SCOR: A secure international informatics infrastructure to investigate COVID-19.
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ABSTRACT:
SUBMITTER: Raisaro JL
PROVIDER: S-EPMC7454652 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesWe describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 challenges and needs required health systems to rapidly redesign the delivery of care.Materials and methodsOur health system deployed 4 COVID-19 telehealth programs and 4 biomedical informatics innovations to screen and care for COVID-19 patients. Using programmatic and electronic health record data, we describe the implementation and initial utilization.ResultsThrough collaboration across multidisciplinary teams and strategic planning, 4 telehealth program initiatives have been deployed in response to COVID-19: virtual urgent care screening, remote patient monitoring for COVID-19-positive patients, continuous virtual monitoring to reduce workforce risk and utilization of personal protective equipment, and the transition of outpatient care to telehealth. Biomedical informatics was integral to our institutional response in supporting clinical care through new and reconfigured technologies. Through linking the telehealth systems and the electronic health record, we have the ability to monitor and track patients through a continuum of COVID-19 services.DiscussionCOVID-19 has facilitated the rapid expansion and utilization of telehealth and health informatics services. We anticipate that patients and providers will view enhanced telehealth services as an essential aspect of the healthcare system. Continuation of telehealth payment models at the federal and private levels will be a key factor in whether this new uptake is sustained.ConclusionsThere are substantial benefits in utilizing telehealth during the COVID-19, including the ability to rapidly scale the number of patients being screened and providing continuity of care.
| S-EPMC7337763 | biostudies-literature
Project description:A robust biomedical informatics infrastructure is essential for academic health centers engaged in translational research. There are no templates for what such an infrastructure encompasses or how it is funded. An informatics workgroup within the Clinical and Translational Science Awards network conducted an analysis to identify the scope, governance, and funding of this infrastructure. After we identified the essential components of an informatics infrastructure, we surveyed informatics leaders at network institutions about the governance and sustainability of the different components. Results from 42 survey respondents showed significant variations in governance and sustainability; however, some trends also emerged. Core informatics components such as electronic data capture systems, electronic health records data repositories, and related tools had mixed models of funding including, fee-for-service, extramural grants, and institutional support. Several key components such as regulatory systems (e.g., electronic Institutional Review Board [IRB] systems, grants, and contracts), security systems, data warehouses, and clinical trials management systems were overwhelmingly supported as institutional infrastructure. The findings highlighted in this report are worth noting for academic health centers and funding agencies involved in planning current and future informatics infrastructure, which provides the foundation for a robust, data-driven clinical and translational research program.
| S-EPMC6390401 | biostudies-literature
Project description:This paper examines sectoral productivity shocks of the COVID-19 pandemic, their aggregate impact, and the possible compensatory effects of improving productivity in infrastructure-related sectors. We employ the KLEMS annual dataset for a group of OECD and Latin America and the Caribbean countries, complemented with high-frequency data for 2020. First, we estimate a panel vector autoregression of growth rates in sector level labor productivity to specify the nature and size of sectoral shocks using the historical data. We then run impulse-response simulations of one standard deviation shocks in the sectors that were most affected by COVID-19. We estimate that the pandemic cut economy-wide labor productivity by 4.9% in Latin America, and by 3.5% for the entire sample. Finally, by modeling the long-run relationship between productivity shocks in the sectors most affected by COVID-19, we find that large productivity improvements in infrastructure-equivalent to at least three times the historical rates of productivity gains-may be needed to fully compensate for the negative productivity losses traceable to COVID-19.Supplementary informationThe online version contains supplementary material available at 10.1007/s41885-021-00098-z.
| S-EPMC8777183 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:BackgroundThe global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak.MethodsA comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis.ResultsIn this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%).ConclusionThe present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.
| S-EPMC8949656 | biostudies-literature
Project description:The bicycle is a low-cost means of transport linked to low risk of transmission of infectious disease. During the COVID-19 crisis, governments have therefore incentivized cycling by provisionally redistributing street space. We evaluate the impact of this new bicycle infrastructure on cycling traffic using a generalized difference in differences design. We scrape daily bicycle counts from 736 bicycle counters in 106 European cities. We combine these with data on announced and completed pop-up bike lane road work projects. Within 4 mo, an average of 11.5 km of provisional pop-up bike lanes have been built per city and the policy has increased cycling between 11 and 48% on average. We calculate that the new infrastructure will generate between $1 and $7 billion in health benefits per year if cycling habits are sticky.
