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NASN School Nurse (Print) May 2019School nurses collect, manage, and report on school health data, which is covered by student privacy laws. School nurses and other district employees who collect and...
School nurses collect, manage, and report on school health data, which is covered by student privacy laws. School nurses and other district employees who collect and manage sensitive data must understand their responsibilities throughout the data life cycle. Nurses must identify who is accountable for data governance in their school and the process for influencing data governance policies and procedures. Every school district is accountable for the integrity and the security of the data it collects about students, families, and staff. Data governance encompasses all decisions about data throughout the data life cycle from defining what data are needed, to collecting, storing, protecting, using, sharing, and retiring and destroying data and who is accountable for those decisions. The data governance in schools is prescribed by the U.S. Department of Education, state education departments, and local school districts. The Student Privacy Policy Office at the U.S. Department of Education publishes clear and precise guidance on the treatment of data collected in schools.
Topics: Child; Computer Security; Confidentiality; Data Collection; Humans; School Nursing; Schools; Surveys and Questionnaires; United States
PubMed: 30935311
DOI: 10.1177/1942602X19838798 -
Internal Medicine Journal Dec 2017Patient-reported outcome measures (PROM) are potentially useful outcome measures that may be reported at the individual clinical, health service and/or health system... (Review)
Review
Patient-reported outcome measures (PROM) are potentially useful outcome measures that may be reported at the individual clinical, health service and/or health system level. PROM require clearly defined patient populations to enable comparisons, and are most meaningful when integrated with clinical data sets. Where possible PROM should be measured pre- and post-intervention using reliable and validated tools. A variety of PROM collection methods exist which each have strengths and limitations, with selection depending on their purpose and patient factors. PROM programmes should be developed with high levels of clinician support and patient input to maximise collection of clinically relevant information.
Topics: Data Collection; Health Services; Humans; Patient Reported Outcome Measures; Patient Satisfaction; Self Care
PubMed: 29224197
DOI: 10.1111/imj.13633 -
NASN School Nurse (Print) Nov 2023This is the first in a series of three articles looking at school health data collection from identification of data points to utilizing data to share your story and...
This is the first in a series of three articles looking at school health data collection from identification of data points to utilizing data to share your story and submitting your data to contribute to the National School Health Data Set: Every Student Counts! Many school nurses cringe at the mention of data collection. However, everything we do as school nurses is data driven. Every documented assessment, observation, and conversation provides the school nurse with data. The barriers often noted to participating in formal data collection efforts are time, workload, access to an electronic health record, and not understanding the and . The key to data collection is identifying the data already being collected and starting where you are. Data collection is not something new that you need to find a way to fit into your already busy schedule. do you currently collect? are you collecting the data you have? do you collect it? do you do with the data? These are all very important questions, but let's take a closer look at the and behind data collection.
Topics: Humans; School Nursing; Data Collection; Electronic Health Records; Students; Communication
PubMed: 37735899
DOI: 10.1177/1942602X231199932 -
Journal of Medical Systems Mar 2022Current trauma registries suffer from inconsistent collection of data needed to assess health equity. To identify barriers/facilitators to collecting accurate...
Current trauma registries suffer from inconsistent collection of data needed to assess health equity. To identify barriers/facilitators to collecting accurate equity-related data elements, we assessed perspectives of national stakeholders, Emergency Department (ED) registration, and Trauma Registry staff. We conducted a Delphi process with experts in trauma care systems and key informant interviews and focus groups with ED patient registration and trauma registry staff at a regional Level I trauma center. Topics included data collection process, barriers/facilitators for equity-related data collection, electronic health record (EHR) entry, trauma registry abstraction, and strategies to overcome technology limitations. Responses were qualitatively analyzed and triangulated with observations of ED and trauma registry staff workflow. Expert-identified barriers to consistent data collection included lack of staff investment in changes and lack of national standardization of data elements; facilitators were simplicity, quality improvement checks, and stakeholder investment in modifying existing technology to collect equity elements. ED staff reported experiences with patients reacting suspiciously to queries regarding race and ethnicity. Cultural resonance training, a script to explain equity data collection, and allowing patients to self-report sensitive items using technology were identified as potential facilitators. Trauma registry staff reported lack of discrete fields, and a preference for auto-populated and designated EHR fields. Identified barriers and facilitators of collection and abstraction of equity-related data elements from multiple stakeholders provides a framework for improving data collection. Successful implementation will require standardized definitions, staff training, use of existing technology for patient self-report, and discrete fields for added elements.
