Improving the Quality of Healthcare Research Data Sets

2011 ◽  
pp. 305-320
Author(s):  
Biswadip Ghosh
Keyword(s):  
Research Study ◽  
Clinical Care ◽  
Healthcare Research ◽  
Research Data ◽  
Data Sources ◽  
Data Sets ◽  
Team Members ◽  
Secondary Sources ◽  
Patient Health

The goal of many healthcare research projects and evidence based medicine programs within healthcare organizations is to support clinical care team members by mining evidence from patient outcomes to support future treatment recommendations. In these research studies, the data is often extracted from secondary sources such as patient health records, benefits systems, and other nonresearch data sources. Good data is important to facilitate a good research study and to support clinical decisions using the results. Often multiple applicable healthcare data sources are available for a research study, some of which may be internal to the organization, while others may be external, such as state or national databases. This chapter attempts to develop an understanding of how the quality of data for healthcare research data sets can be established and improved when using secondary data sources, such as clinical or benefits databases, which were created without primary intentions for research use.

Improving the Quality of Healthcare Research Data Sets

2011 ◽  
pp. 1826-1841
Author(s):  
Biswadip Ghosh
Keyword(s):  
Research Study ◽  
Clinical Care ◽  
Healthcare Research ◽  
Research Data ◽  
Data Sources ◽  
Data Sets ◽  
Team Members ◽  
Secondary Sources ◽  
Patient Health

The goal of many healthcare research projects and evidence based medicine programs within healthcare organizations is to support clinical care team members by mining evidence from patient outcomes to support future treatment recommendations. In these research studies, the data is often extracted from secondary sources such as patient health records, benefits systems, and other nonresearch data sources. Good data is important to facilitate a good research study and to support clinical decisions using the results. Often multiple applicable healthcare data sources are available for a research study, some of which may be internal to the organization, while others may be external, such as state or national databases. This chapter attempts to develop an understanding of how the quality of data for healthcare research data sets can be established and improved when using secondary data sources, such as clinical or benefits databases, which were created without primary intentions for research use.

scholarly journals Improving the Quality of Linked Data Using Statistical Distributions

2018 ◽  
pp. 1638-1664 ◽  
Author(s):  
Heiko Paulheim ◽  
Christian Bizer
Keyword(s):  
Knowledge Base ◽  
Linked Data ◽  
Relational Databases ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sources ◽  
Data Sets ◽  
Statistical Distributions ◽  
The Web

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

scholarly journals Research Clinical Trial- A Review

2021 ◽  
Vol 01 (09) ◽  
Author(s):  
Dr. Ishita Attri ◽  
Keyword(s):  
Clinical Trial ◽  
Healthcare Professionals ◽  
Research Study ◽  
Research Protocol ◽  
Team Members ◽  
Roles And Responsibilities ◽  
Post Marketing Surveillance ◽  
Post Marketing

Majority of healthcare professionals are struggling with conducting and writing a protocol for a research study. Thus, the purpose of this article is to summarize significant steps and necessary guidelines for producing a standard research protocol, roles and responsibilities of various team members involved in the study, and conduction of actual clinical trial including its initiation, phases (I-III), termination or post-marketing surveillance phase. It is important to note that the quality of a clinical trial largely depends on the protocol to achieve success in the research study.

Desain Pendidikan Inklusi di Masa Pandemi Covid-19

2021 ◽  
Vol 3 (2) ◽  
pp. 246-261
Author(s):  
Hartin Kurniawati ◽  
Ika Rahayu Satyaninrum ◽  
Fifin Ari Astutik
Keyword(s):  
Inclusive Education ◽  
Student Development ◽  
Secondary Data ◽  
School Curriculum ◽  
Research Data ◽  
Data Sources ◽  
Social Emotional ◽  
Secondary Sources ◽  
Analysis Technique ◽  
Conducting Research

This study aims to determine the design of inclusive education during the COVID-19 pandemic. The research method used is a qualitative approach. In conducting research, data sources are needed, in data collection using primary and secondary sources. Primary sources are data sources that are obtained directly from informants, in this case the principal and homeroom teacher and special companion teachers. Meanwhile, secondary data is in the form of several documents required for completeness of research data. The data analysis technique used is descriptive qualitative through data reduction, data presentation and drawing conclusions. The results showed that inclusive education served all the needs of students regardless of differences. This can be seen from: (1) the composition of the class consists of various aspects of diversity, (2) each student is given treatment according to their needs, (3) SPMB is based on the child's age and observations of student development are made, (4) implements a modified K13 curriculum with the Nature school curriculum that is modified according to the needs of students, (5) learning develops aspects: cognitive, language, physical-motor, social-emotional, and moral and (6) learning evaluation is carried out every day and once a week, (7) Learning during a pandemic is through video calls, WhatsApp with a duration of 1 hour and uses the question and answer method and demonstrations.  

