Cargas de trabajo de las enfermeras en cuidados intensivos. Validez de su estimación mediante aplicaciones para dispositivos móviles y comparación con nursing activities score. Revisión sistematizada de la literatura

Abstract

Introduction

Adjusting human resources to intensive care units (ICU) workload is essential. The development of software for estimating nursing workload using mobile devices (smartphone, smartwatch and/or tablets) could be a useful tool and complement and/or improve the measurements made with the Nursing Activities Score (NAS), a validated scale.

Objectives

To analyze the validity of devices and mobile applications for estimating ICU nurses’ workloads and their comparison with NAS.

Methodology

Systematised literature review from 2009 to 2021 informed by flowchart PRISMA-2020 and its extension PRISMA-S. Critical reading (CASPe). Steps: elaboration of the research question, concept identification (English and Spanish natural language and descriptors MesH, Emtree and CINAHL Headings), search strategy and data collection in MEDlars online (MEDLINE), Ovid, Excerpta Medica dataBASE (EMBASE), Elsevier, Cumulative Index to Nursing & Allied Health Literature (CINAHL), EBSCO and Web of Science (WoS); data extraction and evaluation, analysis and synthesis of results. We included studies with abstracts published in English and Spanish conducted in ICU with workload measurement (preferably NAS) using mobile devices.

Results

223 studies of which 84 in MEDLINE, 50 in CINAHL, 48 in EMBASE, 33 in WoS and 8 in other sources. 117 were eligible for screening, of which 95 did not meet the inclusion criteria. 22 studies were screened for eligibility, of which 17 were excluded. Five articles were selected for final review. None of the studies provided results on costs, acceptance testing, validity, reliability, system problems, advantages, disadvantages or resource estimation.

Discussion and conclusions

The use of mobile devices and applications to measure ICU workloads is not yet validated and has not been shown to be more accurate than traditional NAS assessment.