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94 results for “Emergency Service”
Synthetic Dataset of Emergency Healthcare Services
<p>Synthetic dataset of emergency services comprised of several CSV files that we have generated using a simulation software. This dataset is open for public use; please cite our work if used in research or applications.</p> <p>## File Overview</p> <ol> <li>**CheckBloodPressure.csv** - (9 KB): Contains blood pressure Server records of patients.</li> <li>**CheckPatientType.csv** - (19 KB): Identifies the type of each patient (e.g., 1 or 3).</li> <li>**Fill_Information.csv** - (2 KB): Fill information records for new patients.</li> <li>**MedicalRecord1.csv** - (10 KB): Medical record dataset for patient type 1.</li> <li>**MedicalRecord2.csv** - (4 KB): Medical record dataset for patient type 2.</li> <li>**MedicalRecord3.csv** - (2 KB): Medical record dataset for patient type 3.</li> <li>**MedicalRecord4.csv** - (13 KB): Medical record dataset for patient type 4.</li> <li>**OutPatientDepartment.csv** - (18 KB): Data related to the satisfaction and length of stay of an given patient.</li> <li>**Triage.csv** - (13 KB): Data related to the triage process.</li> <li>**README.txt** - (4 KB): Documentation of the dataset, including structure, metadata, and usage.</li> </ol> <p> </p> <p>## Common Fields Across Files</p> <ol> <li>**Patient ID** *(Integer)*: Unique identifier for each patient.</li> <li>**Patient Type** *(Integer)*: Classification of patient (e.g., 1, 4).</li> <li>**Medical Records Arrival Time** *(DateTime)*: Timestamp of the patient's first arrival in the medical record department.</li> <li>**Exiting Time** *(DateTime)*: Timestamp when the patient exits a Server.</li> <li>**Waiting Time (min)** *(Real)*: Total waiting time before being attended to.</li> <li>**Resource Used** *(String)*: Resource (e.g., Operator) allocated to the patient.</li> <li>**Utilization %** *(Real)*: Utilization rate of the resource as a percentage.</li> <li>**Queue Count Before Processing** *(Integer)*: Number of patients in the queue before processing begins.</li> <li>**Queue Count After Processing** *(Integer)*: Number of patients in the queue after processing ends.</li> <li>**Queue Difference** *(Integer)*: Difference between the before and after queue counts.</li> <li>**Length of Stay (min)** *(Real)*: Total time spent in the simulation by the patient.</li> <li>**LOS without Queues (min)** *(Real)*: Length of stay excluding any queuing time.</li> <li>**Satisfaction %** *(Real)*: Patient satisfaction rating based on their experience.</li> <li>**New Patient?** *(String)*: Indicates if this is a new patient or a returning one.</li> </ol> <p>Ferreira, M. (2024). Synthetic Dataset of Emergency Healthcare Services [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14212812</p> <p> </p>
Effective use of personal health records to support emergency services
<p>Data and code supporting the paper titled "Effective use of personal health records to support emergency services" by Morales et al. (2020)</p> <p>Content:</p> <ol> <li>Synthetic Health Records dataset <ul> <li>ttl and nt files</li> </ul> </li> <li>Gold Standard <ul> <li>Curated Gold Standard based on the data set</li> </ul> </li> <li>Code used to analyse the dataset <ul> <li>Python</li> <li>Queries the data in Blazegraph, identifies valid data points and obtains people in need of help and the type of disability</li> </ul> </li> </ol>
Project F2 - Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services
<p>This dataset compiles the results of ridesharing trajectory data aggregation, the analysis result of the transit and ridesharing trip data, and raw data of transit station coordinates and schedules in Chengdu, China. This is part of STRIDE project F2 titled "Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services."</p>
Attitudes and Stressors related to the SARS-CoV-2 Pandemic among Emergency Medical Services Workers in Germany: A cross-sectional Study
<p>This dataset stems from a cross-sectional study conducted in April and Mai 2020 among n=1537 emergency medical services workers (EMS) from entire Germany during the first peak of the SARS-CoV-2 pandemic. The study questionnaire was distributed online with help of the German Association of Emergency Medical Service on their social media channels. The collected data provides insights into major stressors among EMS workers at the first peak of the pandemic in Germany and allows for analysis of possible determinants of major stressors via logistic regression analysis. No funding was obtained for this study.</p> <p> </p> <p><strong>Research question:</strong></p> <p>Investigation of pandemic-related attitudes, stressors and work outcomes among emergency medical services workers during the SARS-CoV-2 pandemic</p> <p><strong>Study population: </strong></p> <p>Emergency medical services workers in Germany</p> <p><strong>Study type: </strong></p> <p>Cross-sectional study (two independent cross-sectional waves)</p> <p><strong>File type: </strong></p> <p>SPSS file (.sav)</p> <p><strong>Study periods: </strong></p> <p>First wave: April 9th-16th 2020<br> Second wave: Mai 14th-21st 2020</p> <p><strong>Number of participants: </strong></p> <p>1537</p> <p><strong>Missing values: </strong></p> <p>None (due to online survey) </p> <p><strong>Original variables: </strong></p> <p>v_982, v_1, v_2, v_31, v_4, v_5, v_7, dupl1_v_13, dupl1_v-14, v_57, v_13, v_37, v_38, v_39, v_40, v_41, v_42, v_43, v_44, v_45, v_46, v_47, v_48, v_49, v_50, dupl1_v_40, dupl1_v_41, dupl1_v_42, dupl1_v_43, v_55, Beruf_Rettungsdienst, Welle</p> <p>All other variables were calculated from the original variables either by rescaling or dichotomization. </p> <p>Dichotomization of attitudes, stressors and work outcomes: <br> Answer options "Strongly disagree" and "Disagree" were labelled as "no"<br> Answer options "Agree" and "Strongly Agree" were labelled as "yes"</p> <p>Dichotomization of self-rated health:<br> Answer options "Very bad", "Bad" and "Moderate" were labelled as "Bad".<br> Answer options "Good" and "Very good" were labelled as "Good".</p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Fire danger indices historical data from the Copernicus Emergency Management Service
