Evidence map›Paper›PMID 36163103›Full record

ArticleHealth policy (Amsterdam, Netherlands)2022

Methodology for restarting hospital activities after a pandemic: COVID-19 experience.

Fabian Dehanne, Kevin Lejeune, Benoit Libert

Abstract read
In one paragraph

Article in Health policy (Amsterdam, Netherlands), 2022. 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
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.

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

3 authors.

Fabian DehanneInstitut de Recherche Santé et Société (IRSS) - UCLouvain; Department Executive Manager, CHU UCL Namur. Electronic address: Fabian.dehanne@chuuclnamur.uclouvain.be.
Kevin LejeuneDepartment of Medico-Economic Information, CHU UCL Namur.
Benoit LibertDepartment Executive Manager, CHU UCL Namur.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In addition to the health responsibility of hospitals in managing this COVID-19 crisis, hospital managers must also ensure the financial viability of healthcare structures. This is why, at the dawn of a lockdown exit, managers must anticipate the identification of recovery scenarios. This point refers in particular to the selection and scale of progression of hospital activities, and also to the impacts this will have on staff and patients in the short and medium term. Unfortunately, there is a serious lack of literature on the subject. The aim of this document is therefore to propose a methodology for resuming the medical, economic and social activities of a healthcare network or hospital. In our approach, we identify 6 stages following the COVID-19 peak: assessment of the situation, Act 2, development of scenarios-criteria-conditions, restarting, continuous improvement, and transversal activities. The entirety of our developed methodology is supported by a pragmatic approach with, in particular, the creation of specific tools for each stage of the process. This strategy and these tools have been created with the operational players and adapted to meet the specific features of each hospital while respecting the coherence of the healthcare network's decisions. We are convinced that this approach can be exported on a larger scale to inspire other healthcare networks and other hospitals that have also found themselves without the weapons to prepare for the resumption of hospital activities.

Indexed as

COVID-19PandemicsCommunicable Disease ControlHospitalsHumansSARS-CoV-2COVID-19Hospital activitiesLockdown exitStrategy

Identifiers

PMID36163103
PMCPMC9487165

What OpenQuestion holds

Textmetadata
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.