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Patient Safety in Surgery Jun 2024Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO... (Review)
Review
Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Sammelstudie, 1986), the Major Trauma Outcome Study (MTOS) about survival, and the Trauma Audit and Research Network (TARN) pioneered multi-hospital data collection. Large trauma registries, like the German Trauma Registry (TR-DGU) helped improve evidence levels but were still constrained by predefined data sets and limited physiological parameters. The improvement in the understanding of pathophysiological reactions substantiated that decision making about fracture care led to development of patient's tailored dynamic approaches like the Safe Definitive Surgery algorithm. In the future, artificial intelligence (AI) may provide further steps by potentially transforming fracture recognition and/or outcome prediction. The evolution towards flexible decision making and AI-driven innovations may be of further help. The current manuscript summarizes the development of big data from local databases and subsequent trauma registries to AI-based algorithms, such as Parkland Trauma Mortality Index and the IBM Watson Pathway Explorer.
PubMed: 38902828
DOI: 10.1186/s13037-024-00404-0 -
International Journal of Population... 2023Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation...
INTRODUCTION
Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure.
APPROACH AND AIMS
We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs.
RESULTS
Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty.
CONCLUSIONS
The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.
Topics: Humans; United States; Canada; Privacy
PubMed: 38419825
DOI: 10.23889/ijpds.v8i4.2142 -
Molecular Ecology Resources Oct 2023Advances in sequencing technologies and declining costs are increasing the accessibility of large-scale biodiversity genomic datasets. To maximize the impact of these...
Advances in sequencing technologies and declining costs are increasing the accessibility of large-scale biodiversity genomic datasets. To maximize the impact of these data, a careful, considered approach to data management is essential. However, challenges associated with the management of such datasets remain, exacerbated by uncertainty among the research community as to what constitutes best practices. As an interdisciplinary team with diverse data management experience, we recognize the growing need for guidance on comprehensive data management practices that minimize the risks of data loss, maximize efficiency for stand-alone projects, enhance opportunities for data reuse, facilitate Indigenous data sovereignty and uphold the FAIR and CARE Guiding Principles. Here, we describe four fictional personas reflecting differing user experiences with data management to identify data management challenges across the biodiversity genomics research ecosystem. We then use these personas to demonstrate realistic considerations, compromises and actions for biodiversity genomic data management. We also launch the Biodiversity Genomics Data Management Hub (https://genomicsaotearoa.github.io/data-management-resources/), containing tips, tricks and resources to support biodiversity genomics researchers, especially those new to data management, in their journey towards best practice. The Hub also provides an opportunity for those biodiversity researchers whose expertise lies beyond genomics and are keen to advance their data management journey. We aim to support the biodiversity genomics community in embedding data management throughout the research lifecycle to maximize research impact and outcomes.
PubMed: 37873890
DOI: 10.1111/1755-0998.13880 -
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 -
Methods in Molecular Biology (Clifton,... 2024Genetic design automation (GDA) is the use of computer-aided design (CAD) in designing genetic networks. GDA tools are necessary to create more complex synthetic genetic...
Genetic design automation (GDA) is the use of computer-aided design (CAD) in designing genetic networks. GDA tools are necessary to create more complex synthetic genetic networks in a high-throughput fashion. At the core of these tools is the abstraction of a hierarchy of standardized components. The components' input, output, and interactions must be captured and parametrized from relevant experimental data. Simulations of genetic networks should use those parameters and include the experimental context to be compared with the experimental results.This chapter introduces Logical Operators for Integrated Cell Algorithms (LOICA), a Python package used for designing, modeling, and characterizing genetic networks using a simple object-oriented design abstraction. LOICA represents different biological and experimental components as classes that interact to generate models. These models can be parametrized by direct connection to the Flapjack experimental data management platform to characterize abstracted components with experimental data. The models can be simulated using stochastic simulation algorithms or ordinary differential equations with varying noise levels. The simulated data can be managed and published using Flapjack alongside experimental data for comparison. LOICA genetic network designs can be represented as graphs and plotted as networks for visual inspection and serialized as Python objects or in the Synthetic Biology Open Language (SBOL) format for sharing and use in other designs.
Topics: Software; Programming Languages; Gene Regulatory Networks; Algorithms; Synthetic Biology; Automation
PubMed: 38468100
DOI: 10.1007/978-1-0716-3658-9_22 -
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 -
Environmental Monitoring and Assessment Sep 2023Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected...
Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.
