Evidence map›Paper›PMID 40756819›Full record

ArticleJournal of the Royal Society of New Zealand2025

Critical care resources in surge response: towards real-time situation awareness.

Reem Abbas, Biswash Paudel, Lily Yee, Roopak Sinha, Dave Parry

Abstract read
In one paragraph

Article in Journal of the Royal Society of New Zealand, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Reem AbbasSchool of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
Biswash PaudelSchool of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
Lily YeeFaculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand.
Roopak SinhaSchool of Information Technology, Deakin University, Melbourne, Australia.
Dave ParryIT, Media and Communications, Murdoch University, Perth, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In large-scale emergencies and disasters, real-time situation awareness around health resources can be lifesaving. Understanding the situation at hand, the pressing needs and the actions needed often using information systems that require manual data entry. Relying on humans to enter data manually during surge response is both error-prone and an impediment to the critical requirement of real-time situation awareness. This paper explores the potential of Ultra-High Frequency Radio Frequency Identification (UHF RFID) technology to automate the process of manual data entry followed in providing situation awareness related to critical care resources during surge response. Using design science, an RFID-based prototype was developed to identify and track intensive care resources in real-time. The system was tested in a simulated hospital ward environment to understand deployment challenges and a dashboard was developed to reflect real-time data from multiple sources simultaneously. When properly deployed, UHF RFID can be a viable approach to automating critical care reporting during surge response. Automating routine processes can enhance data quality and minimise workload on health professionals leading to better patient care and evidence based decision-making.

Indexed as

automationCritical care resourcesreal-time situation awarenesssurge responseUHF-RFID

Identifiers

PMID40756819
PMCPMC12315204

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.