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4,954 results for “hospitals”
The deployment of temporary nurses and its association with permanent nurses' outcomes in Swiss psychiatric hospitals: A secondary analysis.
<p>The objective of this analysis was to investigate the frequency of temporary nurses’ deployment and their association with nurse staffing levels and permanent nurses’ outcomes in Swiss psychiatric hospitals. The data is based on the Match<sup>RN</sup> Psychiatry study including 79 psychiatric units and 651 nurses and provides unit level frequency of temporary nurses’ deployment and individual nurse level data on staffing levels and permanent nurses’ outcomes namely job satisfaction, burnout, and intention to leave organization or profession. The data was collected in 2019 and 2020. We provide the unit and nurse-level dataset, a codebook and the r code to replicate the analyses of the paper. You can find the paper here: <a href="https://peerj.com/articles/15300/">https://peerj.com/articles/15300/</a> </p>
Epidemiological and clinical characteristics predictive of ICU mortality of traumatic brain injury patients treated at a trauma reference hospital – A cohort study - Dataset
<p><strong>Dataset of a cohort whose summary is described below.</strong></p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong>: Traumatic brain injury (TBI) has substantial physical, psychological, social and economic impacts, with high rates of morbidity and mortality. Considering its high incidence, the aim of this study was to identify epidemiological and clinical characteristics that predict mortality in patients hospitalized for TBI in intensive care units (ICUs). <strong>Methods</strong>: A retrospective cohort study was carried out with patients over 18 years old with TBI admitted to an ICU of a Brazilian trauma referral hospital between January 2012 and August 2019. TBI was compared with other traumas in terms of clinical characteristics of ICU admission and outcome. Univariate and multivariate analyses were used to estimate the odds ratio for mortality. <strong>Results</strong>: Of the 4816 patients included, 1114 had TBI, with a predominance of males (85.1%). Compared with patients with other traumas, patients with TBI had a lower mean age (45.3 ± 19.1 versus 57.1 ± 24.1 years, p < 0.001), higher median APACHE II (19 versus 15, p <0.001) and SOFA (6 versus 3, p < 0.001) scores, lower median Glasgow Coma Scale (GCS) score (10 versus 15, p < 0.001), higher median length of stay (7 days versus 4 days, p < 0.001) and higher mortality (27.6% versus 13.3%, p < 0.001). In the multivariate analysis, the predictors of mortality were older age (OR: 1.008 [1.002-1.015], p = 0.016), higher APACHE II score (OR: 1.180 [1.155-1.204], p < 0.001), lower GCS score for the first 24 hours (OR: 0.730 [0.700-0.760], p < 0.001), and greater number of brain injuries and presence of associated chest trauma (OR: 1.727 [1.192-2.501], p < 0.001). <strong>Conclusion</strong>: Patients admitted to the ICU for TBI were younger and had worse prognostic scores, longer hospital stays and higher mortality than those admitted to the ICU for other traumas. The independent predictors of mortality were advanced age, APACHE II score, first 24-hour GCS score, number of brain injuries and chest trauma.</p>
Eye tracking videos and raw data of breathing recognition attempts in simulated out-of-hospital cardiac arrest
<div> <div> <div> <p>This dataset comprises eye tracking videos and raw data documenting attempts to recognize breathing in simulated out-of-hospital cardiac arrest scenarios.</p> <p>The data were recorded using an Ergoneers Dikablis head-mounted eye tracker.</p> <p>Our analysis of this data resulted in the publication of two studies: Study 1, available at <a href="https://doi.org/10.1097/SIH.0000000000000617" target="_blank" rel="noopener">https://doi.org/10.1097/SIH.0000000000000617</a>, and Study 2, accessible at <a href="https://doi.org/10.25894/ijfae.2307" target="_blank" rel="noopener">https://doi.org/10.25894/ijfae.2307</a></p> <p> </p> <p>Version 2 is up-to-date.</p> <p>In Version 1:</p> <ul> <li>the doi for Study 2 was incorrect</li> <li>data for participant #51 of Study 1 were missing</li> </ul> </div> </div> </div>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients
