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Published on: June 21, 2010
Nursing minimum data sets: Findings from an umbrella review
Francesca Freguia1, Matteo Danielis1, Renzo Moreale1
1School of Nursing, Department of Medical Sciences, 9316University of Udine, Udine, Italy.
Objectives:
This study explores the evidence available on Nursing Minimum Data Sets (NMDSs) by summarising: (a) the main methodological and reporting features of the reviews published in this field to date; (b) the recommendations developed and published in such reviews regarding the NMDSs, and (c) the categories and items that should be included in the NMDSs according to the available reviews.
Methods:
An Umbrella Review was performed. A search of secondary studies published up to November 2021 that were focused on NMDSs for adult hospitalised patients was conducted using MEDLINE (via PubMed), CINAHL and Scopus databases. The included studies were critically evaluated by using the Checklist for Systematic Review and Research Syntheses. The full review process was performed according to the Preferred Reporting Items for Systematic reviews and the Meta-Analyses statement.
Results:
From the initial 1311 studies that were retrieved, a total of eight reviews published from 1995 to 2018 were included. Their methodological quality was variable; these reviews offered four types of recommendations, namely at the overall, clinical, research and management levels. Additionally, seven NMDSs emerged with different purposes, elements, target populations and taxonomies. A list of categories and items that should be included in NMDSs have been summarised.
Conclusions:
Nurses are daily involved in the nursing care documentation; however, which elements are recorded is mainly defined at the local levels and relies on paper and pencil. NMDS might provide a point of reference, specifically in the time of health digitalisation. Alongside other priorities as underlined in available recommendations, and the need to improve the quality of the reviews in this field, there is a need to develop a common NMDS by establishing its core elements, deciding on a standardised language and identifying linkages with other datasets.
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