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Studies in Health Technology and... Jun 2023The objective of this study, as part of the European HealthyCloud project, has been to analyse the data management mechanisms of representative data hubs in Europe and...
The objective of this study, as part of the European HealthyCloud project, has been to analyse the data management mechanisms of representative data hubs in Europe and identify whether they comply with an adequate adoption of FAIR principles that will enable data discovery. A dedicated consultation survey was performed, and the analysis of the results allowed to generate a set of comprehensive recommendations and best practices so that these data hubs can be integrated into a data sharing ecosystem such as the future European Health Research and Innovation Cloud.
Topics: Ecosystem; Data Management; Europe; Referral and Consultation
PubMed: 37386986
DOI: 10.3233/SHTI230452 -
Respiratory Care Jan 2024Research studies generate data in various forms. Data can be quantitative or qualitative. Research involving human subjects requires protection of data to ensure... (Review)
Review
Research studies generate data in various forms. Data can be quantitative or qualitative. Research involving human subjects requires protection of data to ensure privacy. Various regulations and local policies need to be followed to ensure data security. Data management plans are critical for effective data stewardship and include details plan on data collection, management, storage, and formatting. This paper will review data collection tools, data security strategies, file management, data storage, government regulations, prepping data for analysis, reference management, and file management.
Topics: Humans; Computer Security; Data Management; Research Subjects
PubMed: 37875318
DOI: 10.4187/respcare.11578 -
Studies in Health Technology and... May 2023Extensive workflows have been designed to FAIRify data from various domains. These tend to be cumbersome and overwhelming. This work summarises our own experiences with...
Extensive workflows have been designed to FAIRify data from various domains. These tend to be cumbersome and overwhelming. This work summarises our own experiences with FAIRification in health data management and provides simple steps that can be implemented to achieve a relatively low but improved level of FAIRness. The steps lead the data steward to register the data in a repository and then annotate it with the metadata recommended by that repository. It further leads the data steward to provide the data in a machine-readable format using an established and accessible language, establish a well-defined framework to describe and structure the (meta)data as well as publish the (meta)data. We hope that following the simple roadmap described in this work helps to demystify the FAIR data principles in the health domain.
Topics: Data Management; Metadata
PubMed: 37203700
DOI: 10.3233/SHTI230155 -
Journal of Medical Internet Research May 2019Blockchain is emerging as an innovative technology for secure data management in many areas, including medical practice. A distributed blockchain network is tolerant...
BACKGROUND
Blockchain is emerging as an innovative technology for secure data management in many areas, including medical practice. A distributed blockchain network is tolerant against network fault, and the registered data are resistant to tampering and revision. The technology has a high affinity with digital medicine like mobile health (mHealth) and provides reliability to the medical data without labor-intensive third-party contributions. On the other hand, the reliability of the medical data is not insured before registration to the blockchain network. Furthermore, there are issues with regard to how the clients' mobile devices should be dealt with and authenticated in the blockchain network in order to avoid impersonation.
OBJECTIVE
The aim of the study was to design and validate an mHealth system that enables the compatibility of the security and scalability of the medical data using blockchain technology.
METHODS
We designed an mHealth system that sends medical data to the blockchain network via relay servers. The architecture provides scalability and convenience of operation of the system. In order to ensure the reliability of the data from clients' mobile devices, hash values with chain structure (client hashchain) were calculated in the clients' devices and the results were registered on the blockchain network.
RESULTS
The system was applied and deployed in mHealth for insomnia treatment. Clinical trials for mHealth were conducted with insomnia patients. Medical data of the recruited patients were successfully registered with the blockchain network via relay servers along with the hashchain calculated on the clients' mobile devices. The correctness of the data was validated by identifying illegal data, which were made by simulating fraudulent access.
CONCLUSIONS
Our proposed mHealth system, blockchain combined with client hashchain, ensures compatibility of security and scalability in the data management of mHealth medical practice.
TRIAL REGISTRATION
UMIN Clinical Trials Registry UMIN000032951; https://upload.umin.ac.jp/cgi-open- bin/ctr_e/ctr_view.cgi?recptno=R000037564 (Archived by WebCite at http://www.webcitation.org/78HP5iFIw).
Topics: Blockchain; Data Management; Humans; Reproducibility of Results; Research Design; Telemedicine; Validation Studies as Topic
PubMed: 31099337
DOI: 10.2196/13385 -
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 -
Plastic and Reconstructive Surgery Oct 2023Blockchain technology has attracted substantial interest in recent years, most notably for its effect on global economics through the advent of cryptocurrency. Within...
Blockchain technology has attracted substantial interest in recent years, most notably for its effect on global economics through the advent of cryptocurrency. Within the health care domain, blockchain technology has been actively explored as a tool for improving personal health data management, medical device security, and clinical trial management. Despite a strong demand for innovation and cutting-edge technology in plastic surgery, integration of blockchain technologies within plastic surgery is in its infancy. Recent advances and mainstream adoption of blockchain are gaining momentum and have shown significant promise for improving patient care and information management. In this article, the authors explain what defines a blockchain and discuss its history and potential applications in plastic surgery. Existing evidence suggests that blockchain can enable patient-centered data management, improve privacy, and provide additional safeguards against human error. Integration of blockchain technology into clinical practice requires further research and development to demonstrate its safety and efficacy for patients and providers.
Topics: Humans; Blockchain; Delivery of Health Care; Privacy; Data Management; Computer Security
PubMed: 36917745
DOI: 10.1097/PRS.0000000000010409 -
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 -
Histochemistry and Cell Biology Sep 2023Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data...
Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data sharing practices. Due to the size and types of data they produce, bioimaging researchers confront specific challenges in aligning their data with FAIR principles, ensuring that it is findable, accessible, interoperable, and reusable. Although not always recognized by researchers, libraries can, and have been, offering support for data throughout its lifecycle by assisting with data management planning, acquisition, processing and analysis, and sharing and reuse of data. Libraries can educate researchers on best practices for research data management and sharing, facilitate connections to experts by coordinating sessions using peer educators and appropriate vendors, help assess the needs of different researcher groups to identify challenges or gaps, recommend appropriate repositories to make data as accessible as possible, and comply with funder and publisher requirements. As a centralized service within an institution, health sciences libraries have the capability to bridge silos and connect bioimaging researchers with specialized data support across campus and beyond.
Topics: Data Management; Information Dissemination
PubMed: 37247072
DOI: 10.1007/s00418-023-02198-1 -
BMC Research Notes Jan 2022Research data management (RDM) is the cornerstone of a successful research project, and yet it often remains an underappreciated art that gets overlooked in the hustle...
Research data management (RDM) is the cornerstone of a successful research project, and yet it often remains an underappreciated art that gets overlooked in the hustle and bustle of everyday project management even when required by funding bodies. If researchers are to strive for reproducible science that adheres to the principles of FAIR, then they need to manage the data associated with their research projects effectively. It is imperative to plan your RDM strategies early on, and setup your project organisation before embarking on the work. There are several different factors to consider: data management plans, data organisation and storage, publishing and sharing your data, ensuring reproducibility and adhering to data standards. Additionally it is important to reflect upon the ethical implications that might need to be planned for, and adverse issues that may need a mitigation strategy. This short article discusses these different areas, noting some best practices and detailing how to incorporate these strategies into your work. Finally, the article ends with a set of top ten tips for effective research data management.
Topics: Data Management; Humans; Publishing; Reproducibility of Results; Research Design; Research Personnel
PubMed: 35063017
DOI: 10.1186/s13104-022-05908-5