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International Journal For Equity in... Dec 2019In the European Union (EU), discrimination based on racial and ethnic origin is prohibited under the Racial Equality Directive. Ireland is one of only three EU countries...
BACKGROUND
In the European Union (EU), discrimination based on racial and ethnic origin is prohibited under the Racial Equality Directive. Ireland is one of only three EU countries where a legal duty of equality data collection is placed on public bodies. It provides an important context in which to study ethnic equality monitoring; however no systematic mapping of where it occurs in health information systems has been carried out. The aim of this study is to identify all existing national health and social care data collections with information on ethnicity and to explore how this data has been collected and used.
METHODS
An electronic search of a national catalogue of health and social care data collections (N = 97) was carried out to identify any collections which contained information on ethnicity. Data dictionaries were searched and key informants contacted. For each of the data collections that collected information on ethnicity, data was extracted on the ethnic categories used and how this data is collected; the completeness of ethnicity recording; and other measures related to ethnicity in the data collection. Relevant outputs for these data collections, related to ethnicity, were identified through key informants and electronic searches.
RESULTS
Of the 97 data collections, 14 (14%) collected information on ethnic or cultural background. Country of birth was collected by 10 of these 14 data collections. Most used the ethnic categories in the Census and recommended that ethnicity should be self-identified and not assigned. Reported rates of identification were generally high (≥90%). Data collections which recorded ethnicity tended to be focused on potentially high-risk populations with no routine recording in primary care. There were some examples of where ethnic equality monitoring had informed targeted interventions e.g. vaccination awareness initiatives or cultural training for healthcare staff.
CONCLUSIONS
Despite strong policy and legal imperatives, there is limited data collection of ethnicity in health and social care data collections in Ireland. While there are some examples of where differences by ethnicity have been identified and acted upon, a more coordinated and comprehensive approach to the collection, quality and utilization of ethnicity data is needed to promote health equity.
Topics: Data Collection; Ethnicity; Health Equity; Health Information Systems; Healthcare Disparities; Humans; Ireland
PubMed: 31892328
DOI: 10.1186/s12939-019-1107-y -
Journal of Registry Management 2012A literature review was conducted to identify peer-reviewed articles related to primary/preferred language and interpreter-use data collection practices in hospitals,... (Review)
Review
A literature review was conducted to identify peer-reviewed articles related to primary/preferred language and interpreter-use data collection practices in hospitals, clinics, and outpatient settings to assess its completeness and quality. In January 2011, Embase (Ovid), MEDLINE (Ovid), PubMed, and Web of Science databases were searched for eligible studies. Primary and secondary inclusion criteria were applied to selected eligible articles. This extensive literature search yielded 768 articles after duplicates were removed. After primary and secondary inclusion criteria were applied, 28 eligible articles remained for data abstraction. All 28 articles in this review reported collecting primary/preferred language data, but only 18% (5/28) collected information on interpreter use. This review revealed that there remains variability in the way that primary/preferred language and interpreter use data are collected; all studies used various methodologies for evaluating and abstracting these data. Likewise, the sources from which the data were abstracted differed.
Topics: Data Collection; Electronic Health Records; Humans; Language; Translating
PubMed: 23443456
DOI: No ID Found -
Journal of the Royal Society of Medicine Aug 1993Audit data were collected continuously between February 1988 and July 1991. For the initial period (February 1988-June 1990) data were collected by monitoring of ward...
Audit data were collected continuously between February 1988 and July 1991. For the initial period (February 1988-June 1990) data were collected by monitoring of ward admission and discharge records and by collecting data from operating theatre records whilst complications were noted in a 'complications book' which was kept on the notes trolley. In July 1991, when a computerized system for storing and processing audit data was introduced into the department, the methods of data collection changed. For each patient a proforma was attached to the clinical notes which was filled in at each stage of the hospital stay. On this proforma was a list of possible complications which were ticked, as appropriate, at the time of discharge from hospital. We have reviewed the results of clinical audit during these two periods. The number of operations performed per month fell slightly in the latter period (p = 0.005). However, there was a significant increase in both the number of complications (p < 0.0001) and in the complication rate (p < 0.0001). Further analysis showed that there was a similar increase in the number of recorded major and minor complications, and that this increase was also seen even when changes in medical personnel were accounted for. We suggest that the increased complication rate recorded in the latter period reflects the change in the method of data collection. This has important implications when comparing outcome measures for clinical departments.
