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Journal of the American Medical... Mar 2021
Topics: Data Collection; Electronic Health Records; Health Records, Personal; Humans; Machine Learning; Natural Language Processing; Patient Access to Records; Surveys and Questionnaires
PubMed: 33677514
DOI: 10.1093/jamia/ocab040 -
Current Opinion in Insect Science Dec 2022Innovative methods in data collection and analytics for pest and disease management are advancing together with computational efficiency. Tools, such as the open-data... (Review)
Review
Innovative methods in data collection and analytics for pest and disease management are advancing together with computational efficiency. Tools, such as the open-data kit, research electronic data capture, fall armyworm monitoring, and early warning- system application and remote sensing have aided the efficiency of all types of data collection, including text, location, images, audio, video, and others. Concurrently, data analytics have also evolved with the application of artificial intelligence and machine learning (ML) for early warning and decision-support systems. ML has repeatedly been used for the detection, diagnosis, modeling, and prediction of crop pests and diseases. This paper thus highlights the innovations, implications, and future progression of these technologies for sustainability.
Topics: Animals; Artificial Intelligence; Data Collection; Machine Learning; Plant Diseases; Agriculture; Pest Control; Data Analysis
PubMed: 36055644
DOI: 10.1016/j.cois.2022.100964 -
Journal of Structural Biology Dec 2022This report provides an overview of the discussions, presentations, and consensus thinking from the Workshop on Smart Data Collection for CryoEM held at the New York...
This report provides an overview of the discussions, presentations, and consensus thinking from the Workshop on Smart Data Collection for CryoEM held at the New York Structural Biology Center on April 6-7, 2022. The goal of the workshop was to address next generation data collection strategies that integrate machine learning and real-time processing into the workflow to reduce or eliminate the need for operator intervention.
Topics: Data Collection
PubMed: 36341954
DOI: 10.1016/j.jsb.2022.107913 -
The Journal of Urology Sep 2020
Topics: Data Collection; Humans; Patient Reported Outcome Measures
PubMed: 32574093
DOI: 10.1097/JU.0000000000001030.01 -
The Journal of Urology Sep 2020
Topics: Data Collection; Humans; Patient Reported Outcome Measures
PubMed: 32574092
DOI: 10.1097/JU.0000000000001030.02 -
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 -
Journal of Registry Management 2023The past several years have been marked by substantial growth in pediatric cancer data and collection across the world. In the United States, multiple projects and...
The past several years have been marked by substantial growth in pediatric cancer data and collection across the world. In the United States, multiple projects and standard setters have laid a foundation for the growth of this data, and the need for an overview and explanation of a few of the programs directly relevant to cancer registrars has become apparent. This article will discuss 3 initiatives that highlight many of the efforts and intricacies involved with the collection of pediatric cancer data in the cancer registry world: the National Childhood Cancer Registry, the Toronto Pediatric Cancer Stage Guidelines, and the Pediatric Site-Specific Data Items Work Group.
Topics: Child; Humans; United States; Neoplasms; Registries; Neoplasm Staging; Data Management; Data Collection
PubMed: 37941745
DOI: No ID Found -
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