Titre
Big Data in Oncology Nursing Research: State of the Science.
Type
synthèse (review)
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Harris, C.S.
Auteure/Auteur
Pozzar, R.A.
Auteure/Auteur
Conley, Y.
Auteure/Auteur
Eicher, M.
Auteure/Auteur
Hammer, M.J.
Auteure/Auteur
Kober, K.M.
Auteure/Auteur
Miaskowski, C.
Auteure/Auteur
Colomer-Lahiguera, S.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1878-3449
Statut éditorial
Publié
Date de publication
2023-06
Volume
39
Numéro
3
Première page
151428
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Review
Publication Status: ppublish
Publication Status: ppublish
Résumé
To review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data.
Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines.
Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns.
With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.
Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines.
Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns.
With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.
PID Serval
serval:BIB_55A4F59D9EA7
PMID
Open Access
Oui
Date de création
2023-04-25T12:11:22.180Z
Date de création dans IRIS
2025-05-20T16:07:40Z
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Nom
Big Data Cancer Nursing Research.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
389.65 KB
Format
Adobe PDF
PID Serval
serval:BIB_55A4F59D9EA7.P002
URN
urn:nbn:ch:serval-BIB_55A4F59D9EA74
Somme de contrôle
(MD5):07fc82d5d6bc21d3563a098071fcef37