Topics: Environmental Monitoring; Natural Resources
PubMed: 37665400
DOI: 10.1007/s10661-023-11788-4 -
Revista Espanola de Cardiologia... Jan 2024Telemedicine enables the remote provision of medical care through information and communication technologies, facilitating data transmission, patient participation,... (Review)
Review
Telemedicine enables the remote provision of medical care through information and communication technologies, facilitating data transmission, patient participation, promotion of heart-healthy habits, diagnosis, early detection of acute decompensation, and monitoring and follow-up of cardiovascular diseases. Wearable devices have multiple clinical applications, ranging from arrhythmia detection to remote monitoring of chronic diseases and risk factors. Integrating these technologies safely and effectively into routine clinical practice will require a multidisciplinary approach. Technological advances and data management will increase telemonitoring strategies, which will allow greater accessibility and equity, as well as more efficient and accurate patient care. However, there are still unresolved issues, such as identifying the most appropriate technological infrastructure, integrating these data into medical records, and addressing the digital divide, which can hamper patients' adoption of remote care. This article provides an updated overview of digital tools for a more comprehensive approach to atrial fibrillation, heart failure, risk factors, and treatment adherence.
Topics: Humans; Cardiovascular Diseases; Heart Failure; Telemedicine; Chronic Disease; Early Diagnosis
PubMed: 37838182
DOI: 10.1016/j.rec.2023.07.009 -
Australian Critical Care : Official... Apr 2024Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving...
BACKGROUND
Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting guidelines recommend the inclusion of data-cleaning procedures; however, little practical guidance exists for how to conduct these procedures.
OBJECTIVES
This paper aimed to provide practical guidance for how to perform and report rigorous data-cleaning procedures.
METHODS
A previously proposed data-quality framework was identified and used to facilitate the description and explanation of data-cleaning procedures. The broader data-cleaning process was broken down into discrete tasks to create a data-cleaning checklist. Examples of the how the various tasks had been undertaken for a previous study using data from the Australia and New Zealand Intensive Care Society Adult Patient Database were also provided.
RESULTS
Data-cleaning tasks were described and grouped according to four data-quality domains described in the framework: data integrity, consistency, completeness, and accuracy. Tasks described include creation of a data dictionary, checking consistency of values across multiple variables, quantifying and managing missing data, and the identification and management of outlier values. The data-cleaning task checklist provides a practical summary of the various aspects of the data-cleaning process and will assist clinician researchers in performing this process in the future.
CONCLUSIONS
Data cleaning is an integral part of any statistical analysis and helps ensure that study results are valid and reproducible. Use of the data-cleaning task checklist will facilitate the conduct of rigorous data-cleaning processes, with the aim of improving the quality of future research.
PubMed: 38600009
DOI: 10.1016/j.aucc.2024.03.004 -
Malaria Journal Aug 2023Continuous distribution channels are effective methods to deliver malaria interventions such as insecticide treated nets (ITNs) to pregnant women attending antenatal...
BACKGROUND
Continuous distribution channels are effective methods to deliver malaria interventions such as insecticide treated nets (ITNs) to pregnant women attending antenatal care clinics and children under five attending immunization visits. Facility-based and provider-based checklists were used during supportive supervision visits to measure the quality of facility-based services and interventions. This study looks at ITN distributions at health facilities in Ghana, with the aim of providing insights on how quality can be measured and monitored.
METHODS
Various quality improvement approaches for malaria services occur in Ghana. Selected indicators were analysed to highlight the similarities and differences of how the approaches measured how well the channel was doing. Generally, the approaches assessed (1) service data management, (2) logistics data management, and (3) observation of service provision (ITN issuance, malaria education, ITN use and care education). Two approaches used a binary (Yes/No) scale, and one used a Likert scale.
RESULTS
Results showed that most data reported to the national HMIS is accurate. Logistics data management remained an issue at health facilities, as results showed scores below average across facility stores, antenatal care, and immunization. Though the supervision approaches differed, overall results indicated that almost all eligible clients received ITNs, data were recorded accurately and reported on-time, and logistics was the largest challenge to optimal distribution through health facilities.
CONCLUSION
The supervision approaches provided valuable insights into the quality of facility-based ITN distribution. Ghana should continue to implement supportive supervision in their malaria agenda, with additional steps needed to improve reporting of collected data and increase the number of facilities visited for supportive supervision and the frequency. There were various supervision approaches used with no clear guidance on how to measure quality of facility-based ITN distribution, so there is also need for the global community to agree on standardized indicators and approaches to measuring quality of facility-based ITN distribution. Additionally, future studies can review the effect of multiple rounds of supervision visits on the quality of ITN distribution as well as understand the facilitators and barriers to scaling up supervision of facility-based ITN distribution.
Topics: Child; Humans; Female; Pregnancy; Ghana; Insecticide-Treated Bednets; Malaria; Pregnant Women; Surveys and Questionnaires; Insecticides
PubMed: 37533064
DOI: 10.1186/s12936-023-04626-y