<p>Code and data for the manuscript "Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients".</p> <p>Contains all code located at <a href="https://github.com/lasalletj/COVID_Neutrophils">https://github.com/lasalletj/COVID_Neutrophils</a> as well as additional data files needed to run the code.</p> <p>Three additional publicly available data objects are required to run the code from start to finish. The first, covid.combined_final.Robj, from the Sinha et al. Nature Medicine 2022 paper (<a href="https://doi.org/10.1038/s41591-021-01576-3">https://doi.org/10.1038/s41591-021-01576-3</a>), is downloadable from the following link: <a href="https://figshare.com/ndownloader/files/31562957">https://figshare.com/ndownloader/files/31562957</a>. The other two required objects, seurat_COVID19_Neutrophils_cohort2_rhapsody_jonas_FG_2020-08-18.rds and seurat_COVID19_freshWB-PBMC_cohort2_rhapsody_jonas_FG_2020-08-18.rds, are from the Schulte-Schrepping et al. Cell 2020 paper (<a href="https://doi.org/10.1016/j.cell.2020.08.001">https://doi.org/10.1016/j.cell.2020.08.001</a>), and can be downloaded from <a href="https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files">https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files</a> and <a href="https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files">https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files</a>, respectively.</p> <p>Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact, Moshe Sade-Feldman (msade-feldman@mgh.harvard.edu) upon request.</p>
2021_Veterinary_Hospital_FacilityDistributions
<p>Veterinary and Hospital distributions. </p> <p><strong>Abstract:</strong></p> <p>These maps of veterinary facilities and hospital health care locations are derived from the open-source Open Street Maps (OSM). These are large datasets that underlie most of the satnav utilities, and are continuously updated by members of the public. They are compiled by GEOFABRIK, and can be downloaded from https://www.geofabric.de. Healthcare data have been further enhanced by the Global Health Sites Mapping Project (https://healthsites.io). <br> <br>The data consist of locations (“points of interest”) and building outlines, and are made up of points and polygons that are stored separately in the OSM files. Each record has a series of descriptors (“tags”) such as ‘amenity’ which may include descriptions of a hospital or veterinary clinic. These tags are provided by the people who add the data to the maps and are multilingual and very variable. Most records contain additional more generic tags that can be used to identify classes of locations like hospitals or pharmacies.<br> <br> ERGO has taken the IO and OSM datasets, combined the polygon and point data, converted the polygons to points, and removed any duplicates (where building outlines also have point location records), to produce and global datasets for healthcare and regional datasets for healthcare and veterinary locations covering Europe and its neighboring countries. </p> <p> </p> <p><strong>File naming scheme: </strong></p> <p>Full descriptions in Maps of Healthcare and Veterinary Locations.pptx. </p> <p>ESRI point shapefiles are as follows:<br> Healthcare Global: Ionodewaynodupallmay21.shp<br> Healthcare Regional: MONODWAYALLnodupMAY21.shp<br> Veterinary Regional: afeupolypointsnondupmergemay21.shp<br> <br> The regional maps are produced by tabulating the number of each healthcare amenity or veterinary locations within administrative zones, normalising by the administrative unit areas. The maps are produced from the following shape files:<br> Healthcare Regional: MOiohealthamenitycountadmin2may21.shp<br> Veterinary Regional: MONODWATALLVETSNODUPMAY21.shp<br> An arcGS 10.4 MPK project file (ERGOhealthvetforweb.mpk) has been provided to display the spatial data provided</p> <p> </p> <p><strong>Projection </strong>+ EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><strong>Spatial extent:</strong><br>Extent -73.2621536249999394,18.9632860000000392 : 69.0703202920000336,83.6274185180000842</p> <p><strong>Spatial resolution:</strong><br>Shp files</p> <p><strong>Temporal Resolution:</strong></p> <p>Year 2021</p> <p><strong>Values:</strong></p> <p>Per Units (Number of facilities ): the number of each healthcare amenity or veterinary locations within administrative zones,</p> <p><strong>Source: </strong></p> <p>Open Street Maps (OSM):</p> <p>GEOFABRIK https://www.geofabric.de</p> <p>Health care data https://healthsites.io</p> <p><strong>Software used:</strong><br>ArcMap 10.4</p> <p><strong>License:</strong> CC-BY-SA 4.0</p> <p><strong>Processed by</strong>:<br>ERGO (Environmental Research Group Oxford) <a href="https://ergoonline.co.uk/" target="_blank" rel="noopener">https://ergoonline.co.uk/</a> for the H2020 MOOD project</p>