A failure before analysis: the soup to nuts of preparing for multicountry analyses

2016 ◽  
Vol 23 (4) ◽  
pp. 590-612 ◽  
Author(s):  
Charlotte M. Karam ◽  
David A. Ralston
Keyword(s):  
Design Methodology ◽  
Cross Cultural ◽  
Data Sets ◽  
Review Of The Literature ◽  
Empirical Relationships ◽  
Content Type ◽  
Team Members ◽  
Building Trust ◽  
Key Issues

Purpose A large and growing number of researchers set out to cross-culturally examine empirical relationships. The purpose of this paper is to provide researchers, who are new to multicountry investigations, a discussion of the issues that one needs to address in order to be properly prepared to begin the cross-cultural analyses of relationships. Design/methodology/approach Thus, the authors consider two uniquely different but integrally connected challenges to getting ready to conduct the relevant analyses for just such multicountry studies. The first challenge is to collect the data. The second challenge is to prepare (clean) the collected data for analysis. Accordingly, the authors divide this paper into two parts to discuss the steps involved in both for multicountry studies. Findings The authors highlight the fact that in the process of collecting, there are a number of key issues that should be kept in mind including building trust with new team members, leading the team, and determining sufficient contribution of team members for authorship. Subsequently, the authors draw the reader’s attention to the equally important, but often-overlooked, data cleaning process and the steps that constitute it. This is important because failing to take serious the quality of the data can lead to violations of assumptions and mis-estimations of parameters and effects. Originality/value This paper provides a useful guide to assist researchers who are engaged in data collection and cleaning efforts with multiple country data sets. The review of the literature indicated how truly important a guideline of this nature is, given the expanding nature of cross-cultural investigations.

scholarly journals Quality indicators for clinical care of patients with hypertension: scoping review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e026167
Author(s):  
Hanevi Djasri ◽  
Sekar Laras ◽  
Adi Utarini
Keyword(s):  
Quality Indicators ◽  
Scoping Review ◽  
Medical Information ◽  
Clinical Care ◽  
Research Question ◽  
Grey Literature ◽  
Healthcare Research ◽  
Health Coverage ◽  
Medical Subject Headings

IntroductionCardiovascular diseases impose significant financial impact on countries implementing universal health coverage (UHC). Hypertension is a primary disease that will lead to more severe conditions without adequate clinical care. The quality of its clinical care must be well assessed in order to measure the effective coverage of people with hypertension in UHC. This study aims to identify indicators that can be used to measure the quality of clinical care provided to patients with hypertension in healthcare facilities.Methods and analysisThis review will be conducted using the six stages of the scoping review method: identifying the research question, searching for relevant studies, selecting studies, charting the data, collating, summarising and reporting the results, and conducting consultation exercises. The review will include all quality indicators used for clinical care of patients with hypertension at any healthcare facility. All research designs will be included. Search strategies are developed using the medical subject headings and keywords related to hypertension and quality indicators. Several electronic databases, that is, MEDLINE, Cochrane, Scopus and Web of Science, including clinical-guideline databases from Agency for Healthcare Research and Quality, National Institute for Health and Care Excellence, National Health Service Evidence and Medical Information Network Distribution Service, and also grey literature will be used. Two researchers will screen the titles and abstracts and review the full text of selected articles to determine the final inclusion. The results will be summarised quantitatively, using numerical counts, and qualitatively, using thematic analysis. The data extraction will include a complete list and detailed profile of all indicators. Stakeholder consultation will be conducted at the beginning and after preliminary results to translate findings to the potential knowledge users.Ethical considerations and disseminationReviews of published articles are considered secondary analysis and do not need ethical approval. The findings will be disseminated through various strategies, such as policy briefs, conferences, peer-reviewed journals, and on selected websites relevant to the subject.Study statusData collection for the scoping review will include publications up to May 2019, and the analysis will start in June 2019.

scholarly journals The Dutch Parelsnoer Institute Cerebrovascular Disease Initiative: a retrospective study of the effects of integrating clinical care and research on costs and quality of care in patients with ischaemic stroke

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e028290
Author(s):  
Liselore A Mensing ◽  
Jaap Kappelle ◽  
Julie E Buijs ◽  
Gert-Jan Luijckx ◽  
Hendrik Koffijberg ◽  
...  
Keyword(s):  
Cohort Study ◽  
Quality Of Care ◽  
Cerebrovascular Disease ◽  
Cerebral Ischaemia ◽  
Clinical Care ◽  
Research Data ◽  
Mean Difference ◽  
Time Efficiency ◽  
Discharge Letter

