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PloS One 2021The Protection of Personal Information Act (POPIA) 2013 came into force in South Africa on 1 July 2020. It seeks to strengthen the processing of personal information,...
The Protection of Personal Information Act (POPIA) 2013 came into force in South Africa on 1 July 2020. It seeks to strengthen the processing of personal information, including health information. While POPIA is to be welcomed, there are concerns about the impact it will have on the processing of health information. To ensure that the National Health Laboratory Service [NHLS] is compliant with these new strict processing requirements and that compliance does not negatively impact upon its current screening, treatment, surveillance and research mandate, it was decided to consider the development of a NHLS POPIA Code of Conduct for Personal Health. As part of the process of developing such a Code and better understand the challenges faced in the processing of personal health information in South Africa, 19 semi-structured interviews with stakeholders were conducted between June and September 2020. Overall, respondents welcomed the introduction of POPIA. However, they felt that there are tensions between the strengthening of data protection and the use of personal information for individual patient care, treatment programmes, and research. Respondents reported a need to rethink the management of personal health information in South Africa and identified 5 issues needing to be addressed at a national and an institutional level: an understanding of the importance of personal information; an understanding of POPIA and data protection; improve data quality; improve transparency in data use; and improve accountability in data use. The application of POPIA to the processing of personal health information is challenging, complex, and likely costly. However, personal health information must be appropriately managed to ensure the privacy of the data subject is protected, but equally that it is used as a resource in the individual's and wider public interest.
Topics: Confidentiality; Data Management; Health Records, Personal; Humans; Information Dissemination; Personally Identifiable Information; South Africa
PubMed: 34928950
DOI: 10.1371/journal.pone.0260341 -
Journal of Biomedical Semantics Nov 2023Open Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes,...
BACKGROUND
Open Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes, institutions) and the relationships among them. They present a contextualized view of current research that supports discovery, re-use, reproducibility, monitoring, transparency and omni-comprehensive assessment. A Data Management Plan (DMP) contains information concerning both the research processes and the data collected, generated and/or re-used during a project's lifetime. Automated solutions and workflows that connect DMPs with the actual data and other contextual information (e.g., publications, fundings) are missing from the landscape. DMPs being submitted as deliverables also limit their findability. In an open and FAIR-enabling research ecosystem information linking between research processes and research outputs is essential. ARGOS tool for FAIR data management contributes to the OpenAIRE Research Graph (RG) and utilises its underlying services and trusted sources to progressively automate validation and automations of Research Data Management (RDM) practices.
RESULTS
A comparative analysis was conducted between the data models of ARGOS and OpenAIRE Research Graph against the DMP Common Standard. Following this, we extended ARGOS with export format converters and semantic tagging, and the OpenAIRE RG with a DMP entity and semantics between existing entities and relationships. This enabled the integration of ARGOS machine actionable DMPs (ma-DMPs) to the OpenAIRE OSG, enriching and exposing DMPs as FAIR outputs.
CONCLUSIONS
This paper, to our knowledge, is the first to introduce exposing ma-DMPs in OSGs and making the link between OSGs and DMPs, introducing the latter as entities in the research lifecycle. Further, it provides insight to ARGOS DMP service interoperability practices and integrations to populate the OpenAIRE Research Graph with DMP entities and relationships and strengthen both FAIRness of outputs as well as information exchange in a standard way.
Topics: Humans; Data Management; Reproducibility of Results
PubMed: 37919767
DOI: 10.1186/s13326-023-00297-5 -
Journal of the Medical Library... Apr 2021While data management (DM) is an increasing responsibility of doctorally prepared nurses, little is understood about how DM education and expectations are reflected...
OBJECTIVE
While data management (DM) is an increasing responsibility of doctorally prepared nurses, little is understood about how DM education and expectations are reflected within student handbooks. The purpose of this study was to assess the inclusion of DM content within doctoral nursing student handbooks.
