ArticleLa Tunisie medicale2023
Impact of an approach combining optimization and simulation to improve the service quality of an emergency department.
Article in La Tunisie medicale, 2023. 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.
- Optimizing Emergency Response in Hospitals: A Systematic Review of Surge Capacity Planning and Crisis Resource Management.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEmergency Department (ED) represents a complex system due to its limited resources and random patient arrivals. It is often saturated by a continuous flow of patients, which causes an excessive wait time and length of stay.
aimTo evaluate the impact of the proposed combining approach (optimization with simulation) on improving the performance of an emergency department in terms of patients' length of stay and resources utilization balanced (physicians and nurses).
methodsThis study integrates simulation with optimization to design a planning decision support for an emergency department. First, we used a stochastic mixed integer linear programing (MILP) to find the optimal number of staff and beds while considering the uncertainties on the patients' arrival and service times. Next, we construct a Discrete Event Simulation (DES) model to analyse and evaluate this resources allocation as well as different patient scheduling rules in order to minimize the length of stay and to balance resources utilization.
resultsThe approach is applied to a case study at a Tunisian emergency department. Our analysis indicates that the proposed approach generates the best resources allocation that would significantly reduce the length of stay with an average of 44.55 % and balance the resources utilization.
conclusionThis approach allows helping decision makers to improve the service quality of the ED via key performance indicators. DES and MILP prove to be an effective tool for studying the effects of different scenarios to optimize capacity allocation and to minimize patients' length of stay.
Indexed as
Identifiers
38372538PMC11217955What 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.