Topics: Consultants; Data Collection; Diagnosis-Related Groups; Hospital Records; Hospitals, Teaching; Humans; Medical Audit; Postoperative Complications; United Kingdom; Workload
PubMed: 8078041
DOI: 10.1177/014107689308600809 -
Journal of Medical Internet Research Jun 2016Electronic medical records and electronic data capture (EDC) have changed data collection in clinical and translational research. However, spreadsheet programs, such as...
BACKGROUND
Electronic medical records and electronic data capture (EDC) have changed data collection in clinical and translational research. However, spreadsheet programs, such as Microsoft Excel, are still used as data repository to record and organize patient data for research.
OBJECTIVE
The objective of this study is to assess the efficiency of EDC as against a standard spreadsheet in regards to time to collect data and data accuracy, measured in number of errors after adjudication.
METHODS
This was a crossover study comparing the time to collect data in minutes between EDC and a spreadsheet. The EDC tool used was Research Electronic Data Capture (REDCap), whereas the spreadsheet was Microsoft Excel. The data collected was part of a registry of patients who underwent coronary computed tomography angiography in the emergency setting. Two data collectors with the same experience went over the same patients and collected relevant data on a case report form identical to the one used in our Emergency Department (ED) registry. Data collection tool was switched after the patient that represented half the cohort. For this, the patient cohort was exactly 30 days of our ED coronary Computed Tomography Angiography registry and the point of crossover was determined beforehand to be 15 days. We measured the number of patients admitted, and time to collect data. Accuracy was defined as absence of blank fields and errors, and was assessed by comparing data between data collectors and counting every time the data differed. Statistical analysis was made using paired t -test.
RESULTS
The study included 61 patients (122 observations) and 55 variables. The crossover occurred after the 30th patient. Mean time to collect data using EDC in minutes was 6.2±2.3, whereas using Excel was 8.0±2.0 (P <.001), a difference of 1.8 minutes between both means (22%). The cohort was evenly distributed with 3 admissions in the first half of the crossover and 4 in the second half. We saw 2 (<0.1%) continuous variable typos in the spreadsheet that a single data collector made. There were no blank fields. The data collection tools showed no differences in accuracy of data on comparison.
CONCLUSIONS
Data collection for our registry with an EDC tool was faster than using a spreadsheet, which in turn allowed more efficient follow-up of cases.
Topics: Cross-Over Studies; Data Accuracy; Data Collection; Electronic Health Records; Humans; Internet; Registries
PubMed: 27277523
DOI: 10.2196/jmir.5576 -
Reproductive Health Sep 2015Two recent efforts to quantify the causes of maternal deaths on a global scale generated divergent estimates of abortion-related mortality. Such discrepancies in...
Two recent efforts to quantify the causes of maternal deaths on a global scale generated divergent estimates of abortion-related mortality. Such discrepancies in estimates of abortion-related mortality present an important opportunity to explore unique challenges and opportunities associated with the generation and interpretation of abortion-related mortality estimates. While innovations in primary data collection and estimation methodologies are much needed, at the very least, studies that seek to measure maternal deaths due to abortion should endeavor to improve transparency, acknowledge limitations of data, and contextualize results. As we move towards sustainable development goals beyond 2015, the need for valid and reliable estimates of abortion-related mortality has never been more pressing. The post-MDG development agenda that aims to improve global health, reduce health inequities, and increase accountability, requires new and novel approaches be tested to improve measurement and estimation of abortion-related mortality, as well as incidence, safety and morbidity.
