ArticlePediatric surgery international2014
Addressing the variation of post-surgical inpatient census with computer simulation.
Article in Pediatric surgery international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed, 12 citations in OpenAlex.
- Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus.PloS one · 2021Article
- Theoretical bounds and approximation of the probability mass function of future hospital bed demand.Health care management science · 2020Article
- Simulation to Predict Effect of Citywide Events on Emergency Department Operations.Pediatric quality & safetyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study describes the development of a Discrete Event Simulation (DES) of a large pediatric perioperative department, and its use to compare the effectiveness of increasing the number of post-surgical inpatient beds vs. implementing a new discharge strategy on the proportion of patients admitted to the surgical unit to recover. MATERIALS AND
methodsA DES of the system was developed and simulated data were compared with 1 year of inpatient data to establish baseline validity. Ten years of simulated data generated by the baseline simulation (control) was compared to 10 years of simulated data generated by the simulation for the experimental scenarios. Outcome and validation measures include percentage of patients recovering in post-surgical beds vs. "off floor" in medical beds, and daily census of inpatient volumes.
resultsThe proportion of patients admitted to the surgical inpatient unit rose from 79.0% (95% CI, 77.9-80.1%) to 89.4% (95% CI, 88.7-90.0%) in the discharge strategy scenario, and to 94.2% (95% CI, 93.5-95.0%) in the additional bed scenario. The daily mean number of patients admitted to medical beds fell from 9.3 ± 5.9 (mean ± SD) to 4.9 ± 4.5 in the discharge scenario, and to 2.4 ± 3.2 in the additional bed scenario. DISCUSSION: Every hospital is tasked with placing the right patient in the right bed at the right time. Appropriately validated DES models can provide important insight into system dynamics. No significant variation was found between the baseline simulation and real-world data. This allows us to draw conclusions about the ramifications of changes to system capacity or discharge policy, thus meeting desired system performance measures.
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Registered trials
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