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Journal of Medical Internet Research Sep 2021Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high... (Review)
Review
BACKGROUND
Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high priority to understand and address the causes of disease.
OBJECTIVE
This study aims to provide an outline of the use of genomic data alongside routinely collected data in health research to date. As this field prepares to move forward, it is important to take stock of the current state of play in order to highlight new avenues for development, identify challenges, and ensure that adequate data governance models are in place for safe and socially acceptable progress.
METHODS
We conducted a literature review to draw information from past studies that have used genomic and routinely collected data and conducted interviews with individuals who use these data for health research. We collected data on the following: the rationale of using genomic data in conjunction with routinely collected data, types of genomic and routinely collected data used, data sources, project approvals, governance and access models, and challenges encountered.
RESULTS
The main purpose of using genomic and routinely collected data was to conduct genome-wide and phenome-wide association studies. Routine data sources included electronic health records, disease and death registries, health insurance systems, and deprivation indices. The types of genomic data included polygenic risk scores, single nucleotide polymorphisms, and measures of genetic activity, and biobanks generally provided these data. Although the literature search showed that biobanks released data to researchers, the case studies revealed a growing tendency for use within a data safe haven. Challenges of working with these data revolved around data collection, data storage, technical, and data privacy issues.
CONCLUSIONS
Using genomic and routinely collected data holds great promise for progressing health research. Several challenges are involved, particularly in terms of privacy. Overcoming these barriers will ensure that the use of these data to progress health research can be exploited to its full potential.
Topics: Data Collection; Electronic Health Records; Genomics; Humans; Information Storage and Retrieval; Registries
PubMed: 34559060
DOI: 10.2196/15739 -
Behavior Research Methods Jun 2022Because of the increasing popularity of voice-controlled virtual assistants, such as Amazon's Alexa and Google Assistant, they should be considered a new medium for...
Because of the increasing popularity of voice-controlled virtual assistants, such as Amazon's Alexa and Google Assistant, they should be considered a new medium for psychological and behavioral research. We developed Survey Mate, an extension of Google Assistant, and conducted two studies to analyze the reliability and validity of data collected through this medium. In the first study, we assessed validated procrastination and shyness scales as well as social desirability indicators for both the virtual assistant and an online questionnaire. The results revealed comparable internal consistency and construct and criterion validity. In the second study, five social psychological experiments, which have been successfully replicated by the Many Labs projects, were successfully reproduced using a virtual assistant for data collection. Comparable effects were observed for users of both smartphones and smart speakers. Our findings point to the applicability of virtual assistants in data collection independent of the device used. While we identify some limitations, including data privacy concerns and a tendency toward more socially desirable responses, we found that virtual assistants could allow the recruitment of participants who are hard to reach with established data collection techniques, such as people with visual impairment, dyslexia, or lower education. This new medium could also be suitable for recruiting samples from non-Western countries because of its wide availability and easily adaptable language settings. It could also support an increase in the generalizability of theories in the future.
Topics: Behavioral Research; Data Collection; Humans; Reproducibility of Results; Search Engine; Voice
PubMed: 34508287
DOI: 10.3758/s13428-021-01629-y -
Scientific Data Nov 2022Long-term monitoring datasets are fundamental to understand physical and ecological responses to environmental changes, supporting management and conservation. The data...
Long-term monitoring datasets are fundamental to understand physical and ecological responses to environmental changes, supporting management and conservation. The data should be reliable, with the sources of bias identified and quantified. CETUS Project is a cetacean monitoring programme in the Eastern North Atlantic, based on visual methods of data collection. This study aims to assess data quality and bias in the CETUS dataset, by 1) applying validation methods, through photographic confirmation of species identification; 2) creating data quality criteria to evaluate the observer's experience; and 3) assessing bias to the number of sightings collected and to the success in species identification. Through photographic validation, the species identification of 10 sightings was corrected and a new species was added to the CETUS dataset. The number of sightings collected was biased by external factors, mostly by sampling effort but also by weather conditions. Ultimately, results highlight the importance of identifying and quantifying data bias, while also yielding guidelines for data collection and processing, relevant for species monitoring programmes based on visual methods.
