Evidence map›Paper›PMID 28032261›Full record

ArticleHealth care management science2018

Identifying congestion levels, sources and determinants on intensive care units: the Portuguese case.

Diogo Ferreira, Rui Cunha Marques

Abstract read
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In one paragraph

Article in Health care management science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
8.8field-weighted citation impact, top 2% of its field
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

4 citing papers in PubMed, 23 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Observational
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

2 authors at 1 institution in 1 country.

Diogo FerreiraCESUR, CERIS, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon, Portugal. diogo.cunha.ferreira@tecnico.ulisboa.pt.ORCID http://orcid.org/0000-0001-5418-9337
Rui Cunha MarquesCESUR, CERIS, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
University of Lisbon · PT

Funding

Fundação para a Ciência e a Tecnologia SFRH/BD/113038/2015
6 · The paper itself

Abstract

Healthcare systems are facing a resources scarcity so they must be efficiently managed. On the other hand, it is commonly accepted that the higher the consumed resources, the higher the hospital production, although this is not true in practice. Congestion on inputs is an economic concept dealing with such situation and it is defined as the decreasing of outputs due to some resources overuse. This scenario gets worse when inpatients' high severity requires a strict and effective resources management, as happens in Intensive Care Units (ICU). The present paper employs a set of nonparametric models to evaluate congestion levels, sources and determinants in Portuguese Intensive Care Units. Nonparametric models based on Data Envelopment Analysis are employed to assess both radial and non-radial (in)efficiency levels and sources. The environment adjustment models and bootstrapping are used to correct possible bias, to remove the deterministic nature of nonparametric models and to get a statistical background on results. Considerable inefficiency and congestion levels were identified, as well as the congestion determinants, including the ICU specialty and complexity, the hospital differentiation degree and population demography. Both the costs associated with staff and the length of stay are the main sources of (weak) congestion in ICUs. ICUs management shall make some efforts towards resource allocation to prevent the congestion effect. Those efforts shall, in general, be focused on costs with staff and hospital days, although these congestion sources may vary across hospitals and ICU services, once several congestion determinants were identified.

Indexed as

Efficiency, OrganizationalDemographyHospital AdministrationHumansIntensive Care UnitsLength of StayModels, TheoreticalPortugalWorkforceBootstrapCongestionData Envelopment AnalysisEnvironment adjustmentIntensive Care Units

Identifiers

PMID28032261
OpenAlexW2563676973

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.