Topics: Abortion, Induced; Data Collection; Female; Global Health; Humans; Maternal Mortality; Pregnancy
PubMed: 26377189
DOI: 10.1186/s12978-015-0064-1 -
Medical Care Jul 2009Microcosting studies collect detailed data on resources used and the value of those resources. Such studies are useful for estimating the cost of new technologies or new...
BACKGROUND
Microcosting studies collect detailed data on resources used and the value of those resources. Such studies are useful for estimating the cost of new technologies or new community-based interventions, for producing estimates in studies that include nonmarket goods, and for studying within-procedure cost variation.
OBJECTIVES
The objectives of this article were to (1) describe basic microcosting methods focusing on quantity data collection; and (2) suggest a research agenda to improve methods in and the interpretation of microcosting.
RESEARCH DESIGN
Examples in the published literature were used to illustrate steps in the methods of gathering data (primarily quantity data) for a microcosting study.
RESULTS
Quantity data collection methods that were illustrated in the literature include the use of (1) administrative databases at single facilities, (2) insurer administrative data, (3) forms applied across multiple settings, (4) an expert panel, (5) surveys or interviews of one or more types of providers; (6) review of patient charts, (7) direct observation, (8) personal digital assistants, (9) program operation logs, and (10) diary data.
CONCLUSIONS
Future microcosting studies are likely to improve if research is done to compare the validity and cost of different data collection methods; if a critical review is conducted of studies done to date; and if the combination of the results of the first 2 steps described are used to develop guidelines that address common limitations, critical judgment points, and decisions that can reduce limitations and improve the quality of studies.
Topics: Cost-Benefit Analysis; Data Collection; Health Care Costs; Health Services Research; Health Surveys; Humans; Medical Records; Observation
PubMed: 19536026
DOI: 10.1097/MLR.0b013e31819bc064 -
Medicine, Health Care, and Philosophy Jun 2016An 'Information Centre' has recently been established by law which has the power to collect, collate and provide access to the medical information for all patients...
"You hoped we would sleep walk into accepting the collection of our data": controversies surrounding the UK care.data scheme and their wider relevance for biomedical research.
An 'Information Centre' has recently been established by law which has the power to collect, collate and provide access to the medical information for all patients treated by the National Health Service in England, whether in hospitals or by General Practitioners. This so-called 'care.data' scheme has given rise to major and ongoing controversies. We will sketch the background of the scheme and look at the responses it has elicited from citizens and medical professionals. In Autumn 2013, NHS England set up a care.data website where citizens could record their concerns regarding the collection of health-related data by the Information Centre. We have reviewed all the comments on this website up until June 2015. We have also analysed the readers' comments on the coverage of the care.data scheme in one of the main national UK newspapers. When discussing the responses of citizens, we will make a distinction between the problems that citizens detect and the solutions they propose. The solutions that are being perceived as the most relevant ones can be summarized as follows: citizens wish to further the common good without being manipulated into doing it, while at the same time being safeguarded against various abuses. The issue of trust turns out to figure prominently. Our analysis of reactions to the scheme in no way pretends to be exhaustive, yet it provides various relevant insights into the concerns identified by citizens as well as medical professionals. These concerns, moreover, have a more general relevance in relation to other contexts of medical data-mining as well as biobank research. Our analysis also offers important pointers as to how those concerns might be addressed.
Topics: Biomedical Research; Confidentiality; Data Collection; Electronic Health Records; Humans; United Kingdom
PubMed: 26280642
DOI: 10.1007/s11019-015-9661-6 -
BMJ (Clinical Research Ed.) Mar 2015There is a strong movement to share individual patient data for secondary purposes, particularly for research. A major obstacle to broad data sharing has been the...
There is a strong movement to share individual patient data for secondary purposes, particularly for research. A major obstacle to broad data sharing has been the concern for patient privacy. One of the methods for protecting the privacy of patients in accordance with privacy laws and regulations is to anonymise the data before it is shared. This article describes the key concepts and principles for anonymising health data while ensuring it remains suitable for meaningful analysis.