Topics: Animals; Cetacea; Data Collection; Datasets as Topic; Bias
PubMed: 36357425
DOI: 10.1038/s41597-022-01803-7 -
Computational Intelligence and... 2022In order to effectively reduce the energy consumption, improve the efficiency of data collection in HWSNs, and prolong the lifetime of the overall network, the...
In order to effectively reduce the energy consumption, improve the efficiency of data collection in HWSNs, and prolong the lifetime of the overall network, the clustering method is one of the most effective methods in the data collection methods for HWSNs. The data collection strategy of HWSNs based on the clustering method mainly includes three stages: (1) selecting the appropriate cluster head, (2) forming between clusters, and (3) transferring data between clusters. Among them, the selection of the cluster heads in the first stage. The optimal number of cluster heads in the formation of clusters in the second stage is the core and key to the clustering data collection of HWSNs. In the stage of cluster head selection, a data collection strategy for HWSNs based on the clustering method is proposed. Sink establishes an extreme learning machine neural network model. The cluster member nodes select cluster heads based on the remaining energy of the sensor node, the number of the neighbor node, and the distance to the sink. The best cluster head node is selected through the adaptive learning of the online sequence extreme learning machine. Through comprehensive consideration of various factors to complete the clustering process, the gray wolf algorithm is used to optimize the number of clusters, balance the effect of clustering, and improve the efficiency of data collection while reducing energy consumption. An energy efficient and reliable clustering data collection strategy for HWSNs based on the online sequence extreme learning machine and the gray wolf optimization algorithm is proposed in this paper. The simulation results show that the proposed algorithm not only significantly improves the efficiency of the data collection and reduces energy consumption but also comprehensively improves the reliability of the network and prolongs the network's lifetime.
Topics: Algorithms; Animals; Computer Communication Networks; Data Collection; Reproducibility of Results; Wireless Technology; Wolves
PubMed: 35178077
DOI: 10.1155/2022/4489436 -
Computational Intelligence and... 2022This study studies the problem of efficient multimedia data acquisition and decreasing whole energy expenditure of wireless multimedia sensor networks and proposes a...
This study studies the problem of efficient multimedia data acquisition and decreasing whole energy expenditure of wireless multimedia sensor networks and proposes a three-step multimedia data acquisition and wireless energy supplement strategy. Firstly, for network partition, this study proposes a network partition scheme based on vicinity likeness and distance of sensor nodes (VLD), which divides the whole sensor network into multiple regions. The physical links inside the region are dense and concentrated, while the link connections between regions are sparse. Disconnecting the connections between regions hardly affects the data transmission of sensor nodes. Secondly, this study proposes an efficient data acquisition and processing scheme for wireless multimedia sensor network ASS. Compared with other anchor selection schemes, this scheme has obvious performance advantages. Then, the problem of minimizing network energy expenditure is defined, and the optimal sensor node data perception rate and network link transmission rate of the optimization function are obtained by dual decomposition and sub-gradient method. Finally, in the case of a given network energy threshold, the performance of the overall strategy in this study is verified by comparing the amount of data collected by the base station.
Topics: Computer Communication Networks; Data Collection; Electronic Data Processing; Multimedia; Wireless Technology
PubMed: 35875748
DOI: 10.1155/2022/6394029 -
Therapeutic Innovation & Regulatory... Jan 2021As patient-reported outcome (PRO) measures are being included more frequently in oncology clinical trials, regulatory and health technology assessment agencies have...
As patient-reported outcome (PRO) measures are being included more frequently in oncology clinical trials, regulatory and health technology assessment agencies have begun to request long-term, post-treatment PRO data to supplement traditional survival/progression endpoints. These data may be collected as part of cohort extension or registry studies to describe long-term outcomes of study participants after concluding their cancer treatment. While post-treatment PRO data may be expected to satisfy regulatory and payer expectations, significant practical barriers exist for the efficient incorporation of these data into oncology clinical trials, such as subject attrition, protocol deviations, and treatment crossover. The incorporation of post-treatment PRO assessments is a resource-intensive task requiring clear objectives for how the data will be analyzed and interpreted by both sponsors and regulators. Incorporating PRO data collection via electronic modalities (e.g., smartphone, web) may be a less expensive and more feasible option for incorporating long-term follow-up, reducing the frequency of manual study staff follow-up and expensive clinic visits. It is essential to include well-defined estimands for the statistical analysis, as well as to document limitations associated with the long-term follow-up data-collection approach. Analytical techniques will likely rely on descriptive and model-based statistics, and conclusions about treatment differences will likely be limited to preliminary findings of effectiveness (instead of efficacy). Finally, communications with health authorities and regulatory agencies regarding the LTFU study design and analysis should occur as early as possible to ensure that the PRO data to be collected offer an opportunity to properly evaluate the research question(s) of interest.
