Evidence map›Paper›PMID 34095453›Full record

ArticleInformatics in medicine unlocked2021

Decision support tool for hospital resource allocation during the COVID-19 pandemic.

Sven Brüggemann, Theodore Chan, Gabriel Wardi, Jess Mandel, John Fontanesi, Robert R Bitmead

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Informatics in medicine unlocked, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Sven BrüggemannMechanical & Aerospace Engineering Department, University of California, San Diego, San Diego, CA, USA.
Theodore ChanUniversity of California, San Diego School of Medicine, San Diego, CA, USA.
Gabriel WardiUniversity of California, San Diego School of Medicine, San Diego, CA, USA.
Jess MandelUniversity of California, San Diego School of Medicine, San Diego, CA, USA.
John FontanesiUniversity of California, San Diego School of Medicine, San Diego, CA, USA.
Robert R BitmeadMechanical & Aerospace Engineering Department, University of California, San Diego, San Diego, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients. In light of such high-demand circumstances, we describe an open web-accessible simulation-based decision support tool for a better use of finite hospital resources. The aim is to explore risk and reward under differing assumptions with a model that diverges from most existing models which focus on epidemic curves and related demand of ward and intensive care beds in general. While maintaining intuitive use, our tool allows randomized "what-if" scenarios which are key for real-time experimentation and analysis of current decisions' down-stream effects on required but finite resources over self-selected time horizons. While the implementation is for COVID-19, the approach generalizes to other diseases and high-demand circumstances.

Indexed as

Contingency planningCOVID-19Emergency planningEnsemble simulationErlang distributionIn-house patient modelingReal-time resource planningResource allocationSARS-CoV-2Scenario simulationStaffingWeb application

Identifiers

PMID34095453
PMCPMC8168305

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.