Evidence map›Paper›PMID 33386145›Full record

ArticleNursing outlook

Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients.

Maxim Topaz, Theresa A Koleck, Nicole Onorato, Arlene Smaldone, Suzanne Bakken

Open access · greenAbstract read
In one paragraph

Article in Nursing outlook. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
10.1field-weighted citation impact, top 2% of its field
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

20 citing papers in PubMed, 38 citations in OpenAlex.

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  8. Applying natural language processing to understand symptoms among older adult home healthcare patients with urinary incontinence.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025
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  9. Exploring Natural Language Processing through an Exemplar Using YouTube.International journal of environmental research and public health · 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

5 authors at 3 institutions in 1 country.

Maxim TopazCenter for Home Care Policy and Research, Visiting Nurse Service of New York, New York, NY; Columbia University School of Nursing, Columbia University Data Science Institute, New York, NY.
Theresa A KoleckUniversity of Pittsburgh School of Nursing, Pittsburgh, PA.
Nicole OnoratoCenter for Home Care Policy and Research, Visiting Nurse Service of New York, New York, NY. Electronic address: Nicole.Onorato@vnsny.org.
Arlene SmaldoneColumbia University School of Nursing, Columbia University College of Dental Medicine, New York, NY.
Suzanne BakkenColumbia University School of Nursing, Columbia University Department of Biomedical Informatics, Columbia University Data Science Institute, New York, NY.
Columbia University · USVNS Health · USUniversity of Pittsburgh · US

Funding

Reaching Communities through the Design of Information Visualizations (ReDIVis) Toolbox to Address COVID-19 Vaccine Hesitancy and Uptake.P30NR016587 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE, SMALDONE, ARLENE M · 2016 to 2022
$4.3M
Advancing Chronic Condition Symptom Cluster Science Through Use of Electronic Health Records and Data Science TechniquesR00NR017651 · NINR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KOLECK, THERESA ANN · 2020 to 2022
$722k
NINR NIH HHS P30 NR016587NINR NIH HHS R00 NR017651
6 · The paper itself

Abstract

backgroundNurses often document patient symptoms in narrative notes. PURPOSE: This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare.

methodNLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States.

findingsNLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission. DISCUSSION: Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.

Indexed as

Natural Language ProcessingAdultAgedAged, 80 and overDocumentationElectronic Health RecordsEmergency Service, HospitalFemaleHome Care ServicesHospitalizationHumansMaleMiddle AgedNew EnglandNursing CareRisk AssessmentHealth informaticshome healthnatural language processingNursingnursing documentationsymptoms

Identifiers

PMID33386145
PMCPMC8327386
OpenAlexW3115380437

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.