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1,170 results for “healthcare”
European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks
<p><strong>European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks</strong></p> <p>ECHO indicators rationale and code definition mapped out in ICD-9 and ICD-10 (for diagnoses) and ICD-9, NOMESCO, OPCS-4, ACHI and Leustungkatalog. </p> <p> </p>
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>
Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis
<p>Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis. Data from the ENLIGHTEN project (H2020 #649456).</p>
Data and materials for Wallace et al (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: comparison of two prospective cohort studies v1.2
<p>This comprises the data and materials for the study: Wallace E, Moriarty F, McGarrigle C, Smith SM, Kenny RA, Fahey T. (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: Comparison of two prospective cohort studies. PLOS ONE 13(10): e0206201. <a href="https://doi.org/10.1371/journal.pone.0206201">https://doi.org/10.1371/journal.pone.0206201</a></p> <p>The anonymised TILDA dataset is publicly available to researchers who meet the criteria for access, at no monetary cost, from the Irish Social Science Data Archive (ISSDA) at University College Dublin (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.ucd.ie%2Fissda%2Fdata%2Ftilda%2F&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=%2Fcochi1RuRtYSUa5sF9uA%2BjOOoNYIg7DPpk0mZl5D2s%3D&reserved=0">http://www.ucd.ie/issda/data/tilda/</a>) and the Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.icpsr.umich.edu%2Ficpsrweb%2FICPSR%2Fstudies%2F34315&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=7LHSSqU8xotACMsalpAjVrV5m95DlapgQViyr4P%2FsXY%3D&reserved=0">http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315</a>). For the CPCR cohort, no provision for data sharing was included in the original ethical approval and participant consent form. As a minimal data set necessary to replicate the present study could not be deidentified due to the large number of demographic variables considered, a synthetic version of the study dataset was produced using the synthpop package in R: <a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fsynthpop%2Findex.html&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128229884&sdata=j3If%2FNe%2F1eGsGAt9hyg3ICMqmLec4aOrjRVKppaRSFU%3D&reserved=0">https://cran.r-project.org/web/packages/synthpop/index.html</a>. This dataset and the analytical code for the present study are presented here. Code developed on the synthetic data can be sent to frankmoriarty@rcsi.ie or <a href="mailto:enquiries.cpcr@rcsi.ie">enquiries.cpcr@rcsi.ie</a> to be run on the original data.</p> <p>v1.2 includes a more detailed description of how the dataset was synthesised.</p>
Healthcare Expenditure and Demographic Trends: A Comparative Analysis of Selected Countries (2000-2024)
<p><strong><em><span>This research journal investigates the interplay between healthcare expenditure, life expectancy, and demographic characteristics across selected countries from 2000 to 2024. Utilizing quantitative analysis, the study examines healthcare spending as a percentage of GDP, average life expectancy, and the age distribution of populations. For instance, the USA's healthcare expenditure is projected to reach 19.2% of GDP by 2024, with an average life expectancy of 81.9 years. In contrast, Bangladesh's healthcare expenditure is anticipated to be 7.0% of GDP, with a life expectancy of 70.0 years. The findings reveal critical insights regarding the effectiveness and sustainability of healthcare systems, emphasizing the need for policy interventions that prioritize healthcare funding, especially in aging populations where the percentage of individuals aged 65 and older is expected to rise significantly—projected at 16.0% for the USA and 7.0% for Bangladesh in 2024</span></em></strong><strong><span>.</span></strong><strong><span> </span></strong></p>
Improving the data access control using blockchain for healthcare domain
<p>This research reviews the importance of blockchains in healthcare as they provide infinite possibilities to individuals, companies, and governments.</p>
Psychometric Properties of the Maslach Burnout Inventory in Healthcare Professionals, Ancash Region, Peru
<p><strong>Background:</strong> Burnout syndrome (BS) among healthcare professionals in Peru demands immediate attention. Consequently, there is a need for a validated and standardized instrument to measure and address it effectively. This study aimed to determine the psychometric properties of the Maslach Burnout Inventory (MBI) among healthcare professionals in the Ancash region of Peru.</p> <p><strong>Methods:</strong> Using an instrumental design, this study included 303 subjects of both sexes (77.56% women), ranging in age from 22 to 68 years (M = 44.46, SD = 12.25), selected via purposive non-probability sampling. Appropriate content validity, internal structure validity, and item internal consistency were achieved through confirmatory factor analysis, and discriminant validity for the three dimensions was obtained. Evidence of convergent validity was found for the Emotional Exhaustion (EE) and Personal Accomplishment (PA) dimensions, with reliability values (ω > .75).</p> <p><strong>Results:</strong> The EE and PA dimensions exhibited acceptable levels of reliability (ω and α > .80). However, the Depersonalization (DP) dimension demonstrated significantly lower reliability (α < .60 and ω < .50).</p> <p><strong>Conclusions:</strong> A correlated three-factor model was confirmed, with most items presenting satisfactory factor loadings and inter-item correlations. Nonetheless, convergent validity was not confirmed for the DP dimension.</p> <p><strong>Keywords: </strong>Burnout; Psychometrics; Validity; Reliability; Healthcare Professionals.</p>
