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1,170
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1,170 results for “Healthcare”
Dataset: Healthcare Triangle, Inc. (HCTI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Healthcare Services Group, Inc. (HCSG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
An Attribute-Based Access Control model in RFID systems based on blockchain Decentralized Applications for healthcare environments (video demonstration)
<p>An Attribute-Based Access Control model in RFID systems based on blockchain Decentralized Applications for healthcare environments.</p>
Architecture of the healthcare ecosystem for caregivers in C4 Model
<p>The healthcare ecosystem for caregivers is focused on supporting the learning and knowledge management processes to develop and enhance the caregiving competences both at home and in care environments of formal and informal caregivers (García-Holgado, Marcos-Pablos & García-Peñalvo, 2019).</p> <p>This model is part of a paper accepted in UCAmI2019: 13th International Conference on Ubiquitous Computing and Ambient Intelligence (Vázquez-Ingelmo, García-Holgado, García-Peñalvo & Therón, 2019).</p> <p> </p> <p>García-Holgado, A., Marcos-Pablos, S., & García-Peñalvo, F. J. (2019). A Model to Define an eHealth Technological Ecosystem for Caregivers. In Á. Rocha, H. Adeli, L. Reis, & S. Costanzo (Eds.), New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing (Vol. 932, pp. 422-432). Cham: Springer.</p> <p>Vázquez-Ingelmo, A., García-Holgado, A., García-Peñalvo, F. J., & Therón, R. (2019). Dashboard metamodel for knowledge management in technological ecosystem: a case study in healthcare. In press.</p>
Changing birth places to care for healthcare workers, lessons from the pandemic: a scoping review.
<p><strong>Abstract</strong></p> <p><strong>Background:</strong> Together with women giving birth, healthcare professionals involved in perinatal care faced significant challenges due to Covid-19 restrictions. The birth environment has been affected, influencing the quality and nature of interactions between care providers, women, and their families as well as the quality of care provided to women.</p> <p><strong>Aim:</strong> To identify, categorize and reflect on the most relevant aspects of the experience of healthcare workers, especially obstetricians and gynecologists, involved in the peripartum period, after the first wave of the Covid-19 pandemic up till now.</p> <p><strong>Methods:</strong> A scoping review was conducted. After conducting a literature search using a variety of online data- bases, 22 articles were included, and the main/recurrent themes were identified.</p> <p><strong>Results: </strong>The following main themes were described: fear of the virus, changes in the way care is provided, increase in the workload, mental health, psychological distress, support strategies, university education, and perspectives of health managers.</p> <p><strong>Discussion:</strong> Standard maternity departments appeared not appropriate to face the pandemic. Psychological distress on perinatal healthcare workers’, the majority of whom were women, led to the impairment of their mental health, affecting the quality of care provided to women. A key role was played by healthcare managers support in shaping how doctors can cope and recover, and in reducing the mental health impact.</p> <p><strong>Conclusion:</strong> It is very important to address some of the most relevant issues to acknowledge the role of healthcare workers and improve the health systems organization to face future pandemics.</p>
Water Sanitation & Hygiene Quick Check (WASH Q-Check): A rapid assessment tool for healthcare facilities in India
<p>This is a data sheet of the "<strong>Water Sanitation and Hygiene Quick Check (WASH Q-Check): A rapid assessment tool for healthcare facilities in India</strong><strong> </strong>". </p>
Coursera - Cybersecurity in Healthcare (Hospitals & Care Centres)
<p><strong>Coursera - Cybersecurity in Healthcare (Hospitals & Care Centres)</strong></p> <p>M. Jofre</p> <p>The course "Cybersecurity in Healthcare" has been developed to raise awareness and understanding the role of cybersecurity in healthcare (e.g., hospitals, care centres, clinics, other medical or social care institutions and service organisations) and the challenges that surround it. In this course, we will cover both theoretical and practical aspects of cybersecurity. We look at both social aspects as technical aspects that come into play. Furthermore, we offer helpful resources that cover different aspects of cybersecurity. Even if you are not active in the healthcare domain, you will find helpful tips and insights to deal with cybersecurity challenges within any other organisation or in personal contexts as well.</p> <p>This course begins by introducing the opportunities and challenges that digitalisation of healthcare services has created. It explains how the rise of technologies and proliferation of (medical) data has become an attractive target to cybercriminals, which is essential in understanding why adequate cybersecurity measures are critical within the healthcare environment. In later modules, course contents cover the threats, both inside and outside of healthcare organisations like e.g. social engineering and hacking. Module 4 on Cyber Hygiene describes how to improve cybersecurity within healthcare organisations in practical ways. Module 5 looks deeper into how organisational culture affects cybersecurity, the cybersecurity culture, focusing on the interaction between human behaviour and technology and how organisational factors can boost or diminish the level and attention to cybersecurity in healthcare.</p> <p>References:</p> <p>[1] M. Jofre, “Holistic View Of Healthcare Cybersecurity Ecosystem,” Zenodo, Jul. 2020. doi: 10.5281/zenodo.7999970.<br> [2] M. Jofre, “Minimum Quality Standard For Cybersecurity Training In Healthcare,” Zenodo, Jun. 2020. doi: 10.5281/zenodo.8000029.<br> [3] M. Jofre et al., “Cybersecurity and Privacy Risk Assessment of Point-of-Care Systems in Healthcare—A Use Case Approach,” Appl. Sci., vol. 11, no. 15, Art. no. 15, Jan. 2021, doi: 10.3390/app11156699.</p>
Challenges and Limitations in the Design and Implementation of Fair and Equitable Machine Learning Algorithms in Healthcare
<p>We provide the programs in Python, a CSV file with references, images used in the article and a text corpus generated with Python.</p>
Connected Healthcare for Individuals Living at Home With Chronic Conditions
ClinicalTrials.gov study NCT04339296. IPD Sharing: NO. Countries: 1. Publications: 2.
