Evidence map›Paper›PMID 40626111›Full record

ArticleAcute medicine & surgery

A stratified approach to hospital surge capacity during the COVID-19 pandemic in Tokyo.

Yuzo Kodaira, Masataka Inokuchi, Taketomo Omomo, Kyoji Okazaki, Yasuhiro Takayama, Haruhide Miyagi, Tomoyo Narita

Abstract read
In one paragraph

Article in Acute medicine & surgery. 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. 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

7 authors.

Yuzo KodairaFlowers & Forest Tokyo Hospital Kitaku Tokyo Japan.ORCID https://orcid.org/0009-0000-4992-3330
Masataka InokuchiSocial Medical Corporation Seishikai Machidashi Tokyo Japan.
Taketomo OmomoSocial Medical Corporation Seishikai Machidashi Tokyo Japan.
Kyoji OkazakiFlowers & Forest Tokyo Hospital Kitaku Tokyo Japan.
Yasuhiro TakayamaFlowers & Forest Tokyo Hospital Kitaku Tokyo Japan.
Haruhide MiyagiFlowers & Forest Tokyo Hospital Kitaku Tokyo Japan.
Tomoyo NaritaTokyo Metropolitan Government Bureau of Public Health Shinjyukuku Tokyo Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Surge capacity preparedness is critical for maintaining adequate healthcare delivery during pandemics and disasters. Urbanization and aging populations have heightened the need for innovative capacity strategies. Temporary hospitals provide a flexible solution, enabling adaptive responses through adjustable bed counts, tailored patient criteria, and strategic activation. Methods: Nine temporary hospitals with 842 beds were established across Tokyo to manage COVID-19 surges, focusing on elderly patients with comorbidities, such as dementia, cardiovascular conditions, and chronic respiratory disorders. Admission criteria included age, absence of ventilator dependency, specific comorbidities, and an expected hospital stay of up to 10 days. Results: Over 2 years, the temporary hospitals admitted more than 10,000 patients, providing surge capacity for outbreaks and reducing the strain on permanent hospitals. Elderly patients requiring prolonged bedside care benefited from comprehensive medical management and rehabilitation, improving their functional outcomes. Conclusion: Temporary hospitals designed for stratified patient populations offer a viable model for managing surge capacity in disaster medicine. This approach underscores the importance of adaptive strategies to address healthcare demands during public health emergencies.

Indexed as

aging populationCOVID‐19hospital crowdingsurge capacitytemporary hospital

Identifiers

PMID40626111
PMCPMC12230340

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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.