US Hospitals Quality Data
<p>Cleaned and merged dataset about US hospital-level quality measures, culled from the Centers for Medicare and Medicaid Services open data API as of July 2019. Assumptions and preprocessing to derive dataset can be found at <a href="https://github.com/emigre459/hospital-chargemaster">https://github.com/emigre459/hospital-chargemaster</a>.</p>
Effect of the Increased Nursing Attrition Rate on Nursing Administration Process during the Covid-19 Pandemic in a Selected Tertiary Care Hospital
<p><span>During<span> </span>the<span> </span>COVID-19<span> </span>outbreak,<span> </span>healthcare<span> </span>professionals,<span> </span>particularly<span> </span>nurses,<span> </span>were<span> </span>more<span> </span>prone to<span> </span>diseases.<span> </span>Globally<span> </span>attrition<span> </span>rate<span> </span>was<span> </span>high<span> </span>among<span> </span>nurses<span> </span>and<span> </span>during<span> </span>the<span> </span>pandemic,<span> </span>it<span> </span>increased because<span> </span>of<span> </span>various<span> </span>reasons<span> </span>such<span> </span>as<span> </span>the<span> </span>risk<span> </span>of<span> </span>infection,<span> </span>occupational<span> </span>and<span> </span>psychological<span> </span>stress, causing risk to their loved ones. This led to a chaotic situation where nurse managers were forced to implement specific strategic plans to deal with increased nurse attrition. This study aims<span> </span>to<span> </span>describe<span> </span>the<span> </span>impact<span> </span>of<span> </span>nurse<span> </span>attrition<span> </span>rate<span> </span>on<span> </span>nursing<span> </span>administration<span> </span>during<span> </span>COVID-19 at a selected tertiary care hospital. The research approach adopted in this study is descriptive cross-sectional. A total sample of 66 nurses involved in nursing administration. The data is collected through a structured questionnaire and the nurse attrition data during the COVID-19 pandemic period was collected from the interview method during the survey. Statistical tests used were frequency, percentage, mean, Standard Deviation (S.D). The study showed that there is a moderate impact of increased nurse attrition on nursing administration during the COVID-19 pandemic. The study led to the identification of gaps that need to be addressed in a similar crisis.</span></p>
Intra-hospital transport of newborn infants dataset
<p>The dataset [TRI_database.xslx; TRI_codebook.pdf] provides information in 990 intra-hospital transports performed in 293 infants. Baseline demographics, clinical characteristics, characteristics of transports, adverse events that occurred during transports and interventions are documented.</p>
Environmental efficiency estimates for 127 Swiss acute care hospitals
<p>These are the estimates from our enviromental efficiency estimates using frontier analysis (Stochastic Frontier Analysis and Data Envelopment Analysis) for 127 Swiss acute care hospital in 2018.</p> <p>For further details please see our working paper: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3939627">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3939627</a></p>
Dataset of "Exposure to airborne SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures."
<p>The authors highly appreciate being contacted if the data is to be used for any purpose.</p> <p>The following data set was used in the study entitled "<strong>Exposure to airborne </strong><strong>SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures.</strong>" and published in <em>Heliyon</em> Journal.</p> <p>This study characterized the transmission dynamics of airborne SARS-CoV-2 in normal and intensive care units. The data were collected over the period of 2020. In total, 165 and 62 air and environmental samples, respectively, were collected in four COVID-19 wards and ICUs in Cyprus and analyzed by RT-PCR. The comparison between RT-PCR and an alternative method for SARS-CoV-2 detection in air that provides comparable results but is less cumbersome and time demanding, is also given in the tab "Comparison with BELD".</p> <p>The data from sampling airborne SARS-CoV-2 using a MOUDI impactor are not included in this document but can be found in the supplement of the relevant publication.</p> <p>Please refer to the manuscript and its supplementary material for more information about how the data was collected. </p> <p> </p>
Longitudinal observational study of pediatric patients with primary brain tumors: establishment of a hospital-based registry.