Topics: Data Collection; Humans; Neoplasms; Patient Outcome Assessment; Patient Reported Outcome Measures; Technology Assessment, Biomedical
PubMed: 32643079
DOI: 10.1007/s43441-020-00195-3 -
BMC Pregnancy and Childbirth Mar 2021Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data... (Observational Study)
Observational Study
BACKGROUND
Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data (E-data) collection needs to account for these challenges. The Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) was an observational study to assess measurement of indicators for priority maternal and newborn interventions and took place in five hospitals in Bangladesh, Nepal and Tanzania (July 2017-July 2018). E-data tools were required to capture individually-linked, timed observation of care, data extraction from hospital register-records or case-notes, and exit-survey data from women.
METHODS
To evaluate this process for EN-BIRTH, we employed a framework organised around five steps for E-data design, data collection and implementation. Using this framework, a mixed methods evaluation synthesised evidence from study documentation, standard operating procedures, stakeholder meetings and design workshops. We undertook focus group discussions with EN-BIRTH researchers to explore experiences from the three different country teams (November-December 2019). Results were organised according to the five a priori steps.
RESULTS
In accordance with the five-step framework, we found: 1) Selection of data collection approach and software: user-centred design principles were applied to meet the challenges for observation of rapid, concurrent events around the time of birth with time-stamping. 2) Design of data collection tools and programming: required extensive pilot testing of tools to be user-focused and to include in-built error messages and data quality alerts. 3) Recruitment and training of data collectors: standardised with an interactive training package including pre/post-course assessment. 4) Data collection, quality assurance, and management: real-time quality assessments with a tracking dashboard and double observation/data extraction for a 5% case subset, were incorporated as part of quality assurance. Internet-based synchronisation during data collection posed intermittent challenges. 5) Data management, cleaning and analysis: E-data collection was perceived to improve data quality and reduce time cleaning.
CONCLUSIONS
The E-Data system, custom-built for EN-BIRTH, was valued by the site teams, particularly for time-stamped clinical observation of complex multiple simultaneous events at birth, without which the study objectives could not have been met. However before selection of a custom-built E-data tool, the development time, higher training and IT support needs, and connectivity challenges need to be considered against the proposed study or programme's purpose, and currently available E-data tool options.
Topics: Bangladesh; Data Accuracy; Electronic Health Records; Female; Focus Groups; Hospital Information Systems; Hospitals; Humans; Infant, Newborn; Nepal; Perinatal Care; Pregnancy; Software; Surveys and Questionnaires; Tanzania
PubMed: 33765951
DOI: 10.1186/s12884-020-03426-5 -
Orphanet Journal of Rare Diseases Jul 2023Rare diseases (RDs) affect approximately 8% of all people or > 400 million people globally. The Australian Government's National Strategic Action Plan for Rare... (Review)
Review
BACKGROUND
Rare diseases (RDs) affect approximately 8% of all people or > 400 million people globally. The Australian Government's National Strategic Action Plan for Rare Diseases has identified the need for a national, coordinated, and systematic approach to the collection and use of RD data, including registries. Rare disease registries (RDRs) are established for epidemiological, quality improvement and research purposes, and they are critical infrastructure for clinical trials. The aim of this scoping review was to review literature on the current state of RDRs in Australia; to describe how they are funded; what data they collect; and their impact on patient outcomes.
METHODS
We conducted a literature search on MEDLINE, EMBASE, CINAHL and PsychINFO databases, in addition to Google Scholar and grey literature. Dissertations, government reports, randomised control trials, conference proceedings, conference posters and meeting abstracts were also included. Articles were excluded if they did not discuss RDs or if they were written in a language other than English. Studies were assessed on demographic and clinical patient characteristics, procedure or treatment type and health-related quality of life captured by RDRs or databases that have been established to date.
