Patient classification for nursing staff sizing in intensive care: an integrative review 

Authors

DOI:

https://doi.org/10.62827/eb.v23i4.4028

Keywords:

Intensive care units; nursing assessment; nursing staff hospital; workload.

Abstract

Introduction: The staffing of the nursing workforce is crucial for ensuring the quality of health services and patient safety. The Patient Classification System plays a fundamental role in guiding the allocation of resources and distribution of the nursing team. Objective: To identify the main instruments used in the classification of patients that assist in the staffing of nursing teams. Methods: This is an integrative reviewfrom the Medical Literature Analysis and Retrieval System Online (MEDLINE), Latin American and Caribbean Literature in Health Sciences (LILACS), Nursing Database (BDEnf), The Scientific Electronic Library Online (SCIELO), and PUBMED for the period from 2013 to 2023, aimed at answering the guiding question "What are the classification systems for adult patients in Intensive Care Units for nursing staff sizing?" After inclusion criteria, a sample of 12 articles was totaled. Results: The studies were conducted over 4380 days, with an average of 365 days. The total number of patients in the 12 studies was 2369, with an average of 197.41 patients. Of the patients included, 1209 were men and 842 were women. Of the 19 ICUs studied, 18 were for adult patients (ICU-A) and one was an Oncological ICU, distributed among 9 university hospitals, 1 private, 2 public, 2 non-teaching, and 4 without specific classification. Of the instruments used, 67% of the studies employed the Nursing Activities Score (NAS), followed by the Therapeutic Intervention Scoring System (13%), Nine Equivalents of Nursing Manpower Use Score (7%), Nursing Outcomes Workload (7%), and (6%). Conclusion: The most used instrument in the studies was the NAS. The use of a patient classification system that encompasses the largest number of activities provided by the nursing team is extremely necessary for better patient safety and higher quality care.

Author Biographies

  • Victor Henrique Zanoni Oliveira Sampaio, UFES

    Enfermeiro, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brasil

  • Bruna Moraes Barbieri, UFES

    Discente do Curso de Mestrado em Enfermagem da Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brasil

  • Lucas Dalvi Armond Rezende, UFRJ

    Discente do Curso de Mestrado pelo Programa de Pós-Graduação em Medicina pela disciplina de Endocrinologia na Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil 

  • Márcia Valéria de Souza Almeida, UFES

    Docente do Curso de Enfermagem da Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brasil

  • Rita Inês Casagrande da Silva, UFES

    Docente do Curso de Enfermagem da Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brasil

  • Bruno Henrique Fiorin, UFES

    Docente do Curso de Enfermagem da Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brasil

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Published

2024-10-29

How to Cite

Patient classification for nursing staff sizing in intensive care: an integrative review . (2024). Enfermagem Brasil, 23(4), 1818-1835. https://doi.org/10.62827/eb.v23i4.4028

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