<p>June, July August consolidated data.</p> <p>Generated using Copernicus Climate Change Service information 2021.</p>
Factors related to the implementation of patient safety culture in prehospital emergency services
<p><span>Background</span><span>: Prehospital emergency services are one of the main areas of health care, which provide emergency services for patients who experience acute, critical illness or injury outside the hospital. Prehospital services are more often carried out by the Emergency Medical Services (EMS) team, as one of the health service providers who come to the scene more quickly, both in daily emergencies.</span></p> <p><span>Purpose</span><span>: </span><span>This study aims to determine the factors related to the application of patient safety culture and determine the most dominant factors in the application of patient safety culture at </span><span>the prehospital emergency service in Bantaeng.</span></p> <p><span>Results:</span><span> The results showed that all domains of patient safety culture had a significant effect (p<0.05) namely teamwork 0.000, safety climate 0.000, management perception and support 0.000, job satisfaction 0.019, work environment 0.000 and stress recognition 0.000. The most influential domain is stress recognition 1.116 and Adjust R Square value of 69.7%.</span></p> <p><span>Conclusion:</span><span> Patient safety needs to be a priority in handling and organizations must continue to evaluate the application of patient safety culture in PSC 119 setting in Bantaeng. Nurses are responsible for improving services that prioritize patient safety by understanding all factors related to the implementation of patient safety culture.</span></p>
Designing and implementing smart glass technology for emergency medical services: A sociotechnical perspective
<p>Objective: This study aims to investigate key considerations and critical factors that influence the implementation and adoption of smart glasses in fast-paced medical settings such as emergency medical services (EMS).</p> <p>Materials and Methods: We employed a sociotechnical theoretical framework and conducted a set of participatory design workshops with fifteen EMS providers to elicit their opinions and concerns about using smart glasses in real practice.</p> <p>Results: Smart glasses were recognized as a useful tool to improve EMS workflow given their hands-free nature and capability of processing and capturing various patient data. Out of the eight dimensions of the sociotechnical model, we found that hardware and software, human-computer interface, workflow, and external rules and regulations were cited as the major factors that could influence the adoption of this novel technology. EMS participants highlighted several key requirements for successful implementation of smart glasses in the EMS context, such as durable devices, easy-to-use and minimal interface design, seamless integration with existing systems and workflow, and secure data management.</p> <p>Discussion: Applications of the sociotechnical model allowed us to identify a range of factors, including not only technical aspects, but also social, organizational, and human factors, that impact the implementation and uptake of smart glasses in EMS. Our work informs design implications for smart glass applications to fulfill EMS providers' needs.</p> <p>Conclusion: The successful implementation of smart glasses in EMS and other dynamic healthcare settings needs careful consideration of sociotechnical issues and close collaboration between different stakeholders.</p>
THE FIELD'S MALL MASS SHOOTING: EMERGENCY MEDICAL SERVICES RESPONSE
<p>Case report describing the mass shooting at the Field's shopping mall in Copenhagen, July 3rd, 2022.</p> <p>Dataset to support the findings of the case report, hopefully to be published after peer-review in Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.</p>
Emergency Medical Services Box
An original Emergency Medical Services (EMS) Box. Also known long ago as a First Aid box. Perhaps dates back to the 1940's. This object is a part of the Indianapolis Fire Fighters Museum collection. This item was 3D scanned using a Creaform Go Scan 50. For more information about this object, feel free to visit: https://www.visitindy.com/indianapolis-firefighters-museum-historical-society For more information about the 3D Digitization Program at IUPUI visit: https://www.ulib.iupui.edu/digitalscholarship/3ddig Source: Objaverse 1.0 / Sketchfab
Designing and implementing smart glass technology for emergency medical services: A sociotechnical perspective
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Factors related to the implementation of patient safety culture in prehospital emergency services
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Detailed emergency medical services midazolam administration by dose and route
Open the record for dataset details and reuse information.
