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Annals of Emergency Medicine Jul 2016
Topics: Data Collection; Electronic Health Records; Emergency Medical Services; Health Equity; Humans; Information Dissemination; Physicians
PubMed: 27343653
DOI: 10.1016/j.annemergmed.2016.04.027 -
JAMA Internal Medicine May 2021Clinical trials conducted at clinical sites are limited to enrolling people who live nearby and are able to attend visits at clinics. Some types of clinical trials can...
Clinical trials conducted at clinical sites are limited to enrolling people who live nearby and are able to attend visits at clinics. Some types of clinical trials can be performed without clinical sites, which enables people to participate regardless of proximity to a clinical site or limitations that make visits difficult. Trials at clinical sites involve face-to-face relationships with in-person collection of informed consent, examinations, data, and specimens. In contrast, without clinical sites, informed consent and data are obtained online, limited examinations can be performed by telemedicine or visiting research nurses, biospecimens can be collected by visiting nurses or local laboratories, and treatments can be sent to homes or administered by nurses in participants' homes. Trials without clinical sites require internet access and must adapt to the lack of face-to-face interactions with study staff, with communication conducted by email, telephone, or video. Many trials cannot be performed entirely without clinical sites because they require examinations, tests, or treatments that must be given at a clinical site. However, some of the methods required for trials without sites, such as online data collection, follow-up visits by telemedicine or research nurses, and delivery of treatments to home, could reduce the need for visits to clinical sites and reduce the burden of participating in a clinical trial. When feasible, conducting clinical trials without clinical sites has the potential to expand participation and the generalizability of their results.
Topics: Data Collection; Humans; Informed Consent; Remote Consultation; Research Design
PubMed: 33646281
DOI: 10.1001/jamainternmed.2020.9223 -
Journal of Continuing Education in... Feb 2017Big data is a big topic in all leadership circles. Leaders in professional development must develop an understanding of what data are available across the organization...
Big data is a big topic in all leadership circles. Leaders in professional development must develop an understanding of what data are available across the organization that can inform effective planning for forecasting. Collaborating with others to integrate data sets can increase the power of prediction. Big data alone is insufficient to make big decisions. Leaders must find ways to access small data and triangulate multiple types of data to ensure the best decision making. J Contin Educ Nurs. 2017;48(2):60-61.
Topics: Adult; Data Collection; Datasets as Topic; Decision Making; Female; Humans; Male; Middle Aged; Nurse Administrators
PubMed: 28135377
DOI: 10.3928/00220124-20170119-04 -
Journal of the American College of... Sep 2019Data collection and analysis are central to all quality improvement initiatives. However, there is often a lack of understanding about the different ways data can be... (Review)
Review
Data collection and analysis are central to all quality improvement initiatives. However, there is often a lack of understanding about the different ways data can be collected and why one mode of data collection should be selected over another. In this article, a framework is provided to help quality improvement teams identify and define the data to be collected. This is followed by a discussion of the different modes of data collection. Examples are provided to help illustrate each mode of data collection. Finally, the advantages and disadvantages of each mode are described.
Topics: Data Collection; Forecasting; Humans; Quality Improvement; Radiology; United States
PubMed: 31492402
DOI: 10.1016/j.jacr.2019.05.032 -
Addiction (Abingdon, England) Oct 2020Amazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1)...
AIMS
Amazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1) provides an introduction to the mechanics and validity of MTurk research; (2) gives examples of MTurk research; and (3) discusses current limitations and best practices in MTurk research.
METHODS
We review four use cases of MTurk for research relevant to addictions: (1) the development of novel measures, (2) testing interventions, (3) the collection of longitudinal use data to determine the feasibility of longer-term studies of substance use and (4) the completion of large batteries of assessments to characterize the relationships between measured constructs. We review concerns with the platform, ways of mitigating these and important information to include when presenting findings.
RESULTS
MTurk has proved to be a useful source of data for behavioral science more broadly, with specific applications to addiction science. However, it is still not appropriate for all use cases, such as population-level inference. To live up to the potential of highly transparent, reproducible science from MTurk, researchers should clearly report inclusion/exclusion criteria, data quality checks and reasons for excluding collected data, how and when data were collected and both targeted and actual participant compensation.
CONCLUSIONS
Although on-line survey research is not a substitute for random sampling or clinical recruitment, the Mechanical Turk community of both participants and researchers has developed multiple tools to promote data quality, fairness and rigor. Overall, Mechanical Turk has provided a useful source of convenience samples despite its limitations and has demonstrated utility in the engagement of relevant groups for addiction science.
