Emergency departments (EDs) have been becoming increasingly congested due to the combined impacts of growing demand, access block and increased clinical capability of the EDs. This congestion has known to have adverse impacts on the performance of the healthcare services. Attempts to overcome with this challenge have focussed largely on the demand management and the application of system wide process targets such as the "four-hour rule" intended to deal with access blocks. In addition, EDs have introduced various strategies such as "fast tracking", "enhanced triage" and new models of care such as introducing nurse practitioners aimed at improving throughput. However, most of these practices require additional resources. Some researchers attempted to optimise the resources using various optimisation models to ensure best utilisation of resources to improve patient flow. However, not all modelling approaches are suitable for all situations and there is no critical review of optimisation models used in hospital EDs. The aim of this article is to review various analytical models utilised to optimise ED resources for improved patient flow and highlight benefits and limitations of these models. A range of modelling techniques including agent-based modelling and simulation, discrete-event simulation, queuing models, simulation optimisation and mathematical modelling have been reviewed. The analysis revealed that every modelling approach and optimisation technique has some advantages and disadvantages and their application is also guided by the objectives. The complexity, interrelationships and variability of ED-related variables make the application of standard modelling techniques difficult. However, these models can be used to identify sources of flow obstruction and to identify areas where investments in additional resources are likely to have most benefit.
Objective To evaluate the Canadian Syncope Risk Score (CSRS) in syncope patients presenting to the ED from an economic perspective, using very‐low and low‐risk patients (CSRS −3 to 0) as a threshold for avoiding hospital admissions. Methods A decision‐analytic model, specifically a decision‐tree, was developed to evaluate application of the CSRS. A hypothetical cohort of 1000 patients was modelled based on characteristics and outcome of patients enrolled in a clinical validation study performed alongside this evaluation. Several analytic based approaches were used to handle model outputs and uncertainties. Results For a cohort of 1000 patients, applying the CSRS was associated with 169 less inpatient admissions from the ED, when compared to usual care. There was also a cost‐saving of $8255 per admitted patient, when the CSRS was applied, compared to usual care. Adopting the CSRS was the optimal approach in all scenario analyses and was robust to changes in model parameters. More than three‐quarters (78.6%) of all model simulations showed that applying the CSRS is a cost‐saving approach to managing syncope. There was high confidence in all results, with the approach using the CSRS reducing the costs and number of syncope‐related hospital admissions. Conclusions Compared to usual care, applying the CSRS appeared as a cost‐effective strategy. This new evidence will help decision‐makers choose cost‐effective approaches for the management of patients presenting to the ED with syncope, as they search for efficient ways to maximise health gain from a finite budget.
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