ReviewHeliyon2025
A compartmental model to describe acute medical in-patient flow through a hospital.
Review in Heliyon, 2025. 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.
- Risk Assessment of Emergency Medical Resource Exhaustion Under Major Infectious Disease Outbreaks: Based on Discrete-Event Simulation Models.Risk management and healthcare policy · 2026Article
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
Managing patient flow in hospitals is a critical part of effective secondary care. Considering a hospital as a multi-compartment system through which patients pass, we will derive relevant equations to allow a description of patient flow through these departments as a series of interconnected dynamic relationships. These relationships are determined by many factors, some known, and many interdependent. We do not need to, and indeed cannot, know all of these factors (as needed for discrete event simulation, or agent-based modelling), but will merely examine the net changes between compartments (i.e. a 'system dynamics' approach). Using this approach, we were able to identify two relevant states of equilibrium: the first (trivial) is when the hospital is empty; the second, is when there is activity. We plan to use bed usage data from a UK hospital in an attempt to validate this methodology and then assess its generalizability.
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.