Topics: Behavior, Addictive; Behavioral Research; Crowdsourcing; Data Accuracy; Data Collection; Humans; Patient Selection
PubMed: 32135574
DOI: 10.1111/add.15032 -
BMJ (Clinical Research Ed.) Jul 2015
Topics: Adult; Cross-Sectional Studies; Data Collection; Health Records, Personal; Health Services Research; Humans; Informed Consent; Public Health Surveillance; Public Opinion; Registries; Surveys and Questionnaires; United Kingdom
PubMed: 26231184
DOI: 10.1136/bmj.h4155 -
Expert Review of Pharmacoeconomics &... Jul 2024
Topics: Humans; Biomarkers; Data Collection; Time Factors; Endpoint Determination
PubMed: 38362754
DOI: 10.1080/14737167.2024.2320233 -
Current Opinion in Urology Jul 2017Secondary data analysis has become increasingly common in health services research, specifically comparative effectiveness research. While a comprehensive study of the... (Review)
Review
PURPOSE OF REVIEW
Secondary data analysis has become increasingly common in health services research, specifically comparative effectiveness research. While a comprehensive study of the techniques and methods for secondary data analysis is a wide-ranging topic, we sought to perform a descriptive study of some key methodological issues related to secondary data analyses and to provide a basic summary of techniques to address them.
RECENT FINDINGS
In this study, we first address common issues seen in analysis of secondary datasets, and limitations of datasets with respect to bias. We cover some strategies for handling missing or incomplete data and a basic summary of three statistical approaches that can be used to address the problem of bias.
SUMMARY
While it is unrealistic for surgeon scientists to aspire to the depth of knowledge of professional statisticians or data scientists, it is important for researchers and clinicians reading to understand some of the common pitfalls and issues when using secondary data to investigate clinical questions. Ultimately, the choice of analytical technique and the particular data sets used should be dictated by the research question and hypothesis being tested. Transparency about data handling and statistical techniques are vital elements of secondary data analysis.
Topics: Comparative Effectiveness Research; Data Collection; Data Interpretation, Statistical; Humans; Research Design
PubMed: 28570290
DOI: 10.1097/MOU.0000000000000407 -
Journal of Nursing Scholarship : An... Mar 2015To describe novel and emerging strategies practiced globally in research to improve longitudinal data collection. (Review)
Review
PURPOSE
To describe novel and emerging strategies practiced globally in research to improve longitudinal data collection.
ORGANIZING CONSTRUCT
In research studies, numerous strategies such as telephone interviews, postal mailing, online questionnaires, and electronic mail are traditionally utilized in longitudinal data collection. However, due to technological advances, novel and emerging strategies have been applied to longitudinal data collection, such as two-way short message service, smartphone applications (or "apps"), retrieval capabilities applied to the electronic medical record, and an adapted cloud interface. In this review, traditional longitudinal data collection strategies are briefly described, emerging and novel strategies are detailed and explored, and information regarding the impact of novel methods on participant response rates, the timeliness of participant responses, and cost is provided. We further discuss how these novel and emerging strategies affect longitudinal data collection and advance research, specifically nursing research.
CONCLUSIONS
Evidence suggests that the novel and emerging longitudinal data collection strategies discussed in this review are valuable approaches to consider. These strategies facilitate collecting longitudinal research data to better understand a variety of health-related conditions. Future studies, including nursing research, should consider using novel and emerging strategies to advance longitudinal data collection.
CLINICAL RELEVANCE
A better understanding of novel and emerging longitudinal data collection strategies will ultimately improve longitudinal data collection as well as foster research efforts. Nurse researchers, along with all researchers, must be aware of and consider implementing novel and emerging strategies to ensure future healthcare research success.
Topics: Data Collection; Electronic Mail; Health Services Research; Humans; Longitudinal Studies; Nursing Research; Postal Service; Surveys and Questionnaires; Telephone
PubMed: 25490868
DOI: 10.1111/jnu.12116 -
The Journal of Law, Medicine & Ethics :... Dec 2020The firearms data infrastructure in the United States is severely limited in scope and fragmented in nature. Improved data systems are needed in order to address gun...
The firearms data infrastructure in the United States is severely limited in scope and fragmented in nature. Improved data systems are needed in order to address gun violence and promote productive conversation about gun policy. In the absence of federal leadership in firearms data systems improvement, motivated states may take proactive steps to stitch gaps in data systems. We propose that states evaluate the gaps in their systems, expand data collection, and improve data presentation and availability.
Topics: Data Collection; Data Systems; Databases as Topic; Federal Government; Firearms; Gun Violence; History, 20th Century; Humans; Information Systems; State Government; United States
PubMed: 33404295
DOI: 10.1177/1073110520979399