ArticleHealth systems (Basingstoke, England)2020
Improving emergency department resource planning: a multiple case study.
Article in Health systems (Basingstoke, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- An Overview of Hospital Capacity Planning and Optimisation.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sizing and allocating health-care professionals are a critical problem in the management of emergency departments (EDs) managed by a public company in Rio de Janeiro (Brazil). An efficient ED configuration that is cost and time effective must be developed by this company for hospital managers. In this paper, the problem of health-care professional configurations in EDs is modelled to minimise the total labour cost while satisfying patient queues and waiting times as defined by the actual ED capacity and current clinical protocols. To solve this issue, mixed integer linear programming (MILP) that allocates health-care professionals and specifies the amount of professionals who must be hired is proposed. To consider the uncertainties in this environment and evaluate their impacts, a discrete-event simulation model is developed to reflect patient flow. An optimisation and simulation approach is used to search for efficiency leads for different ED configurations. These configurations change depending on the shift and the day of the week.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.