METHODS
A list of 346 doctoral programs was obtained from the American Association of Colleges of Nursing (AACN). Program websites were searched to locate program handbooks, which were downloaded for analysis. A textual review of 261 handbooks from 215 institutions was conducted to determine whether DM was mentioned and, if so, where the DM content was located. Statistical analysis was performed to compare the presence of DM guidance by type of institution, Carnegie Classification, and the type of doctoral program handbook.
RESULTS
A total of 1,382 codes were identified across data life cycle stages, most commonly in the handbooks' project requirements section. The most frequent mention of DM was in relation to collecting and analyzing data; the least frequent related to publishing and sharing data and preservation. Significant differences in the frequency and location of codes were identified by program type and Carnegie Classification.
CONCLUSIONS
Nursing doctoral program handbooks primarily address collecting and analyzing data during student projects. Findings suggest limited education about, and inclusion of, DM life cycle content, especially within DNP programs. Collaboration between nursing faculty and librarians and nursing and library professional organizations is needed to advance the adoption of DM best practices for preparing students in their future roles as clinicians and scholars.
Topics: Data Management; Education, Nursing, Graduate; Faculty, Nursing; Humans; Physicians; Students, Nursing
PubMed: 34285667
DOI: 10.5195/jmla.2021.1115 -
Journal of Biomolecular Techniques : JBT Jul 2021The Biomolecular Research Center at Boise State University is a research core facility that supports the study of biomolecules with an emphasis on protein structure and...
The Biomolecular Research Center at Boise State University is a research core facility that supports the study of biomolecules with an emphasis on protein structure and function, molecular interactions, and imaging. The mission of the core is to facilitate access to instrumentation that might otherwise be unavailable because of the cost, training for new users, and scientific staff with specialized skills to support early-stage investigators, as well as more established senior investigators. Data collection and management of users and their research output is essential to understand the impact of the center on the research environment and research productivity. However, challenges are often encountered when trying to fully quantify the impact of a core facility on the institution, as well as on the career success of individual investigators. This challenge can be exacerbated under the conditions of unprecedented growth in biomedical research and shared core facility use that has been experienced at Boise State University, an institution of emerging research excellence. Responding to these challenges required new approaches to information management, reporting, assessment, and evaluation. Our specific data management, evaluation, and assessment challenges included ) collection and management of annual reporting information from investigators, staff, and students in a streamlined manner that did not lead to reporting fatigue; ) application of software for analyzing synergy among programs' management strategy and investigator success; and ) consolidation of core facility management, billing, and reporting capabilities into 1 cohesive system. The data management tools adopted had a beneficial effect by saving time, reducing administrative burden, and streamlining reporting. Practices implemented for data management have facilitated effective evaluation and future program planning. The substantial burden of assessment requirements necessitates early consideration of a strategy for data management to allow assessment of impact.
Topics: Biomedical Research; Data Management; Humans; Research Personnel
PubMed: 34121933
DOI: 10.7171/jbt.20-3203-002 -
Frontiers in Public Health 2021ODK provides software and standards that are popular solutions for off-grid electronic data collection and has substantial code overlap and interoperability with a...
ODK provides software and standards that are popular solutions for off-grid electronic data collection and has substantial code overlap and interoperability with a number of related software products including CommCare, Enketo, Ona, SurveyCTO, and KoBoToolbox. These tools provide open-source options for off-grid use in public health data collection, management, analysis, and reporting. During the 2018-2020 Ebola epidemic in the North Kivu and Ituri regions of Democratic Republic of Congo, we used these tools to support the DRC Ministère de la Santé RDC and World Health Organization in their efforts to administer an experimental vaccine (VSV-Zebov-GP) as part of their strategy to control the transmission of infection. New functions were developed to facilitate the use of ODK, Enketo and in large scale data collection, aggregation, monitoring, and near-real-time analysis during clinical research in health emergencies. We present enhancements to ODK that include a built-in audit-trail, a framework and companion app for biometric registration of ISO/IEC 19794-2 fingerprint templates, enhanced performance features, better scalability for studies featuring millions of data form submissions, increased options for parallelization of research projects, and pipelines for automated management and analysis of data. We also developed novel encryption protocols for enhanced web-form security in Enketo. Against the backdrop of a complex and challenging epidemic response, our enhanced platform of open tools was used to collect and manage data from more than 280,000 eligible study participants who received VSV-Zebov-GP under informed consent. These data were used to determine whether the VSV-Zebov-GP was safe and effective and to guide daily field operations. We present open-source developments that make electronic data management during clinical research and health emergencies more viable and robust. These developments will also enhance and expand the functionality of a diverse range of data collection platforms that are based on the ODK software and standards.
