Evidence map›Paper›PMID 42376342›Full record

ArticleNursing economic$

Nursing Surveillance from Invisible to Measurable to Indispensable: The CONCERN Early Warning System Trial.

Sarah C Rossetti, Kenrick Cato

Abstract read
In one paragraph

Article in Nursing economic$. 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

2 authors.

Sarah C RossettiAssociate Professor, Biomedical Informatics and Nursing, Columbia University, New York, NY.
Kenrick CatoProfessor, Informatics, University of Pennsylvania, Philadelphia, PA, Assistant Professor, School of Nursing, Columbia University, New York, NY.

Funding

Communicating Narrative Concerns Entered by RNs (CONCERN)R01NR016941 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Kenrick Dwain Cato, Sarah Collins Rossetti · 2017 to 2026
$6.7M
NINR NIH HHS R01 NR016941
6 · The paper itself

Abstract

Excessive documentation burdens undermine nurses' ability to effectively perform their most valuable and lifesaving skill - expert nursing surveillance. This article discusses the development and implementation of the CONCERN Early Warning System, an artificial intelligence algorithm that measures nursing surveillance by predicting deterioration of hospital patients, and then displaying that predictive score to the interdisciplinary care team.

Indexed as

acute careartificial intelligenceclinical deteriorationelectronic health recordsintensive carenursingNursing surveillancepredictive modeling

Identifiers

PMID42376342
PMCPMC13313598

What OpenQuestion holds

Textmetadata
LicenceTDM
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.