Emergency department resource optimisation for improved performance: a review

Abstract

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.

Diurnal variation in SARS-CoV-2 PCR test results: Test accuracy may vary by time of day

“…First, a 2-fold variation in test sensitivity could be used to optimize test collection time, result interpretation, and patient counselling. Additionally, a shedding cycle that peaks in the afternoon may play a role in community and hospital spread if it occurs when patients are more likely to interact with others or seek medical care (10). Such temporal considerations may be leveraged to maximize the effectiveness of non-pharmaceutical intervention strategies to contain further viral spread and even vaccination strategies.…”

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

“…The healthcare sector of every country has a sensitive setting and plays an important role in national politics. Any deviation from the optimal health service places additional pressure on healthcare professionals [9]. The main concern regarding healthcare services is the issue of overcrowding in ED, which has been thoroughly studied [30,31].…”

“…Notably, the function of ED is to treat patients in a critical or life-threatening situation and not to deal with patients that present low-acuity injuries or illnesses [8]. Thus, ED is the most challenging element of the healthcare system [9]. Patients expect well-organized services from a customer perspective.…”

“…Several ED simulations models have been proposed to continuously improve ED services, maximize ED resources, and eventually ensure patient satisfaction and provide effective treatment services [9,13,14]. However, only a few ED models examine the operational patient flow process in ED with respect to reducing patient throughput time [15,16].…”

“…Throughput time is also one of the essential aspects that exert significant effects on ED operations (e.g., patient flow) [21,22] Although the usefulness of existing models that were developed to address the patient throughput time issue has been established, a close examination reveals that these models cannot sufficiently address the issue of patient throughput time. The issue of patient throughput time is affected by several causative factors, including waiting time, length of stay (LoS), and decision-making [9,22], but existing models have failed to sufficiently handle these causative factors [9] because these models do not sufficiently consider all causative factors that affect the issue of throughput time in ED [18]. Thus, patient throughput time must be improved by minimizing patients' waiting time and LoS and considering decision-making factors [23].…”