ArticleBMC nursing2025
Application of and research on 5G mobile nursing stations in clinical settings.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study investigated the application of and research on 5G mobile nursing stations in clinical settings.
methodsA randomized sample of 300 nurses from one hospital was selected. The control group was established from December 2021 to September 2022 as the nurses used the computerized hospital information system (HIS) for nursing documentation and performed routine manual verification of the treatment and patient information. The observation group was established from October 2022 to August 2023 as the nurses used portable digital assistants (PDAs) from the 5G mobile nursing stations for nursing documentation and scanned quick response (QR) codes to verify treatment and patient information. This study compared the time taken by nurses in both scenarios to verify oral medicine dispensing and intravenous (IV) infusions against medical orders, as well as the time taken by nurses to input vital signs and complete nursing documentation.
resultsIn the observation group, the average time for verifying IV infusions against medical orders was 52.34 ± 2.61 s, that for verifying oral medicine dispensing against medical orders was 29.32 ± 1.71 s, that for inputting vital signs was 46.82 ± 3.64 s, and that for completing nursing documentation was 86.81 ± 5.62 s. These times were significantly different from those of the control group, which were 59.84 ± 5.76 s, 35.30 ± 2.31 s, 58.58 ± 3.59 s, and 128.51 ± 6.19 s, respectively (P < 0.05).
conclusionsThe use of 5G mobile nursing station PDAs to scan QR codes to verify oral medicine dispensing, IV infusions, other treatments, and patient information resulted in faster verification speeds and 100% accuracy, ensuring accurate treatment information verification and patient safety. Moreover, the use of 5G mobile nursing station PDAs for nursing documentation shortened the time for nurses to input vital signs and complete nursing documentation, thereby improving their efficiency in completing documentation tasks and allowing them to devote more time to patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.