Data from: Reducing early infant mortality in India: results of a prospective cohort of pregnant women utilizing emergency medical services
Objectives: To describe the demographic characteristics and clinical outcomes of neonates born within 7 days of public ambulance transport to hospitals across five states in India. Design: Prospective observational study. Setting: Five Indian states using a centralised EMS agency that transported 3.1 million pregnant women in 2014. Participants: Over 6 weeks in 2014, this study followed a convenience sample of 1,431 neonates born to women utilizing a public-private ambulance service for a 'pregnancy related' problem. Initial calls were deemed 'pregnancy related' if categorised by EMS dispatchers as 'pregnancy', 'childbirth', 'miscarriage' or 'labour pains'. Interfacility transfers, patients absent on ambulance arrival, refusal of care, and neonates born to women beyond 7 days of using the service were excluded. Main outcome measures: Death at 2, 7 and 42 days after delivery. Results: Among 1,684 women, 1,411 gave birth to 1,431 newborns within 7 days of initial ambulance transport. Median maternal age at delivery was 23 years (IQR: 21-25). Most mothers were from rural/tribal areas (92.5%) and lower social (79.9%) and economic status (69.9%). Follow-up rates at 2, 7 and 42 days were 99.8%, 99.3% and 94.1%, respectively. Cumulative mortality rates at 2, 7 and 42-days follow-up were 41, 53 and 62 per 1000 births, respectively. The perinatal mortality rate (PMR) was 53 per 1000. Preterm birth [OR: 2.89, 95% CI: 1.67-5.00], twin deliveries (OR: 2.80, 95% CI: 1.10-7.15), and cesarean section (2.21, 95% CI: 1.15-4.23) were the strongest predictors of mortality. Conclusions: The perinatal mortality rate associated with this cohort of patients with high-acuity conditions of pregnancy was nearly two times the most recent rate for India as a whole (28 per 1000 births). EMS data has the potential to provide more robust estimates of PMR, reduce inequities in timely access to healthcare, and increase facility-based care through service of marginalized populations.
Why do payments for watershed services emerge? A cross-country analysis of adoption contexts -- data
<p>Data used in article. Why do payments for watershed services emerge? A cross-country analysis of adoption contexts</p>
Job satisfaction and performance orientation in German emergency medical service – a nationwide survey
<p>In a nationwide German cross-sectional questionnaire survey, we collected data on job satisfaction an perfomance orientation of paramedics in Germany.</p> <p>This file contains the underlying data set as a result of the online questionnaire.</p>
Data from: Characteristics and outcomes of women utilizing emergency medical services for third-trimester pregnancy-related complaints in India: a prospective observational study
Objectives: Characterize the demographics, management, and outcomes of obstetric patients transported by emergency medical services (EMS). Design: Prospective observational study. Setting: Five Indian states utilizing a centralized EMS agency that transported 3.1 million pregnant women in 2014. Participants: This study enrolled a convenience sample of 1684 women in third trimester of pregnancy calling with a "pregnancy-related" complaint for free-of-charge ambulance transport. Calls were deemed "pregnancy-related" if categorized by EMS dispatchers as "pregnancy", "childbirth", "miscarriage", or "labor pains". Interfacility transfers, patients absent upon ambulance arrival, and patients refusing care were excluded. Main outcome measures: Emergency medical technician (EMT) interventions, method of delivery, and death. Results: The median age enrolled was 23 years (IQR 21-25). Women were primarily from rural/tribal areas (1550/1684 (92.0%)) and lower economic strata (1177/1684 (69.9%)). Time from initial call to hospital arrival was longer for rural/tribal compared to urban patients (66 min (IQR 51-84) vs 56 min (IQR 42-73), respectively, p<0.0001). EMTs assisted delivery in 44 women, delivering the placenta in 33/44 (75%), performing transabdominal uterine massage in 29/33 (87.9%), and administering oxytocin in none (0%). There were 1411 recorded deliveries. Most women delivered at a hospital (1212/1411 (85.9%)), however 126/1411 (8.9%) delivered at home following hospital discharge. Follow-up rates at 48 hours, 7 days, and 42 days were 95.0%, 94.4%, and 94.1%, respectively. Four women died, all within 48 hours. The cesarean section rate was 8.2% (116/1411). On multivariate regression analysis, women transported to private hospitals versus government primary health centers were less likely to deliver by cesarean section (odds ratio 0.14 (0.05 to 0.43)). Conclusions: Pregnant women from vulnerable Indian populations use free-of-charge EMS for impending delivery, making it integral to the health care system. Future research and health system planning should focus on strengthening and expanding EMS as a component of EmONC.
Safety and Efficacy of Emergency On-call Respiratory Physiotherapy Services in the Paediatric Intensive Care Unit
ClinicalTrials.gov study NCT01999426. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Response Times in Danish Emergency Medical Services
ClinicalTrials.gov study NCT06666647. IPD Sharing: NO. Countries: 1. Publications: 38.
Pilot Project: Community Health Assessment Program Through Emergency Medical Services
ClinicalTrials.gov study NCT02772263. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.