IntroductionThe Dutch Parelsnoer Institute (PSI) is a collaboration between all university medical centres in which clinical data, imaging and biomaterials are prospectively and uniformly collected for research purposes. The PSI has the ambition to integrate data collected in the context of clinical care with data collected primarily for research purposes. We aimed to evaluate the effects of such integrated registration on costs, efficiency and quality of care.MethodsWe retrospectively included patients with cerebral ischaemia of the PSI Cerebrovascular Disease Consortium at two participating centres, one applying an integrated approach on registration of clinical and research data and another with a separate method of registration. We determined the effect of integrated registration on (1) costs and time efficiency using a comparative matched cohort study in 40 patients and (2) quality of the discharge letter in a retrospective cohort study of 400 patients.ResultsA shorter registration time (mean difference of −4.6 min, SD 4.7, p=0.001) and a higher quality score of discharge letters (mean difference of 856 points, SD 40.8, p<0.001) was shown for integrated registration compared with separate registration. Integrated registration of data of 300 patients per year would save around €700 salary costs per year.ConclusionIntegrated registration of clinical and research data in patients with cerebral ischaemia is associated with some decrease in salary costs, while at the same time, increased time efficiency and quality of the discharge letter are accomplished. Thus, we recommend integrated registration of clinical and research data in centres with high-volume registration only, due to the initial investments needed to adopt the registration software.

scholarly journals Content Curation for Research: A Framework for Building a “Data Museum”

2015 ◽  
Vol 10 (2) ◽  
pp. 58-68
Author(s):  
San Cannon
Keyword(s):  
Research Data ◽  
Data Sources ◽  
Digital Content ◽  
Data Sets ◽  
Digital Curation ◽  
Academic Researchers ◽  
Definition Of ◽  
Digital Assets ◽  
Support Research

In the current digital age, data are everywhere and are continually being created, collected and otherwise captured by a range of users for a variety of applications. Curating digital content is a growing concern both for business users and academic researchers. Selecting, collecting, preserving and archiving digital assets, especially research data sets, are important steps in the research life cycle and can help expand the boundaries of research by allowing data to be reused. Creating research data sets often starts with selecting input data sources; in this age of new or “big” data, that choice set keeps expanding, thereby making it more difficult and time consuming to discover and understand the vast data landscape when beginning an empirical research project. This paper proposes an approach to make finding and learning about data easier and less time-consuming for researchers. While cognizant of the role of digital curation for research data sets, we focus on the traditional “museum” definition of curation to outline how data-oriented content curation can support research. The process of selecting, evaluating and presenting information about potential data inputs can help researchers more easily understand how certain data sets are used and better determine which data sources might be fit for their purposes. Although the paper draws on examples from economics citing U.S. data, the techniques could be used across disciplines and countries.

scholarly journals Acceptability of a German multicentre healthcare research study: a survey of research personnels’ attitudes, experiences and work load

BMJ Open ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. e023166
Author(s):  
Sebastian Blecha ◽  
Susanne Brandstetter ◽  
Frank Dodoo-Schittko ◽  
Magdalena Brandl ◽  
Bernhard M Graf ◽  
...  
Keyword(s):  
Data Collection ◽  
Online Survey ◽  
Research Study ◽  
Data Entry ◽  
Healthcare Research ◽  
Good Time ◽  
Documentation System ◽  
Additional Time ◽  
Study Results

ObjectivesThe DACAPO study as a multicentre nationwide observational healthcare research study investigates the influence of quality of care on the quality of life in patients with acute respiratory distress syndrome. The aim of this study was to investigate the acceptability to the participating research personnels by assessing attitudes, experiences and workload associated with the conduct of the DACAPO study.Design, setting and participantsA prospective anonymous online survey was sent via email account to 169 participants in 65 study centres. The questionnaire included six different domains: (1) training for performing the study; (2) obtaining informed consent; (3) data collection; (4) data entry using the online documentation system; (5) opinion towards the study and (6) personal data. Descriptive data analysis was carried out.ResultsA total of 78 participants took part (46%) in the survey, 75 questionnaires (44%) could be evaluated. 51% were senior medical specialists. 95% considered the time frame of the training as appropriate and the presentation was rated by 93% as good or very good. Time effort for obtaining consent, data collection and entry was considered by 41% as a burden. Support from the coordinating study centre was rated as good or very good by more than 90% of respondents. While the DACAPO study was seen as scientifically relevant by 81%, only 45% considered the study results valuable for improving patient care significantly.ConclusionCollecting feedback on the acceptability of a large multicentre healthcare research study provided important insights. Recruitment and data acquisition was mainly performed by physicians and often regarded as additional time burden in clinical practice. Reducing the amount of data collection and simplifying data entry could facilitate the conduct of healthcare research studies and could improve motivation of researchers in intensive care medicine.Trial registration numberNCT02637011; Pre-results.

scholarly journals Improving the Quality of Linked Data Using Statistical Distributions

2014 ◽  
Vol 10 (2) ◽  
pp. 63-86 ◽  
Author(s):  
Heiko Paulheim ◽  
Christian Bizer
Keyword(s):  
Knowledge Base ◽  
Linked Data ◽  
Relational Databases ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sources ◽  
Data Sets ◽  
Statistical Distributions ◽  
The Web

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

Sign in / Sign up

Export Citation Format

Share Document