Evidence map›Paper›PMID 34889766›Full record

ArticleJMIR research protocols2021

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial.

Sarah Collins Rossetti, Patricia C Dykes, Christopher Knaplund, Min-Jeoung Kang, Kumiko Schnock, Jose Pedro Garcia, Li-Heng Fu, Frank Chang, Tien Thai, Matthew Fred and 9 more

Registry-linked trialAbstract read
In one paragraph

Article in JMIR research protocols, 2021. 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. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Innovative Technologies: Optimizing Nursing Practice.Computers, informatics, nursing : CIN · 2026
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  16. Advancing Data Science Competencies for Nursing PhD Students.Studies in health technology and informatics · 2024
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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

19 authors.

Sarah Collins RossettiDepartment of Biomedical Informatics, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0003-2632-8867
Patricia C DykesBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0003-4597-0732
Christopher KnaplundDepartment of Biomedical Informatics, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0002-9441-0021
Min-Jeoung KangBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0001-9130-3741
Kumiko SchnockBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0002-1107-6399
Jose Pedro GarciaBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0001-5089-547X
Li-Heng FuDepartment of Biomedical Informatics, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0001-5662-8321
Frank ChangBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0003-1774-9926
Tien ThaiBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0002-9746-4658
Matthew FredWorking Diagnosis, Haddonfield, NJ, United States.ORCID https://orcid.org/0000-0002-7749-1907
Tom Z KorachBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0002-6533-7961
Li ZhouBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0003-3874-4833
Jeffrey G KlannHarvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0003-2043-1601
David AlbersDepartment of Biomedical Informatics, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0002-5369-526X
Jessica SchwartzSchool of Nursing, Columbia University Medical Center, New York, NY, United States.ORCID https://orcid.org/0000-0002-1457-5724
Graham LowenthalBrigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0002-5480-0711
Haomiao JiaSchool of Nursing, Columbia University Medical Center, New York, NY, United States.ORCID https://orcid.org/0000-0002-1391-8190
Fang LiuDepartment of Biomedical Informatics, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0003-2922-4414
Kenrick CatoSchool of Nursing, Columbia University Medical Center, New York, NY, United States.ORCID https://orcid.org/0000-0002-0704-3826

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

backgroundEvery year, hundreds of thousands of inpatients die from cardiac arrest and sepsis, which could be avoided if those patients' risk for deterioration were detected and timely interventions were initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize to identify patients at risk of deterioration and thus prevent mortality and improve patient health outcomes. The overarching goal of the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study is to implement and evaluate an early warning score system that provides clinical decision support (CDS) in electronic health record systems. With a combination of machine learning and natural language processing, the CONCERN CDS utilizes nursing documentation patterns as indicators of nurses' increased surveillance to predict when patients are at the risk of clinical deterioration.

objectiveThe objective of this cluster randomized pragmatic clinical trial is to evaluate the effectiveness and usability of the CONCERN CDS system at 2 different study sites. The specific aim is to decrease hospitalized patients' negative health outcomes (in-hospital mortality, length of stay, cardiac arrest, unanticipated intensive care unit transfers, and 30-day hospital readmission rates).

methodsA multiple time-series intervention consisting of 3 phases will be performed through a 1-year period during the cluster randomized pragmatic clinical trial. Phase 1 evaluates the adoption of our algorithm through pilot and trial testing, phase 2 activates optimized versions of the CONCERN CDS based on experience from phase 1, and phase 3 will be a silent release mode where no CDS is viewable to the end user. The intervention deals with a series of processes from system release to evaluation. The system release includes CONCERN CDS implementation and user training. Then, a mixed methods approach will be used with end users to assess the system and clinician perspectives.

resultsData collection and analysis are expected to conclude by August 2022. Based on our previous work on CONCERN, we expect the system to have a positive impact on the mortality rate and length of stay.

conclusionsThe CONCERN CDS will increase team-based situational awareness and shared understanding of patients predicted to be at risk for clinical deterioration in need of intervention to prevent mortality and associated harm.

trial registrationClinicalTrials.gov NCT03911687; https://clinicaltrials.gov/ct2/show/NCT03911687. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30238.

Indexed as

clinical decision support systemclinical trialdeteriorationearly warning systemnatural language processingnursing documentationprediction

Identifiers

PMID34889766
PMCPMC8709914

What OpenQuestion holds

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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.