Topics: Data Management; Electronics; Epidemics; Hemorrhagic Fever, Ebola; Humans
PubMed: 34805059
DOI: 10.3389/fpubh.2021.665584 -
BMC Health Services Research Nov 2023This study aims to develop a scale that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment.
PURPOSE
This study aims to develop a scale that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment.
METHOD
Within the scope of the study, in the scale development process; The stages of literature review, creation of items, getting expert opinion, conducting a pilot study, ensuring construct and criterion validity, and reliability analyses were carried out. The literature was searched for the formation of the question items. To evaluate the created question items, expert opinion was taken, and the question items were arranged according to the feedback from the experts. In line with the study's purpose and objectives, the focus group consisted of individuals aged 18 and above within the community. The convenience sampling method was employed for sample selection. Data were collected using an online survey conducted through Google Forms. Before commencing the survey, participants were briefed on the research's content. A pilot study was conducted with 30 participants, and as a result of the feedback from the participants, eliminations were made in the question items and the scale was made ready for application. The research was conducted by reference to 812 participants in the community. Expert evaluations of the question items were obtained, and a pilot study was conducted. A sociodemographic information form, a scale developed by the researcher, Norman and Skinner's e-Health Literacy Scale, and the Mobile Health and Personal Health Record Management Scale were used as data collection tools.
RESULTS
The content validity of the research was carried out by taking expert opinions and conducting a pilot study. Exploratory factor analysis and confirmatory factor analysis were performed to ensure construct validity. The total variance explained by the scale was 60.43%. The results of confirmatory factor analysis indicated that the 20-Item 5-factor structure exhibited good fit values. According to the analysis of criterion validity, there are significant positive correlations among the Data Management in the Digital Health Environment Scale, Norman and Skinner's e-Health Literacy Scale and the Mobile Health and Personal Health Record Management Scale (p < 0.01; r = .669, .378). The Cronbach's alpha value of the scale is .856, and the test-retest reliability coefficient is .909.
CONCLUSION
The Data Management in the Digital Health Environment Scale is a valid and reliable measurement tool that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment.
Topics: Humans; Data Management; Reproducibility of Results; Pilot Projects; Surveys and Questionnaires; Personal Satisfaction; Psychometrics
PubMed: 37964225
DOI: 10.1186/s12913-023-10205-3 -
Current Opinion in Biotechnology Oct 2020Biological samples such as tissues, blood and other body fluids, plants or seeds, prokaryotic and eukaryotic cells or isolated biomolecules as well as associated data... (Review)
Review
Biological samples such as tissues, blood and other body fluids, plants or seeds, prokaryotic and eukaryotic cells or isolated biomolecules as well as associated data are the essential raw material for research and development in medicine, biotechnology and agriculture. The collection, processing, preservation, and storage of these resources, in addition to provision of access, are key activities of biobanks or biological resource centres. Biobanks have to ensure proper quality of samples and data, ethical and legal compliance as well as transparent and efficient access procedures. In this context the review places special emphasis on pre-analytical procedures and international standards, which are essential to improving analytical data reliability and reproducibility, as well as on the increasing importance of data management. These requirements of biobanks are demonstrated using the example of pathogen-containing and microbiome biobanks, and refer to needs in cancer research and development.
