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Monographs of the Society For Research... Mar 2018
Topics: Child, Preschool; Data Collection; Female; Humans; Language Development; Male; Multilingualism; Research Design; Surveys and Questionnaires
PubMed: 29468693
DOI: 10.1111/mono.12349 -
The Veterinary Clinics of North... May 2020Data collection and research about adverse effects associated with euthanasia are lacking in the veterinary profession. The goal of this article is to review current... (Review)
Review
Data collection and research about adverse effects associated with euthanasia are lacking in the veterinary profession. The goal of this article is to review current research about euthanasia and propose concepts to collect and document euthanasia data to support future studies. A better understanding of the side effects witnessed near perimortem should provide benefits to pet owners, veterinarians, and staff, especially if methods are uncovered to minimize or mitigate the adverse events witnessed. Such data can provide valuable insight and guidance in improving the quality of death and furthering education about the dying process.
Topics: Animal Welfare; Animals; Data Collection; Euthanasia, Animal; Pets
PubMed: 32115279
DOI: 10.1016/j.cvsm.2019.12.006 -
Public Health Genomics 2019Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of... (Review)
Review
BACKGROUND
Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data (HD), dental medicine is edging into its fourth stage of digitization using artificial intelligence (AI). This narrative literature review outlines the challenge of managing HD and anticipating the potential of AI in oral healthcare and dental research by summarizing the current literature.
SUMMARY
The basis of successful management of HD is the establishment of a generally accepted data standard that will guide its implementation within electronic health records (EHR) and health information technology ecosystems (HIT Eco). Thereby continuously adapted (self-) learning health systems (LHS) can be created. The HIT Eco of the future will combine (i) the front-end utilization of HD in clinical decision-making by providers using supportive diagnostic tools for patient-centered treatment planning, and (ii) back-end algorithms analyzing the standardized collected data to inform population-based policy decisions about resource allocations and research directions. Cryptographic methods in blockchain enable a safe, more efficient, and effective dental care within a global perspective. Key Message: The interoperability of HD with accessible digital health technologies is the key to deliver value-based dental care and exploit the tremendous potential of AI.
Topics: Artificial Intelligence; Data Collection; Dental Research; Humans; Medical Informatics Applications; Public Health
PubMed: 31390644
DOI: 10.1159/000501643 -
AORN Journal Oct 2016Big data are large volumes of digital data that can be collected from disparate sources and are challenging to analyze. These data are often described with the five...
Big data are large volumes of digital data that can be collected from disparate sources and are challenging to analyze. These data are often described with the five "Vs": volume, velocity, variety, veracity, and value. Perioperative nurses contribute to big data through documentation in the electronic health record during routine surgical care, and these data have implications for clinical decision making, administrative decisions, quality improvement, and big data science. This article explores methods to improve the quality of perioperative nursing data and provides examples of how these data can be combined with broader nursing data for quality improvement. We also discuss a national action plan for nursing knowledge and big data science and how perioperative nurses can engage in collaborative actions to transform health care. Standardized perioperative nursing data has the potential to affect care far beyond the original patient.
Topics: Catheter-Related Infections; Clinical Decision-Making; Data Collection; Delivery of Health Care; Documentation; Electronic Health Records; Humans; Patient Handoff; Perioperative Nursing; Pressure Ulcer; Quality Improvement; Urinary Tract Infections; Venous Thrombosis
PubMed: 27692075
DOI: 10.1016/j.aorn.2016.07.009 -
International Journal of Public Health Jul 2018To systematically review the literature and compare response rates (RRs) of web surveys to alternative data collection methods in the context of epidemiologic and public... (Review)
Review
OBJECTIVES
To systematically review the literature and compare response rates (RRs) of web surveys to alternative data collection methods in the context of epidemiologic and public health studies.
METHODS
We reviewed the literature using PubMed, LILACS, SciELO, WebSM, and Google Scholar databases. We selected epidemiologic and public health studies that considered the general population and used two parallel data collection methods, being one web-based. RR differences were analyzed using two-sample test of proportions, and pooled using random effects. We investigated agreement using Bland-and-Altman, and correlation using Pearson's coefficient.
RESULTS
We selected 19 studies (nine randomized trials). The RR of the web-based data collection was 12.9 percentage points (p.p.) lower (95% CI = - 19.0, - 6.8) than the alternative methods, and 15.7 p.p. lower (95% CI = - 24.2, - 7.3) considering only randomized trials. Monetary incentives did not reduce the RR differences. A strong positive correlation (r = 0.83) between the RRs was observed.
CONCLUSIONS
Web-based data collection present lower RRs compared to alternative methods. However, it is not recommended to interpret this as a meta-analytical evidence due to the high heterogeneity of the studies.
