Evidence map›Paper›PMID 40417527›Full record

Trial reportAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial.

Rachel Y Lee, Kenrick D Cato, Patricia C Dykes, Graham Lowenthal, Jennifer B Withall, Sandy Cho, Haomiao Jia, Sarah C Rossetti

Abstract readMulticenter StudyPragmatic Clinical TrialRandomized Controlled Trial
In one paragraph

Trial report in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. Trial
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

8 authors.

Rachel Y LeeColumbia University, Department of Biomedical Informatics, New York, NY.
Kenrick D CatoColumbia University, School of Nursing, New York, NY.
Patricia C DykesBrigham and Women's Hospital, Boston, MA.
Graham LowenthalBrigham and Women's Hospital, Boston, MA.
Jennifer B WithallColumbia University, Department of Biomedical Informatics, New York, NY.
Sandy ChoNewton-Wellesley Hospital, Newton, MA.
Haomiao JiaColumbia University, School of Nursing, New York, NY.
Sarah C RossettiColumbia University, Department of Biomedical Informatics, New York, NY.

Funding

Reducing Health Disparities Through Informatics - Genomics SupplementT32NR007969 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SUZANNE BAKKEN, Rebecca Schnall · 2002 to 2026
$7.9M
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 NR016941NINR NIH HHS T32 NR007969
6 · The paper itself

Abstract

Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retrospective cohort study of 1,013 hospital encounters with unanticipated ICU transfers from a multi-site pragmatic randomized controlled trial, we assessed the influence of CONCERN EWS on in-hospital mortality and length of stay following unanticipated ICU transfers. Chi-square tests, t-tests, multivariate logistic regression, and generalized linear models were used. Our findings showed that patients who had unanticipated ICU transfers from acute care units with CONCERN EWS had a lower in-hospital mortality rate and a shorter average hospital stay than those transferred from units receiving usual care. These results suggest that CONCERN EWS enhances shared situational awareness for care teams, improves communication, and effectively facilitates timely interventions, thereby streamlining care processes and improving patient outcomes.

Indexed as

Early Warning ScoreHospital MortalityIntensive Care UnitsLength of StayPatient TransferAgedFemaleHumansMachine LearningMaleMiddle AgedRetrospective Studies

Identifiers

PMID40417527
PMCPMC12099359

What OpenQuestion holds

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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.