Evidence map›Paper›PMID 38883706›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Sarah C Rossetti, Patricia C Dykes, Chris Knaplund, Sandy Cho, Jennifer Withall, Graham Lowenthal, David Albers, Rachel Lee, Haomiao Jia, Suzanne Bakken and 11 more

Registry-linked trialAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03911687 (Communicating Narrative Concerns Entered by RNs), which is not on this 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.

NCT03911687 nacompletednot on this map

Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System

TypeinterventionalSponsorColumbia UniversityRan2020 to 2022Enrolled60,893ConditionsHospital Acquired ConditionArmsCONCERN CDS system notification
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

21 authors.

Sarah C RossettiColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.ORCID 0000-0003-2632-8867
Patricia C DykesBrigham and Women's Hospital, Boston, MA.
Chris KnaplundColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Sandy ChoNewton Wellesley Hospital, Newton, MA.
Jennifer WithallColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Graham LowenthalBrigham and Women's Hospital, Boston, MA.
David AlbersColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Rachel LeeColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Haomiao JiaColumbia University Irving Medical Center, School of Nursing, New York, NY.
Suzanne BakkenColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Min-Jeoung KangBrigham and Women's Hospital, Boston, MA.
Frank Y ChangBrigham and Women's Hospital, Boston, MA.
Li ZhouBrigham and Women's Hospital, Boston, MA.
David W BatesBrigham and Women's Hospital, Boston, MA.
Temiloluwa DaramolaColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Fang LiuUniversity of Pennsylvania, Philadelphia, PA.
Jessica Schwartz-DillardColumbia University Irving Medical Center, School of Nursing, New York, NY.
Mai TranColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Syed Mohtashim Abbas BokhariColumbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
Jennifer ThateSiena College, Loudonville, NY.
Kenrick D CatoUniversity of Pennsylvania, Philadelphia, PA.

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

Importance: Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs. Objective: To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS. Design: One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups. Setting: Two large U.S. health systems. Participants: Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. Intervention: The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Main Outcomes and Measures: Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission. Results: A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. Conclusions and Relevance: A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. Trial Registration: ClinicalTrials.gov Identifier: NCT03911687.

Indexed as

artificial intelligenceclinical decision supportEarly warning systemelectronic health recordinpatientmachine learningnursesnursing informaticsrandomized controlled trial

Identifiers

PMID38883706
PMCPMC11177900

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.