Topics: Data Collection; Electronic Health Records; Health Equity; Humans; Registries; Trauma Centers
PubMed: 35260929
DOI: 10.1007/s10916-022-01804-4 -
Nursing Standard (Royal College of... May 2015A focus group is usually understood as a group of people brought together by a researcher to interact as a group. Focus group research explicitly uses interaction as...
A focus group is usually understood as a group of people brought together by a researcher to interact as a group. Focus group research explicitly uses interaction as part of its methodology. This article summarises the practice of running focus groups, explores the nature of focus group data and provides an example of focus group analysis.
Topics: Data Collection; Focus Groups; Humans; Nursing Research; Research Design; United Kingdom
PubMed: 25967446
DOI: 10.7748/ns.29.37.44.e8822 -
American Journal of Public Health Dec 2021
Topics: COVID-19; Data Accuracy; Data Collection; Humans; Influenza, Human; Public Health Surveillance; SARS-CoV-2; Self Report; Surveys and Questionnaires; Time Factors; United States; Zika Virus Infection
PubMed: 34878882
DOI: 10.2105/AJPH.2021.306553 -
Epilepsia Mar 2021Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic... (Review)
Review
Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient-level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is useful, but not readily applicable to an individual patient. This has brought into sharp focus the desire for a more individualized approach through which we counsel people based on individual characteristics, as opposed to population-level data. We are now accruing data at unprecedented rates, allowing us to convert this ideal into reality. In addition, we have access to growing volumes of administrative and electronic health records data, biometric, imaging, genetics data, microbiome, and other "omics" data, thus paving the way toward phenome-wide association studies and "the epidemiology of one." Despite this, there are many challenges ahead. The collating, integrating, and storing sensitive multimodal data for advanced analytics remains difficult as patient consent and data security issues increase in complexity. Agreement on many aspects of epilepsy remains imperfect, rendering models sensitive to misclassification due to a lack of "ground truth." Even with existing data, advanced analytics models are prone to overfitting and often failure to generalize externally. Finally, uptake by clinicians is often hindered by opaque, "black box" algorithms. Systematic approaches to data collection and model generation, and an emphasis on education to promote uptake and knowledge translation, are required to propel epilepsy-based precision medicine from the realm of the theoretical into routine clinical practice.
Topics: Algorithms; Data Analysis; Data Collection; Electronic Health Records; Epilepsy; Humans; Precision Medicine
PubMed: 33205406
DOI: 10.1111/epi.16739 -
American Journal of Botany Apr 2017
Topics: Data Collection; High-Throughput Screening Assays; Image Processing, Computer-Assisted; Information Storage and Retrieval; Phenotype; Plant Physiological Phenomena; Plants
PubMed: 28400413
DOI: 10.3732/ajb.1700044 -
Zeitschrift Fur Orthopadie Und... Jun 2018
Topics: Data Collection; Germany; Humans; Prostheses and Implants; Prosthesis Failure; Quality Assurance, Health Care; Registries; Reoperation; Societies, Medical
PubMed: 29954033
DOI: 10.1055/a-0597-4610 -
Academic Radiology Dec 2015Successful research results from the combination of multiple elements, including an appropriate research question, study design, research method, statistical analysis,... (Review)
Review
Successful research results from the combination of multiple elements, including an appropriate research question, study design, research method, statistical analysis, and interpretation of results. One element of research that is easy to overlook is proper data collection and preparation for analysis. If data collection or preparation is inadequately planned or executed, the data may not be analyzable by a statistician without significant effort spent on data cleaning. Even worse, the data may contain problems that can be resolved only through time-consuming revision or repeat data collection. In this review, we present some practical guidelines and best practices for preparing data that can reduce the work of subsequent analysis.
Topics: Data Collection; Data Interpretation, Statistical; Guidelines as Topic; Humans; Research Design; Software
PubMed: 26454810
DOI: 10.1016/j.acra.2015.08.024