ArticleCritical care explorations2020
Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges.
Article in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Use of computer simulation to identify effects on hospital census with reduction of transfers for non-procedural patients in community hospitals.BMJ open quality · 2024Article
- [The SCATTER project: computer-based simulation in the strategic transfer of intensive care patients].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Article
- Flow-Sizing Critical Care Resources.Critical care medicine · 2023Article
- A Path to Real-World Evidence in Critical Care Using Open-Source Data Harmonization Tools.Critical care explorations · 2023Article
- Building on a novel bootstrapping modelling technique to predict region-wide critical care capacity requirements over the next decade.Future healthcare journal · 2023Article
- Cytokine Blockade in Coronavirus Disease 2019: Keeping an Open Mind.Critical care explorations · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAs the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios.
designComplex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU.
settingCombined data from two ICUs in United Kingdom. PATIENTS: The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients.
interventionsNone. MEASUREMENTS AND MAIN
resultsDuring model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization.
conclusionsUsing discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased.
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