Evidence map›Paper›PMID 36247975›Full record

ArticleSocio-economic planning sciences2022

Planning feasible and efficient operational scenarios for a university hospital through multimethodology.

Maria Stella de Castro Lobo, Marcos Pereira Estellita Lins, Henrique de Castro Rodrigues, Gabriel Martins Soares

Abstract read
In one paragraph

Article in Socio-economic planning sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

The trial behind it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Maria Stella de Castro LoboInstitute for Studies in Public Health (IESC), Federal University of Rio de Janeiro (UFRJ), 21941-598, Avenida Horácio Macedo s/n, Cidade Universitária, lha do Fundão, Rio de Janeiro, Brazil.
Marcos Pereira Estellita LinsProduction Engineering Department, CCET, Federal University of the State of Rio de Janeiro (UNIRIO), 22290-240, Av. Pasteur 458, Urca, Rio de Janeiro, Brazil.
Henrique de Castro RodriguesAlberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), 21941-914, Centro de Tecnologia, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, Brazil.
Gabriel Martins SoaresAlberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), 21941-914, Centro de Tecnologia, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic required managerial and structural changes inside hospitals to address new admission demands, frequently reducing their care capacity for other diseases. In this regard, this study aims to support the recovery of hospital productivity in the post-pandemic context. The major challenge will be to make use of all the resources the institution has obtained (equipment, beds, temporarily hired human resources) and to increase production to meet the existing repressed demand. To support evidence-based decision-making at a major university hospital in Rio de Janeiro, hospital managers and operations research analysts designed an approach based on multiple methodologies. Besides multimethodology, one important novelty of this study is the application of a productivity frontier function to future scenario planning through the quantitative DEA methodology. Concept maps were used to structure the problem and emphasize stakeholders' perspectives. In sequence, data envelopment analysis (DEA) was applied, as it combines benchmarking best practices and assigns weights to inputs and outputs. To guarantee that the efficiency measurement considers all inputs and outputs before any inclusion of expert judgment, the scope was redirected to full dimensional efficient facet, if any, or to maximum efficient faces. The results indicate that production scenarios proposed by stakeholders based on the Ministry of Health parameters overestimate the viable production framework and that the scenario that maintains temporary human resource contracts is more compatible with quality in health provision, teaching, and research. These findings will serve as a basis for decision-making by the governmental agency that provided temporary contracts. The present methodology can be applied in different settings and scales.

Indexed as

Covid-19Data envelopment analysisFull dimensional facetGoal programmingHospital planningPublic health

Identifiers

PMID36247975
PMCPMC9554220

What OpenQuestion holds

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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.