Topics: Biomedical Research; Canada; Confidentiality; Data Collection; European Union; Humans; Information Dissemination; Medical Records; United States
PubMed: 25794882
DOI: 10.1136/bmj.h1139 -
BMC Medical Informatics and Decision... Sep 2017Primary care data gathered from Electronic Health Records are of the utmost interest considering the essential role of general practitioners (GPs) as coordinators of... (Review)
Review
BACKGROUND
Primary care data gathered from Electronic Health Records are of the utmost interest considering the essential role of general practitioners (GPs) as coordinators of patient care. These data represent the synthesis of the patient history and also give a comprehensive picture of the population health status. Nevertheless, discrepancies between countries exist concerning routine data collection projects. Therefore, we wanted to identify elements that influence the development and durability of such projects.
METHODS
A systematic review was conducted using the PubMed database to identify worldwide current primary care data collection projects. The gray literature was also searched via official project websites and their contact person was emailed to obtain information on the project managers. Data were retrieved from the included studies using a standardized form, screening four aspects: projects features, technological infrastructure, GPs' roles, data collection network organization.
RESULTS
The literature search allowed identifying 36 routine data collection networks, mostly in English-speaking countries: CPRD and THIN in the United Kingdom, the Veterans Health Administration project in the United States, EMRALD and CPCSSN in Canada. These projects had in common the use of technical facilities that range from extraction tools to comprehensive computing platforms. Moreover, GPs initiated the extraction process and benefited from incentives for their participation. Finally, analysis of the literature data highlighted that governmental services, academic institutions, including departments of general practice, and software companies, are pivotal for the promotion and durability of primary care data collection projects.
CONCLUSION
Solid technical facilities and strong academic and governmental support are required for promoting and supporting long-term and wide-range primary care data collection projects.
Topics: Data Collection; Electronic Health Records; Humans; Primary Health Care
PubMed: 28946908
DOI: 10.1186/s12911-017-0538-x -
BMC Medical Research Methodology Jan 2011In research, diagrams are most commonly used in the analysis of data and visual presentation of results. However there has been a substantial growth in the use of... (Review)
Review
BACKGROUND
In research, diagrams are most commonly used in the analysis of data and visual presentation of results. However there has been a substantial growth in the use of diagrams in earlier stages of the research process to collect data. Despite this growth, guidance on this technique is often isolated within disciplines.
METHODS
A multidisciplinary systematic review was performed, which included 13 traditional healthcare and non-health-focused indexes, non-indexed searches and contacting experts in the field. English-language articles that used diagrams as a data collection tool and reflected on the process were included in the review, with no restriction on publication date.
RESULTS
The search identified 2690 documents, of which 80 were included in the final analysis. The choice to use diagrams for data collection is often determined by requirements of the research topic, such as the need to understand research subjects' knowledge or cognitive structure, to overcome cultural and linguistic differences, or to understand highly complex subject matter. How diagrams were used for data collection varied by the degrees of instruction for, and freedom in, diagram creation, the number of diagrams created or edited and the use of diagrams in conjunction with other data collection methods. Depending on how data collection is structured, a variety of options for qualitative and quantitative analysis are available to the researcher. The review identified a number of benefits to using diagrams in data collection, including the ease with which the method can be adapted to complement other data collection methods and its ability to focus discussion. However it is clear that the benefits and challenges of diagramming depend on the nature of its application and the type of diagrams used.
DISCUSSION/CONCLUSION
The results of this multidisciplinary systematic review examine the application of diagrams in data collection and the methods for analyzing the unique datasets elicited. Three recommendations are presented. Firstly, the diagrammatic approach should be chosen based on the type of data needed. Secondly, appropriate instructions will depend on the approach chosen. And thirdly, the final results should present examples of original or recreated diagrams. This review also highlighted the need for a standardized terminology of the method and a supporting theoretical framework.
Topics: Communication; Data Collection; Humans; Research Design; Statistics as Topic
PubMed: 21272364
DOI: 10.1186/1471-2288-11-11