Topics: Biomedical Research; Data Collection; Humans; Internet; Public Health; Randomized Controlled Trials as Topic; Surveys and Questionnaires
PubMed: 29691594
DOI: 10.1007/s00038-018-1108-4 -
Nursing Standard (Royal College of... Jul 2015This article presents an overview of the diary as a popular method for data collection in nursing and health research. The context for using diaries as a data collection...
This article presents an overview of the diary as a popular method for data collection in nursing and health research. The context for using diaries as a data collection tool is considered and the nature and purpose of the diary and its relationship with health care are examined. The author reflects on different types of diary and their use in data collection, and explores the advantages and disadvantages of using a diary approach to data collection in health care.
Topics: Confidentiality; Data Collection; Documentation; Nursing Research; Records
PubMed: 26136033
DOI: 10.7748/ns.29.44.36.e9251 -
International Journal of Medical... Oct 2022Golestan Population-based Cancer Registry (GPCR) with more than 15-years experiences developed an in-house online software called Cancer Data Collection and Processing...
Development of an online cancer data collection and processing tool for population-based cancer registries in a low-resource setting: The CanDCap experience from Golestan, Iran.
BACKGROUND
Golestan Population-based Cancer Registry (GPCR) with more than 15-years experiences developed an in-house online software called Cancer Data Collection and Processing (CanDCap) to improve its data collection operations from the conventional offline method to new online method. We aimed to report the methods and framework that GPCR applied to design and implementation of the CanDCap.
METHODS
CanDCap was designed based on International Agency for Research on Cancer (IARC) protocols and standards and according to the GPCR workflow. CanDCap has two parts including a web-based online part for data collection and a windows-based part for data processing consisting of quality control and deduplication of repeated records. Questionnaire for User Interface Satisfaction (QUIS) was used in order to assess user interaction satisfaction.
RESULTS
CanDCap was implemented in 2018 and could improve the quality of the GPCR data during its first three years of activity (2018-2020), during which about 9,000 records were registered. The coverage for optional items including national ID, father name, address and telephone number were improved from 23 %, 32 %, 83 % and 82 % in conventional offline method (2015-2017) to 83 %, 81 %, 87 %, and 90 % after using the CanDCap (2018-2020), respectively. The timeliness was also improved from 4 years to 2 years. Overall, user interaction satisfaction was acceptable (7.8 out of 9).
CONCLUSION
CanDCap could resulted in improvement in data quality and timeliness of the GPCR as a cancer registry unit with limited resources. It has the potential to be considered as a model for population-based cancer registries in lower-resource settings.
Topics: Data Accuracy; Data Collection; Humans; Iran; Neoplasms; Registries; Surveys and Questionnaires
PubMed: 35981480
DOI: 10.1016/j.ijmedinf.2022.104846 -
Journal of Plastic, Reconstructive &... Jun 2016
Topics: Access to Information; Biomedical Research; Breast Implants; Data Accuracy; Data Collection; Humans; Publishing; Plastic Surgery Procedures; Surgery, Plastic
PubMed: 27287212
DOI: 10.1016/j.bjps.2016.03.020 -
Journal of the American Medical... Jan 2019Lesbian, gay, bisexual, transgender, and queer (LGBTQ) people experience significant health disparities across the life course and require health care that addresses...
Lesbian, gay, bisexual, transgender, and queer (LGBTQ) people experience significant health disparities across the life course and require health care that addresses their unique needs. Collecting information on the sexual orientation and gender identity (SO/GI) of patients and entering SO/GI data in electronic health records has been recommended by the Institute of Medicine, the Joint Commission, and the Health Resources and Services Administration as fundamental to improving access to and quality of care for LGBTQ people. Most healthcare organizations, however, have yet to implement a system to collect SO/GI data due to multiple barriers. This report addresses those concerns by presenting recommendations for planning and implementing high-quality SO/GI data collection in primary care and other health care practices based on current evidence and best practices developed by a federally qualified health center and leader in LGBTQ health care.
Topics: Data Collection; Electronic Health Records; Female; Gender Identity; Humans; Male; Sexual Behavior; Sexual and Gender Minorities
PubMed: 30445621
DOI: 10.1093/jamia/ocy137 -
BMC Research Notes Aug 2019Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a...
OBJECTIVE
Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category.
RESULTS
Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1370/12,530). Overall 64% (1499/2352) of all discrepancies were due to data omissions, 76.6% (1148/1499) of missing entries were among categorical data. Omissions in PBDC (n = 1002) were twice as frequent as in EDC (n = 497, p < 0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.
Topics: Biomedical Research; Data Collection; Electronic Health Records; Humans; Nepal; Publications; Reproducibility of Results; Surveys and Questionnaires; Text Messaging
PubMed: 31439025
DOI: 10.1186/s13104-019-4574-8