<p>Although tumors of the central nervous system (CNS) represent 2 % of all malignancies in general, they cause a disproportionately large morbidity and mortality and are the second most common form of cancer in children and the major solid tumor in childhood in the U.S., occurring in 21.3% of all children with malignant disease. The treatment of brain tumors in children and adolescents has evolved significantly in recent decades. Nowadays, most children with a diagnosis of brain tumor are treated properly and achieve prolonged survival. In order to obtain an overview of the impact of brain tumors, specialized registries, which provide information on all types of brain tumors, have emerged in several countries. Following on the pioneering Japanese and American experiences of specialized national records of brain tumors, other specialized registries were opened in European countries. This project aims to initiate a registry of the epidemiological profile of patients treated for CNS tumors in the Pediatric Cancer Center (CPC) of our hospital from January 2000 to December 2013, at diagnosis and during follow-up, updating information periodically. This data will be recorded in an electronic database capable of storing, retrieving and presenting information of interest. Prospectively recorded epidemiological data of patients diagnosed from January 2014 will be accrued, maintaining the database active to continuously record information on patients with CNS tumors treated in the CPC HIAS. Thus, creating a hospital registry of pediatric patients with CNS tumors. To this end, an instrument of data collection will be created using Google Apps (Google Inc., 2014), a digital platform with capacity for storage, creation and editing of documents and collaboration in real time over the cloud.</p>
Why, what and how do European healthcare managers use performance data? Results of a survey and workshop among members of the European Hospital and Healthcare Federation (Data set; anonymised)
<p>The dataset presents results of a descriptive cross-sectional study based on a survey, delivered through an online self-reported questionnaire. The questionnaire was distributed to managers of hospitals and other health care organisations in a purposive sample of participants to the Exchange Programmes of the European Hospital and Health Care Federation (HOPE) eliciting information on the actual use of performance data in hospitals and other healthcare organisations in Europe in 2019.<br> Data collected through the online questionnaire was analysed using univariate descriptive statistics. Analyses were conducted using the R statistical program version 3.6.1. Respondents were, for certain parts of the analysis, sub-grouped by their reported managerial position and experience, as well as the type of organisation they work for. Analysis was done on a full sample of respondents, including the primary, 2019 HOPE Exchange Programme participants, and the secondary study population, 2015-2018 Exchange Programme alumni and local hosts.</p>
Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH): outcome data
<p>The datasets reports baseline and outcome data from the 'Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH)' experimental study. The study evaluated the effectiveness of a geriatric co-management programme on the cardiac care units of the University Hospitals Leuven. Sample included patients aged 75 years or older. Measurements included: demographic, functional status, cognitive status, depressive symptoms, anxiety symptoms, quality of life, physical performance, readmission rates, survival.</p> <p>Please see Word document for more information.</p> <p>Please see protocols for more information:</p> <p><a href="https://clinicaltrials.gov/ct2/show/NCT02890927">https://clinicaltrials.gov/ct2/show/NCT02890927</a></p> <p><a href="https://bmjopen.bmj.com/content/8/10/e023593">https://bmjopen.bmj.com/content/8/10/e023593</a></p> <p>The evaluation study is available at https://agsjournals.onlinelibrary.wiley.com/doi/full/10.1111/jgs.17093 </p>
Importance of respiratory syncytial virus as a predictor of hospital length of stay in Bronchiolitis
<p>DATA BASE OF ARTICLE TITTLED "<strong>Importance of respiratory syncytial virus as a predictor of hospital length of stay in Bronchiolitis"</strong></p>
Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study
