Evidence map›Paper›PMID 36890538›Full record

ArticleBMC medical informatics and decision making2023

Rapid response nursing triage outcomes for COVID-19: factors associated with patient's participation in triage recommendations.

Jyu-Lin Chen, Chen-Xi Lin, Mijung Park, Jerry John Nutor, Rosalind de Lisser, Thomas J Hoffmann, Hannah J Kim

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the 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, top 95% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

7 authors at 3 institutions in 1 country.

Jyu-Lin ChenDepartment of Family Health Care Nursing, University of California, San Francisco, 2 Koret Way, San Francisco, CA, 94143-0606, USA. jyu-lin.chen@ucsf.edu.ORCID http://orcid.org/0000-0002-8687-4109
Chen-Xi LinDepartment of Family Health Care Nursing, University of California, San Francisco, 2 Koret Way, San Francisco, CA, 94143-0606, USA.
Mijung ParkDepartment of Family Health Care Nursing, University of California, San Francisco, 2 Koret Way, San Francisco, CA, 94143-0606, USA.
Jerry John NutorDepartment of Family Health Care Nursing, University of California, San Francisco, 2 Koret Way, San Francisco, CA, 94143-0606, USA.
Rosalind de LisserDavis Betty Irene Moore Hall, School of Nursing, University of California, 2570 48th St., Sacramento, CA, 95817, USA.
Thomas J HoffmannDepartment of Epidemiology and Biostatistics, University of California, San Francisco, 513 Parnassus Ave, MSB, San Francisco, CA, 94117, USA.
Hannah J KimKaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
University of California, San Francisco · USKaiser Permanente · USUC Davis Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 is an ongoing global health crisis with prevention and treatment recommendations rapidly changing. Rapid response telephone triage and advice services are critical in providing timely care during pandemics. Understanding patient participation with triage recommendations and factors associated with patient participation can assist in developing sensitive and timely interventions for receiving the treatment to prevent adverse health effects of COVID-19.

methodsThis cohort study aimed to assess patient participation (percentage of patients who followed nursing triage suggestions from the COVID hotline) and identify factors associated with patient participation in four quarterly electronic health records from March 2020 to March 2021 (Phase 1: 14 March 2020-6 June 2020; Phase 2: 17 June 2020-16 September 2020; Phase 3: 17 September 2020-16 December 2020; Phase 4: 17 December 2020-16 March 2021). All callers who provided their symptoms (including asymptomatic with exposure to COVID) and received nursing triage were included in the study. Factors associated with patient participation were identified using multivariable logistic regression analyses, including demographic variables, comorbidity variables, health behaviors, and COVID-19-related symptoms.

resultsThe aggregated data included 9849 encounters/calls from 9021 unique participants. Results indicated: (1) 72.5% of patient participation rate; (2) participants advised to seek emergency department care had the lowest patient participation rate (43.4%); (3) patient participation was associated with older age, a lower comorbidity index, a lack of unexplained muscle aches, and respiratory symptoms. The absence of respiratory symptoms was the only factor significantly associated with patient participation in all four phases (OR = 0.75, 0.60, 0.64, 0.52, respectively). Older age was associated with higher patient participation in three out of four phases (OR = 1.01-1.02), and a lower Charlson comorbidity index was associated with higher patient participation in phase 3 and phase 4 (OR = 0.83, 0.88).

conclusionPublic participation in nursing triage during the COVID pandemic requires attention. This study supports using a nurse-led telehealth intervention and reveals crucial factors associated with patient participation. It highlighted the importance of timely follow-up in high-risk groups and the benefit of a telehealth intervention led by nurses serving as healthcare navigators during the COVID-19 pandemic.

Indexed as

COVID-19NursesCohort StudiesHumansPandemicsPatient ParticipationTriageCOVID-19Nursing triagePatient participationSymptom assessment

Identifiers

PMID36890538
PMCPMC9994385
OpenAlexW4323544277

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.