since education levels are not easily modifiable, our results suggest that policymakers would be best served by closing knowledge gaps to overcome negative perceptions of the vaccine through tailored interventions.