RESULTS
Seventy-four RDRs were identified; 19 were global registries in which Australians participated, 24 were Australian-only registries, 10 were Australia and New Zealand based, and five were Australian jurisdiction-based registries. Sixteen "umbrella" registries collected data on several different conditions, which included some RDs, and thirteen RDRs stored rare cancer-specific information. Most RDRs and databases captured similar types of information related to patient characteristics, comorbidities and other clinical features, procedure or treatment type and health-related quality of life measures. We found considerable heterogeneity among existing RDRs in Australia, especially with regards to data collection, scope and quality of registries, suggesting a national coordinated approach to RDRs is required.
CONCLUSION
This scoping review highlights the current state of Australian RDRs, identifying several important gaps and opportunities for improvement through national coordination and increased investment.
Topics: Humans; Rare Diseases; Quality of Life; Australia; Registries; Data Collection
PubMed: 37501152
DOI: 10.1186/s13023-023-02823-1 -
BMJ Open Jan 2022Our understanding of community violence is limited by incomplete information, which can potentially be resolved by collecting violence-related injury information through...
OBJECTIVES
Our understanding of community violence is limited by incomplete information, which can potentially be resolved by collecting violence-related injury information through healthcare systems in tandem with prior data streams. This study assessed the feasibility of implementing Cardiff Model data collection procedures in the emergency department (ED) setting to improve multisystem data sharing capabilities and create more representative datasets.
DESIGN
Information collection fields were incorporated into the ED electronic health record (EHR), which gathered additional information from patients reporting assaultive injuries. ED nurses were surveyed to evaluate implementation and feasibility of information collection. Logistic regression was performed to determine associations between missing location information and patient demographic data.
SETTING
60-bed academic level I trauma adult ED in a large Midwestern city.
PARTICIPANTS
2648 patients screened positive for assault injuries between 2017 and 2020. 198 patients were omitted due to age outside the range served by this ED. Unselected inclusion of 150 ED nurses was surveyed.
MAIN OUTCOME MEASURES
Main outcomes include nursing staff survey responses and ORs for providing complete injury information across various patient demographics.
RESULTS
Most ED nurses believed that information collection aligned with the hospital's mission (92%), wanted information collection to continue (88%), did not believe that information collection impacted their workflow (88%), and reported taking under 1 min to screen and document violence information (77%). 825 patients (31.2%) provided sufficient information for geospatial mapping. Likelihood of providing complete location information was significantly associated with patient gender, race, arrival means, accompaniment, trauma type and year.
CONCLUSIONS
It is feasible to implement information collection procedures about location-based, assault-related injuries through the EHR in the adult ED setting. Nurses reported being receptive to collecting information. Analyses suggest patient-level and time variables impact information collection completeness. The geospatial information collected can greatly improve preexisting law enforcement and emergency medical systems datasets.
Topics: Adult; Crime Victims; Electronic Health Records; Emergency Service, Hospital; Humans; Surveys and Questionnaires; Violence
PubMed: 34992109
DOI: 10.1136/bmjopen-2021-052344 -
International Journal of Environmental... Nov 2021There is a growing interest in the collection and use of patient reported outcomes because they not only provide clinicians with crucial information, but can also be... (Review)
Review
There is a growing interest in the collection and use of patient reported outcomes because they not only provide clinicians with crucial information, but can also be used for economic evaluation and enable public health decisions. During the collection phase of PROMs, there are several factors that can potentially bias the analysis of PROM data. It is crucial that the collected data are reliable and comparable. The aim of this paper was to analyze the type of bias that have already been taken into consideration in the literature. A literature review was conducted by the authors searching on PubMed database, after the selection process, 24 studies were included in this review, mostly regarding orthopedics. Seven types of bias were identified: Non-response bias, collection method related bias, fatigue bias, timing bias, language bias, proxy response bias, and recall bias. Regarding fatigue bias and timing bias, only one study was found; for non-response bias, collection mode related bias, and recall bias, no agreement was found between studies. For these reasons, further research on this subject is needed in order to assess each bias type in relation to each medical specialty, and therefore find correction methods for reliable and comparable data for analysis.
Topics: Bias; Cost-Benefit Analysis; Data Collection; Humans; Orthopedics; Patient Reported Outcome Measures; Quality of Life
PubMed: 34886170
DOI: 10.3390/ijerph182312445