<p>This is the dataset of the study called "Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study". <br> <br> <strong>Abstract: </strong></p> <p>Background<br> Coronavirus disease 2019 (COVID-19) is now a global pandemic with Europe and the USA at its epicenter. Little is known about risk factors for progression to severe disease in Europe. This study aims to describe the epidemiology of COVID-19 patients in a Swiss university hospital.</p> <p>Methods<br> This retrospective observational study included all adult patients hospitalized with a laboratory confirmed SARS-CoV-2 infection from March 1 to March 25, 2020. We extracted data from electronic health records. The primary outcome was the need to mechanical ventilation at day 14. We used multivariate logistic regression to identify risk factors for mechanical ventilation. Follow-up was of at least 14 days. <br> <br> Results<br> 200 patients were included, of whom 37 (18·5%) needed mechanical ventilation at 14 days. The median time from symptoms onset to mechanical ventilation was 9·5 days (IQR 7.00, 12.75). Multivariable regression showed increased odds of mechanical ventilation in males (3.26, 1.21-9.8; p=0.025), in patients who presented with a qSOFA score ≥2 (6.02, 2.09-18.82; p=0.001), with bilateral infiltrate (5.75, 1.91-21.06; p=0.004) or with a CRP of 40 mg/l or greater (4.73, 1.51-18.58; p=0.013). <br> <br> Conclusions<br> This study gives some insight in the epidemiology and clinical course of patients admitted in a European tertiary hospital with SARS-CoV-2 infection. Male sex, high qSOFA score, CRP of 40 mg/l or greater and a bilateral radiological infiltrate could help clinicians identify patients at high risk for mechanical ventilation.</p>
hospital-lyon
<h3><strong>Overview</strong></h3><p>This dataset contains the temporal network of contacts between patients, between patients and healthcare workers (HCWs), and among HCWs in a hospital ward in Lyon, France, from Monday, December 6, 2010, at 1:00 pm to Friday, December 10, 2010, at 2:00 pm. The study included 46 HCWs and 29 patients.</p><p>Timestamps were recorded in 20-second intervals. In the original data, each line has the form "t i j Si Sj", where i and j are the anonymous IDs of the persons in contact, Si and Sj are their labels (NUR=paramedical staff, i.e. nurses and nurses' aides; PAT=Patient; MED=Medical doctor; ADM=administrative staff), and the interval during which this contact was active is [ t – 20s, t ]. We form hyperedges through cliques of simultaneous contacts. Specifically, for every unique timestamp in the dataset, we construct a hyperedge for every maximal clique amongst the contact edges that exist for that timestamp. All timestamps are in standard ISO8601 format.</p><h4><strong>Statistics</strong></h4><ul><li>number of nodes: 75 (46 HCWs and 29 patients)</li><li>number of timestamped hyperedges: 21,398</li><li>there is a single connected component of size 75</li></ul><h4><strong>Source of original data</strong></h4><ul><li><a href="http://www.sociopatterns.org/datasets/hospital-ward-dynamic-contact-network/">Hospital ward dynamic contact network</a></li></ul><h4><strong>References</strong></h4><p>If you use this dataset, please cite the following:</p><ul><li><a href="http://dx.doi.org/10.1371%2Fjournal.pone.0073970">Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors</a>. Vanhems et al., PLoS ONE, 2013.</li><li><a href="http://www.sociopatterns.org/">The SocioPatterns collaboration</a></li></ul>
Data for "Host starvation and in hospite degradation of algal symbionts shape the heat stress response of the Cassiopea-Symbiodiniaceae symbiosis"
<p>Raw data associated with the publication "Host starvation and in hospite degradation of algal symbionts shape the heat stress response of the Cassiopea-Symbiodiniaceae symbiosis". Temperature profile, daily measurements, physiological measurements, elemental analysis, NanoSIMS data, and cell density data are included as individual tabs in the Excel file. </p>
Understanding Organizational Commitment and its Factors Influencing the Nurse's Job Satisfaction in Hospitals- A Systematic Literature Review and Further Research Agendas
<div> <p><em><span>Organizational commitment is a crucial concept when it comes to human resource management and Organizational behavior. It has to do with how much a worker commits to and identifies with the goals, values, and purposes of their company. Elevated levels of Organizational commitment are associated with enhanced job satisfaction, less attrition, and better performance. The expected ideal condition, status, and research deficit are all included in this article. A research agenda is determined by applying the ABCD framework to qualitatively analyze the identified research gap.The paper documents the topic and provides helpful information about it, which will aid future scholars. </span></em></p> </div>
PubMLST allele profiles and sequence alignments of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019-2020
<p>This dataset provides PubMLST allele profiles, sequence alignments, and Mash distance data used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan". </p>
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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.