ES17 - Healthcare - Aguilar de Campoo (Spain)
<p>Data files for building: ES17 - Healthcare - Aguilar de Campoo (Spain)</p><p>Languages: Spanish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
ES10 - Healthcare - Villagonzalo/Pedernales (Spain)
<p>Data files for building: ES10 - Healthcare - Villagonzalo/Pedernales (Spain)</p><p>Languages: Spanish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </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>
Practical reflection model for healthcare professional students
<p>This series of videos examines my method of reflective thinking to aid healthcare professional students in developing their personal and professional beleifs, attitudes and behaviours.</p>
Social determinants of Covid-19 infection and death in a rural Indonesia: A rapid healthcare assessment
<p>Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.</p>
Factors influencing the likelihood of accessing healthcare during the COVID-19 pandemic in Ireland: lessons for the future
<p>This is an adapted version of the original National Household Survey - Wave 1 whereby existing variables were recoded to create new variables for the purpose of a new analysis.</p>
IMPLEMENTING AND EVALUATING A PAEDIATRIC EMERGENCY MANAGEMENT TRAINING PROGRAM FOR PRIMARY HEALTHCARE WORKERS IN ZARIA
<p>Nigeria continues to grapple with alarmingly high under-five mortality rates, with approximately 2,300 children dying daily due to preventable and treatable diseases such as pneumonia, malaria, and diarrheal illnesses. This crisis is particularly pronounced in rural areas, where childhood mortality rates are significantly higher than in urban centres. For many rural communities, primary healthcare (PHC) centres serve as the sole access point to medical services, making these centres critical to addressing this public health challenge. Despite their importance, PHC centres often face significant challenges, including inadequate infrastructure, limited access to essential medications, and a shortage of adequately trained health personnel.</p> <p>This study aimed to implement and evaluate a Paediatric Emergency Management Training Program for healthcare workers at PHC centres in Zaria, Kaduna State. The primary objective was to assess the effectiveness of the training in improving the competency of health workers in managing common paediatric emergencies, thereby enhancing the overall quality of care provided at these centres.</p> <p>A cross-sectional descriptive study was conducted involving 139 healthcare workers from selected PHC centres in Sabon Gari and Zaria Local Government Areas, utilizing a multi-stage random sampling technique. Data collection was performed using a pre-tested, semi-structured, self-administered questionnaire designed to evaluate the participants' baseline knowledge, followed by an assessment after the training intervention. The data were analysed using SPSS version 21, with chi-square tests employed to examine the associations between variables. Results were presented using tables and charts for clarity.</p> <p>The findings revealed a generally low baseline competency in managing common paediatric emergencies among the healthcare workers. Specifically, 66 (48.5%) of the respondents demonstrated very poor knowledge regarding the causes of common paediatric emergencies, 32 (23.5%) had poor knowledge, 34 (25.0%) had good knowledge, and only 4 (2.9%) of the respondents exhibited excellent knowledge. Similarly, knowledge regarding the treatment of these emergencies was also lacking, with 46.7% of respondents showing very poor understanding, 34.1% having poor knowledge, 16.3% demonstrating good knowledge, and only 3.0% showing excellent knowledge. A statistically significant relationship was observed between academic qualifications and knowledge levels in both causes (p = 0.004) and treatment (p = 0.000) of paediatric emergencies.</p> <p>The low baseline knowledge underscores the critical need for targeted training interventions. The study highlights the importance of establishing a sustained partnership between the Department of Paediatrics at Ahmadu Bello University Teaching Hospital and the Departments of Primary Health Care in Zaria and Sabon Gari Local Governments. Regular training workshops, held quarterly, are recommended to continuously equip PHC workers with the necessary skills to effectively manage paediatric emergencies, ultimately contributing to a reduction in childhood mortality rates in the region.</p>
Digital Twins - from industrial management to healthcare practice
<p>A guest seminar offered by SCImPULSE Foundation CTO Taghi Aliyev for the University of Parma (IT) master "ARTE" https://www.masterarte-unipr.it/</p>