| S-EPMC8053938 | biostudies-literature
Project description:To develop and validate an informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness.Using open-schema data feeds imported from electronic medical records (EMRs), we developed a near-real-time relational database (Multidisciplinary Epidemiology and Translational Research in Intensive Care Data Mart). Imported data domains included physiologic monitoring, medication orders, laboratory and radiologic investigations, and physician and nursing notes. Open database connectivity supported the use of Boolean combinations of data that allowed authorized users to develop syndrome surveillance, decision support, and reporting (data "sniffers") routines. Random samples of database entries in each category were validated against corresponding independent manual reviews.The Multidisciplinary Epidemiology and Translational Research in Intensive Care Data Mart accommodates, on average, 15,000 admissions to the intensive care unit (ICU) per year and 200,000 vital records per day. Agreement between database entries and manual EMR audits was high for sex, mortality, and use of mechanical ventilation (kappa, 1.0 for all) and for age and laboratory and monitored data (Bland-Altman mean difference +/- SD, 1(0) for all). Agreement was lower for interpreted or calculated variables, such as specific syndrome diagnoses (kappa, 0.5 for acute lung injury), duration of ICU stay (mean difference +/- SD, 0.43+/-0.2), or duration of mechanical ventilation (mean difference +/- SD, 0.2+/-0.9).Extraction of essential ICU data from a hospital EMR into an open, integrative database facilitates process control, reporting, syndrome surveillance, decision support, and outcome research in the ICU.
| S-EPMC2843116 | biostudies-other
Project description:Background The Covid-19 pandemic has highlighted weaknesses in the National Health Service critical care provision including both capacity and infrastructure. Traditionally, healthcare workspaces have failed to fully incorporate Human-Centred Design principles resulting in environments that negatively affect the efficacy of task completion, patient safety and staff wellbeing. In the summer of 2020, we received funds for the urgent construction of a Covid-19 secure critical care facility. The aim of this project was to design a pandemic resilient facility centred around both staff and patient requirements and safety, within the available footprint. Methods We developed a simulation exercise, underpinned by Human-Centred Design principles, to evaluate intensive care designs through Build Mapping, Tasks Analysis and Qualitative data. Build Mapping involved taping out sections of the design and mocking up with equipment. Task Analysis and qualitative data were collected following task completion. Results 56 participants completed the build simulation exercise generating 141 design suggestions (69 task related, 56 patient and relative related, 16 staff related). Suggestions translated to 18 multilevel design improvements; five significant structural changes (Macro level) including wall moves and lift size change. Minor improvements were made at a Meso and Micro design level. Critical care design drivers identified included functional drivers (visibility, Covid-19 secure environment, workflow, and task efficiency) and behavioural drivers (learning and development, light, humanising intensive care and design consistency). Conclusion Success of clinical tasks, infection control, patient safety and staff/patient wellbeing are highly dependent on clinical environments. Primarily, we have improved clinical design by focusing on user requirements. Secondly, we developed a replicable approach to exploring healthcare build plans revealing significant design changes, that may have only been identified once built.
| S-EPMC9204129 | biostudies-literature
Project description: Not available
| S-BSST416 | biostudies-other
Project description:COVID-19 is a biphasic infectious disease with no approved vaccine or pharmacotherapy. The first drug that has shown promise in reducing COVID-19 mortality in severely-ill patients is dexamethasone, a cheap, well-known anti-inflammatory glucocorticoid, approved for the treatment of inflammatory conditions including respiratory diseases such as asthma and tuberculosis. However, about 80% of COVID-19 patients requiring oxygenation, and about 67% of patients on ventilators, are not responsive to dexamethasone therapy mainly. Additionally, using higher doses of dexamethasone for prolonged periods of time can lead to severe side effects and some patients may develop corticosteroid resistance leading to treatment failure. In order to increase the therapeutic efficacy of dexamethasone in COVID-19 patients, while minimizing dexamethasone-related complications that could result from using higher doses of the drug, we applied a chemocentric informatics approach to identify combination therapies. Our results indicated that combining dexamethasone with fast long-acting beta-2 adrenergic agonists (LABAs), such as formoterol and salmeterol, can ease respiratory symptoms hastily, until dexamethasone's anti-inflammatory and immunosuppressant effects kick in. Our studies demonstrated that LABAs and dexamethasone (or other glucocorticoids) exert synergistic effects that will augment both anti-inflammatory and fibronectin-mediated anticoagulant effects. We also propose other alternatives to LABAs that are supported by sound systems biology evidence, such as nitric oxide. Other drugs such as sevoflurane and treprostinil interact with the SARS-CoV-2 interactome and deserve further exploration. Moreover, our chemocentric informatics approach provides systems biology evidence that combination therapies for COVID-19 will have higher chances of perturbing the SARS-CoV-2 human interactome, which may negatively impact COVID-19 disease pathways.
| S-EPMC7689662 | biostudies-literature