Evidence map›Paper›PMID 34703382›Full record

ArticleJournal of family & community medicine

Telemedicine monitoring of high-risk coronavirus disease 2019 (COVID-19) patients by family medicine service after discharge from the emergency department.

Imran Khalid, Maryam Imran, Manahil Imran, Saifullah Khan, Muhammad A Akhtar, Khadijah Amanullah, Tabindeh J Khalid

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Article in Journal of family & community medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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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.

Imran KhalidDepartment of Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.
Maryam ImranShifa College of Medicine, Islamabad, Pakistan.
Manahil ImranShifa College of Medicine, Islamabad, Pakistan.
Saifullah KhanDepartment of Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.
Muhammad A AkhtarDepartment of Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.
Khadijah AmanullahDepartment of Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.
Tabindeh J KhalidDepartment of Family Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUp to 25% of the total coronavirus disease 2019 (COVID-19) admissions comprise patients with comorbidities who present to the emergency department (ED) with only mild-to-moderate disease. It is unclear whether as an alternative to hospitalization, telemedicine can be used to monitor these "high-risk" comorbid patients. The aim of our study was to answer this question by comparing the outcome of such patients discharged under a family medicine service (FMS) telemonitoring program and those admitted to hospital. MATERIALS AND

methodsPatients with three or more risk factors for progression to severe COVID-19 disease were designated as "high-risk" in our study. In the absence of acute indication for hospitalization, these high-risk patients with mild-to-moderate disease were discharged home under the supervision of FMS led telemonitoring between October 2020 and February 2021 and were labelled as "Telemedicine group." They were compared to similar patients who were admitted to hospital between March-August 2020 before the implementation of telemedicine service (TMS) and were taken as "Control group." Outcome measures included intubation, number of inpatient days, 28-day mortality and cost analysis for the two groups.

resultsOut of 572 COVID-19 patients who presented to the ED, 70 met the inclusion criteria for the "Telemedicine Group" and 35 were included in the "Control Group". In the Telemedicine group, 21 (30.0%) patients were brought back to ED for re-evaluation and 16 (22.9%) were eventually admitted to the hospital. There was no difference in terms of oxygen requirements, intubation, and intensive care unit admission (

conclusionFamily medicine-led telemonitoring of high-risk COVID-19 patients presenting to the ED with mild-to-moderate disease is a feasible and cost-effective alternative to hospitalization.

Indexed as

Cost effectiveCOVID-19dischargeemergency departmentfamily medicinehigh-risktelemedicine

Identifiers

PMID34703382
PMCPMC8496702

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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.