Evidence map›Paper›PMID 35300264›Full record

ArticleInternational journal of health sciences

Impact of one-stop clinic on the clearance of COVID-19 surgical backlog.

Yasir AlShareef, Sami A AlShammary, Yacoub Abuzied, Yahya AlAsseri, Khalid I AlQumaizi

Abstract read
In one paragraph

Article in International journal of health sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Yasir AlShareefDepartment of Surgery, College of Medicine, Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
Sami A AlShammaryDepartment of Palliative Care, Comprehensive Cancer Center, King Fahad Medical City, Riyadh, Saudi Arabia.
Yacoub AbuziedDepartment of Nursing, Rehabilitation Hospital, King Fahad Medical City, Riyadh, Saudi Arabia.
Yahya AlAsseriDepartment of Northern Business Unit, Health Holding Company, Ministry of Health, Riyadh, Saudi Arabia.
Khalid I AlQumaiziDepartment of Family Medicine, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: During the COVID-19 pandemic, there was a significant decrease in the number of operated elective cases due to the shutdown procedures. This situation created a substantial surgical backlog. Several visits are generally necessary before a surgical decision making and booking the surgery time. Improving patient access by creating "one-stop" clinics might help decrease the number of visits before the surgery. This study investigated the effect of one-stop clinic application as a new care model on COVID-19 surgical backlog clearance. Methods: We conducted a quasi-experimental, interrupted time-series, pretest-posttest study design in the northern areas of Saudi Arabia. The study outcomes were the number of surgeries and the time elapsed from the first seen day to the booked room day. Results: A total of 358 surgeries were included. Of them, 107 (29.9%) surgeries were performed before initiation of the one-stop clinic and 251 (70.1%) after initiation. The median (quartile Q1, Q3) time elapsed from the first seen day until the booked OR day post-one-stop clinic was 10 (6-17), which was significantly lower than pre-one-stop clinic 20 (11-33), Conclusion: One-stop clinics shortened the period between first visit and booking in the operating room. One-stop clinics may alleviate the surgical backlog resulting from the COVID-19 crisis.

Indexed as

clearanceCOVID-19one-stop clinicsurgical backlog

Identifiers

PMID35300264
PMCPMC8905038

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.