Planning universal accessibility to public healthcare in sub-Saharan Africa
<p>Replication code and data for the the paper "Planning universal accessibility to public healthcare in sub-Saharan Africa". </p> <p>The tarball contains computer code (in R and javascript) and input data to replicate or update the analysis and the figures, and the result data of baseline and sensitivity analysis model runs. Powerful (or cloud, e.g. Google Earth Engine, RStudio Cloud, or Google Colab) computing facilities are recommended for a successful replication.</p>
CUPID: Parental preferences for unscheduled paediatric healthcare: A Discrete Choice Experiment
<p>Unscheduled healthcare is a key component of healthcare delivery and makes up a significant proportion of healthcare access with children being particularly high users of unscheduled healthcare. Understanding the relative importance of factors that influence this behaviour and decision making is fundamental to ensuring the system is best designed to meet the needs of users, and the appropriate cost-effective usage of health system resources.</p> <p>A discrete choice experiment (DCE) was developed to identify the preferences of parents accessing unscheduled healthcare for their children. Data was collected from parents in Ireland (N = 458) to elicit preferences across five attributes: timeliness, appointment type, healthcare professional attended, telephone guidance prior to attending, and cost. Using mixed logit models, all attributes were statistically significant, with same-day or next-day access, coupled with care by their own GP, identified as the strongest preferences of parents accessing unscheduled healthcare for their children. The results have implications for policy development and implementation initiatives that seek to improve unscheduled health services as understanding how parents use these services can maximise their effectiveness. </p> <p>Files explained:</p> <p>1. Parent_Decision_Making_DCE_Survey.docx details the survey.</p> <p>2. Final Parent DCE design.ngd: Ngene™ file used to set-up the DCE. A Bayesian efficient design, based on minimising the Bayesian D-error criterion, was used to develop the choice sets and the alternatives using Ngene<sup>TM </sup>software. In total, 24 choice sets were created, and a blocked design split the choice sets into two blocks of twelve to minimise the burden on respondents.</p> <p>3. Data from Survey 23 Feb 2021.xlsx is the source file detailing responses to the survey.</p> <p>4. Stata Data Formatted for DCE.dta is the file formatted for the DCE for analysis.</p>
Images (healthcare-1831464)
<p>Histological images of Programmed cell death 1 ligand 1 (CD274, PD-L1) .</p> <p>[Artificial Intelligence Analysis of Ulcerative Colitis Using an Autoimmune Discovery Transcriptomic Panel]</p> <p>There are two types of biopsies: control tissue (n = 5), and ulcerative colitis (n = 15).</p>
Quality of work organization, distress and absenteeism among healthcare workers
<p>Database of the study "Quality of work organization, distress and absenteeism among healthcare workers" to be published.</p>
CONCEPT-COSTS. Compendium of Healthcare Costs in Spain (CONCEPT-COSTS Database)
<p><strong>Technical notes and documentation</strong></p> <p>The Compendium of Healthcare Costs in Spain (CONCEPT-COSTS Database) is a database of Spanish healthcare unit costs estimated from different national sources.</p> <p>Version 3.0 of the CONCEPT-COSTS Database contains costs estimates (expresed in EUR 2024) for a core set of service items commonly used in the chronic health problems evaluated in CONCEPT Project.</p> <p>It is a living document planned to be regularly updated and expanded in terms of the covered service over time.</p> <p><strong>Aims of CONCEPT-COSTS project:</strong></p> <p>CONCEPT-COSTS is part of the coordinated CONCEPT Project, which comprises four subprojects whose objective is to analyse the effectiveness and efficiency of care pathways (CP) in three chronic health problems of high prevalence and socioeconomic impact, which are diabetes mellitus type 2, breast cancer and ischemic stroke. As a common denominator, CONCEPT shares the innovative perspective of focusing its analysis on CP as a key determinant of healthcare adequacy, adherence to treatment, health outcomes and economic consequences. CONCEPT-COSTS' first objective is to complement the results produced by each CONCEPT clinical cohort, with a broad proposal of economic analyses based on real-world data (RWD), including incurred costs, avoidable costs and efficiency evaluation of identified CP. These results will be used to inform the clinical and management decisions about those CP to be promoted or avoided. As a second objective, CONCEPT-COSTS will identify the ethodological and logistical challenges faced by economic evaluations based on RWD, to develop a framework that will include recommendations for improvements related to feasibility, validity and transferability of results.</p> <p>Files included in this publication:</p> <ul> <li>CONCEPT_COSTS_Database_v3.csv</li> <li>CONCEPT_COSTS_Database_v3.html</li> <li>Readme_v3.doc</li> </ul> <p><strong>What's new</strong></p> <ul> <li>Costs updated to 2024</li> <li>Some sources updated</li> </ul>
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