Evidence map›Paper›PMID 35334128›Full record

ArticlePublic health nursing (Boston, Mass.)2022

Effectiveness of a telephone-based nursing intervention to reduce hospital utilization by COVID-19 patients.

Samantha Korycinski, David Metcalf, Courtland Keteyian

Abstract read
In one paragraph

Article in Public health nursing (Boston, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
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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

3 authors.

Samantha KorycinskiPreventive Medicine Residency Program, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.ORCID 0000-0002-0172-9608
David MetcalfResearch Statistician, Research and Support Systems, Henry Ford Allegiance Health, Jackson, Michigan, USA.
Courtland KeteyianPresident/CEO, Jackson Health Network, Jackson, Michigan, USA.ORCID 0000-0003-4252-776X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDetermine the effectiveness of a COVID-19 remote monitoring and management program in reducing preventable hospital utilization.

designA retrospective cohort study utilizing data from electronic health records. SAMPLE: Two hundred ninety-three patients who tested positive for COVID-19 at a drive-through testing site in Michigan. [Correction added on 11 April 2022, after first online publication: In the preceding sentence, "Two hundred and ninety-third" has been corrected to "Two hundred ninety-three" in this version.] The intervention group, consisting of 139 patients, was compared to a control group of 154 patients. MEASUREMENTS: The primary outcome was the 30-day probability of hospital utilization. The covariates included in the analysis were age, gender, tobacco use, body mass index (BMI), race, and ethnicity.

interventionA nurse-led, telephone-based active management protocol for COVID-19 patients who were isolating at home.

resultsThe intervention group had a non-statistically significant 42% reduction in risk of hospital utilization within 30 days of a positive COVID-19 test when compared to the control group (HR = 0.578, p-value .111, HR 95% CI [0.29, 1.13]).

conclusionsA nurse-led remote monitoring and management program for COVID-19 reduced the probability of 30-day hospital utilization. Although the findings were not statistically significant, the program yielded practical significance by reducing hospital utilization, in-person interaction, and the risk of infection for healthcare workers.

Indexed as

COVID-19HospitalsHumansRetrospective StudiesSARS-CoV-2TelephoneCOVID-19hospitalizationhospital resourcesnursingSARS-CoV-2telemedicine

Identifiers

PMID35334128
PMCPMC9115443

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.