Topics: Biological Science Disciplines; Biological Specimen Banks; Biomedical Research; Containment of Biohazards; Data Management; Precision Medicine; Reference Standards; Reproducibility of Results
PubMed: 31896493
DOI: 10.1016/j.copbio.2019.12.004 -
Online sleep diaries: considerations for system development and recommendations for data management.Sleep Oct 2023To present development considerations for online sleep diary systems that result in robust, interpretable, and reliable data; furthermore, to describe data management...
STUDY OBJECTIVES
To present development considerations for online sleep diary systems that result in robust, interpretable, and reliable data; furthermore, to describe data management procedures to address common data entry errors that occur despite those considerations.
METHODS
The online sleep diary capture component of the Sleep Healthy Using the Internet (SHUTi) intervention has been designed to promote data integrity. Features include diary entry restrictions to limit retrospective bias, reminder prompts and data visualizations to support user engagement, and data validation checks to reduce data entry errors. Despite these features, data entry errors still occur. Data management procedures relying largely on programming syntax to minimize researcher effort and maximize reliability and replicability. Presumed data entry errors are identified where users are believed to have incorrectly selected a date or AM versus PM on the 12-hour clock. Following these corrections, diaries are identified that have unresolvable errors, like negative total sleep time.
RESULTS
Using the example of one of our fully-powered, U.S. national SHUTi randomized controlled trials, we demonstrate the application of these procedures: of 45,598 total submitted diaries, 487 diaries (0.01%) required modification due to date and/or AM/PM errors and 27 diaries (<0.001%) were eliminated due to unresolvable errors.
CONCLUSION
To secure the most complete and valid data from online sleep diary systems, it is critical to consider the design of the data collection system and to develop replicable processes to manage data.
CLINICAL TRIAL REGISTRATION
Sleep Healthy Using The Internet for Older Adult Sufferers of Insomnia and Sleeplessness (SHUTiOASIS); https://clinicaltrials.gov/ct2/show/NCT03213132; ClinicalTrials.gov ID: NCT03213132.
Topics: Humans; Aged; Data Management; Retrospective Studies; Reproducibility of Results; Sleep; Sleep Initiation and Maintenance Disorders
PubMed: 37480840
DOI: 10.1093/sleep/zsad199 -
Journal of Pharmaceutical Sciences Apr 2020The process of assembling regulatory documents for submission to multiple global health agencies can present a repetitive cycle of authoring, editing, and data... (Review)
Review
The process of assembling regulatory documents for submission to multiple global health agencies can present a repetitive cycle of authoring, editing, and data verification, which increases in complexity as changes are made for approved products, particularly from a chemistry, manufacturing, and controls (CMC) perspective. Currently, pharmaceutical companies rely on a workflow that involves manual CMC change management across documents. Similarly, when regulators review submissions, they provide feedback and insight into regulatory decision making in a narrative format. As accelerated review pathways are increasingly used and pressure mounts to bring products to market quickly, innovative solutions for assembling, distributing, and reviewing regulatory information are being considered. Structured content management (SCM) solutions, in which data are collated into centrally organized content blocks for use across different documents, may aid in the efficient processing of data and create opportunities for automation and machine learning in its interpretation. The US Food and Drug Administration (FDA) has recently created initiatives that encourage application of SCM for CMC data, though many challenges could impede their success and efficiency. The goal is for industry and health authorities to collaborate in the development of SCM for CMC applications, to potentially streamline compilation of quality data in regulatory submissions.
Topics: Commerce; Data Management; United States; United States Food and Drug Administration; Workflow
PubMed: 32004537
DOI: 10.1016/j.xphs.2020.01.020 -
Radiology Apr 2020Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical...
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.
Topics: Algorithms; Data Collection; Data Management; Diagnostic Imaging; Humans; Machine Learning
PubMed: 32068507
DOI: 10.1148/radiol.2020192224