A Study Aimed to Assess the Needs of Subjects With Sickle Cell Disease and Healthcare Professionals Managing Sickle Cell Disease Patients in Selected Nigerian Centres
ClinicalTrials.gov study NCT04505969. IPD Sharing: YES. Countries: 1. Publications: 7.
Healthcare Worker Exposure Response and Outcomes of Hydroxychloroquine
ClinicalTrials.gov study NCT04334148. IPD Sharing: YES. Countries: 1. Publications: 1.
Investigating Healthcare Disparities in Vitiligo
ClinicalTrials.gov study NCT06097494. IPD Sharing: YES. Countries: 1. Publications: 1.
Healthcare Resource Utilisation, Common Mental Health Problems, and Infections in People With Inflammatory Bowel Disease
ClinicalTrials.gov study NCT03836612. IPD Sharing: YES. Countries: 1. Publications: 1.
dataset for "Predictors of unplanned healthcare utilization after ureteroscopic lithotripsy"
<p>Deidentified dataset for submitted manuscript "Predictors of unplanned healthcare utilization after ureteroscopic lithotripsy".</p>
In-hospital patient safety events, healthcare costs and utilization: an analysis of data from the incident reporting system in an academic medical center
<p>Raw Datasets for the study 'In-hospital patient safety events, healthcare costs and utilization: an analysis of data from the incident reporting system in an academic medical center'.</p>
Pediatric Asthma Healthcare Utilization, Viral Testing, and Air Pollution Changes during the COVID-19 Pandemic
<p>Data related to asthma care encounters during the COVID19 pandemic.</p>
Dataset - Overcoming Barriers for Ubiquitous User-Centric Healthcare Services
<p>Datasets used for experimental results (Figure 5) for different virtualization configurations: (a) Average TCP latency; (b) Average TCP throughput w.r.t. message size; (c) Request service response time for variable number of concurrent connections; (d) Request throughput per second for variable number of concurrent connections.</p> <p>Intel Xeon E5-2650 Haswell 2.60 GHz, 64 GB RAM, bare-metal OS: Centos 7</p> <p>OpenStack, Linux KVM, Ubuntu 16.04, para-virtualized VirtIO drivers (network card, disk)</p> <p>Data format: CSV</p> <p>Source: Experiments</p>
Dataset for 'Water, Sanitation and Hygiene Infrastructure and Quality in Rural Healthcare Facilities in Rwanda'
<p>Dataset for 'Water, Sanitation and Hygiene Infrastructure and Quality in Rural Healthcare Facilities in Rwanda' BMC Public Health addressing: (i) general water and power characteristics of 17 rural healthcare facilities in Rwanda, assessments conducted in 2011 and 2012; (ii) functionality of water access points in 10 rural healthcare facilities in Rwanda, 2012, (iii) availability of soap and water for hand washing in 10 rural healthcare facilities in Rwanda, 2012, (iv) state of toilets and latrines in 10 rural healthcare facilities in Rwanda, 2012, (v) Total Coliform concentration (MPN/100 mL) in tap water samples and treated drinking water samples from rural health centers in Rwanda collected during rapid assessment in 2011 and baseline assessment in 2012, (vi) <em>E. coli</em> concentration (MPN/100 mL) in tap water samples and treated drinking water samples from rural health centers in Rwanda collected during rapid assessment in 2011 and baseline assessment in 2012, (vii) free chlorine residual (mg/L) in tap water samples and treated drinking water samples from rural healthcare facilities in Rwanda collected during rapid assessment in 2011 and baseline assessment in 2012.</p>
Sociodemographic and Work-Related Factors Associated with Burnout Syndrome in Healthcare Professionals
<p><span>Background: Burnout syndrome, intensified during the COVID-19 pandemic, has profoundly impacted healthcare professionals' mental health globally. This study in Peru aimed to investigate sociodemographic and work-related factors associated with burnout dimensions among healthcare workers. Methods: Employing a cross-sectional predictive design, this study encompassed 1,844 healthcare professionals across three major hospitals in Peru. A carefully selected sample of 334 participants, ranging in age from 22 to 68 years and including both genders, was studied. Data collection involved a comprehensive sociodemographic work form and the Maslach Burnout Inventory (MBI), which is recognized for its robust psychometric properties. The research included thorough psychometric, descriptive, and inferential analyses, using linear regression to test various predictive models. Results: The study found that emotional exhaustion was significantly associated with several factors, including employment status (R² = 0.010), marital status (R² = 0.003), and length of service (R² = 0.015). Depersonalization showed a significant correlation with gender (R² = 0.015) and the amount of daily patient contact. Personal accomplishment was notably influenced by gender (R² = 0.039) and length of service (R² = 0.025). Conclusions: The research identified several key factors contributing to burnout, notably being female, having a contracted employment status, shorter service duration (specifically 6 to 10 years), and engaging in 1 to 6 hours of patient contact per day. These insights are crucial for understanding burnout in healthcare settings and underscore the need for targeted interventions and support strategies for healthcare professionals.</span></p>
Careers in Science and Healthcare - How changes to medical device regulation have increased training needs
<p>Article contributed to Careers in Science and Healthcare report.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.