Evidence map›Paper›PMID 36006276›Full record

ArticleTropical medicine and infectious disease2022

Facing the Impact of the COVID-19 Pandemic: How Can We Allocate Outpatient Doctor Resources More Effectively?

Xiaojing Hu, Hongjun Fang, Ping Wang

Abstract read
In one paragraph

Article in Tropical medicine and infectious disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Xiaojing HuMedical Affairs Department, Peking University First Hospital, Beijing 100034, China.ORCID 0000-0002-7126-8851
Hongjun FangMedical Affairs Department, Peking University First Hospital, Beijing 100034, China.
Ping WangMedical Affairs Department, Peking University First Hospital, Beijing 100034, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic caused significant damage to global healthcare systems. Previous studies regarding COVID-19’s impact on outpatient numbers focused only on a specific department, lacking research data for multiple departments in general hospitals. We assessed differences in COVID-19’s impact on outpatient numbers for different departments to help hospital managers allocate outpatient doctor resources more effectively during the pandemic. We compared the outpatient numbers of 24 departments in a general hospital in Beijing in 2019 and 2020. We also examined an indicator not mentioned in previous studies, monthly departmental patient reservation rates. The results show that, compared with 2019, 2020 outpatient numbers decreased overall by 33.36%. Ten departments’ outpatient numbers decreased >33.36%; however, outpatient numbers increased in two departments. In 2020, the overall patient reservation rate in 24 departments was 82.22% of the 2019 reservation rate; the rates in 14 departments were <82.22%. Moreover, patient reservation rates varied across different months. Our research shows that COVID-19’s impact on different departments also varied. Additionally, our research suggests that well-known departments will be less affected by COVID-19, as will departments related to tumor treatment, where there may also be an increase in patient numbers. Patient reservation rates are an indicator worthy of attention. We suggest that hospital managers classify departments according to changes in outpatient numbers and patient reservation rates and adopt accurate, dynamic, and humanized management strategies to allocate outpatient doctor resources.

Indexed as

COVID-19doctor resources allocationmanagement strategyoutpatient numberspatient reservation rate

Identifiers

PMID36006276
PMCPMC9416261

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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.