Mitigating overcrowding in emergency departments using Six Sigma and simulation: A case study in Egypt

Key performance indicators in emergency department simulation: a scoping review

BACKGROUND

One way to measure emergency department (ED) performance is using key performance indicators (KPIs). Thus, identifying reliable KPIs can be critical in appraising ED performance. This study aims to introduce and classify the KPIs related to ED in simulations through the Balanced Scorecard (BSC) framework.

METHOD

This scoping review was performed in 2024 without any time limitation based on the Arksey and O'Malley framework. The electronic databases of PubMed, Scopus, Web of Science, EMBASE, MathSciNet, Google Scholar, and Persian databases such as IranDoc, MagIran, and SID were searched. The winter simulation conference was also investigated through manual searching. Furthermore, the screening process of included studies was based on the PRISMA reporting checklist. The data were analyzed by content analysis deductively and inductively. The extracted KPIs were coded as analysis units and transferred to the MAXQDA2020 software. Then, the KPIs were integrated and organized based on similarity. Moreover, the two authors discussed disagreements to reach a consensus on the final codes. The final KPIs classification was carried out based on the BSC framework to achieve a holistic view. The BSC is a managerial tool for evaluating organizations' performance via different dimensions. It contains four main dimensions: Customer, Financial, Growth and infrastructure, and Internal Processes. In addition, the management (vision, objectives, and strategies) has been positioned at the heart of the framework.

RESULT

Initially, 4257 articles were retrieved, and 125 articles were included after screening. Finally, 109 KPIs were extracted and classified into five categories. They include input, processing time, cost and revenue, utilization and productivity, and output indicators. Then, each category of KPIs was positioned in the BSC framework dimensions. Additionally, the findings showed that most indicators were related to the time of process indicators.

CONCLUSIONS

The study findings have collected a comprehensive set of KPIs to measure ED performance in simulations. These results can assist policymakers, managers, and researchers in measuring ED performance and help improve ED performance through a holistic view.

Effective Response to Hospital Congestion Scenarios: Simulation-Based Evaluation of Decongestion Interventions

Hospital overcrowding is becoming a major concern in the modern era due to the increasing demand for hospital services. This study seeks to identify effective and efficient ways to resolve the serious problem of congestion in hospitals by testing a range of decongestion strategies with simulated scenarios. In order to determine more efficient solutions, interventions with smaller changes were consistently tested at the beginning through a simulation platform. In addition, the implementation patterns were investigated, which are important to hospital managers with respect to the decisions made to control hospital congestion. The results indicated that diverting a small number of ambulances seems to be more effective and efficient in congestion reduction compared to other approaches. Furthermore, instead of implementing an isolated approach continuously, combining one approach with other strategies is recommended as a method for dealing with hospital overcrowding.

Overcrowding analysis in emergency department through indexes: a single center study

Introduction

Overcrowding in the Emergency Department (ED) is one of the major issues that must be addressed in order to improve the services provided in emergency circumstances and to optimize their quality. As a result, in order to help the patients and professionals engaged, hospital organizations must implement remedial and preventative measures. Overcrowding has a number of consequences, including inadequate treatment and longer hospital stays; as a result, mortality and the average duration of stay in critical care units both rise. In the literature, a number of indicators have been used to measure ED congestion. EDWIN, NEDOCS and READI scales are considered the most efficient ones, each of which is based on different parameters regarding the patient management in the ED.

Methods

In this work, EDWIN Index and NEDOCS Index have been calculated every hour for a month period from February 9th to March 9th, 2020 and for a month period from March 10th to April 9th, 2020. The choice of the period is related to the date of the establishment of the lockdown in Italy due to the spread of Coronavirus; in fact on 9 March 2020 the Italian government issued the first decree regarding the urgent provisions in relation to the COVID-19 emergency. Besides, the Pearson correlation coefficient has been used to evaluate how much the EDWIN and NEDOCS indexes are linearly dependent.

Results

EDWIN index follows a trend consistent with the situation of the first lockdown period in Italy, defined by extreme limitations imposed by Covid-19 pandemic. The 8:00–20:00 time frame was the most congested, with peak values between 8:00 and 12:00. on the contrary, in NEDOCS index doesn’t show a trend similar to the EDWIN one, resulting less reliable. The Pearson correlation coefficient between the two scales is 0,317.

Conclusion

In this study, the EDWIN Index and the NEDOCS Index were compared and correlated in order to assess their efficacy, applying them to the case study of the Emergency Department of “San Giovanni di Dio e Ruggi d’Aragona” University Hospital during the Covid-19 pandemic. The EDWIN scale turned out to be the most realistic model in relation to the actual crowding of the ED subject of our study. Besides, the two scales didn’t show a significant correlation value.

A virtual evaluation of options for managing risk of hospital congestion with minimum intervention

Hospital congestion is a common problem for the healthcare sector. However, existing approaches including hospital resource optimization and process improvement might lead to huge cost of human and physical structure changes. This study evaluated less disruptive interventions based on a hospital simulation model and offer objective reasoning to support hospital management decisions. This study tested a congestion prevention method that estimates hospital congestion risk level (R), and activates minimum intervention when R is above certain threshold, using a virtual hospital created by simulation modelling. The results indicated that applying a less disruptive intervention is often enough, and more cost effective, to reduce the risk level of hospital congestion. Moreover, the virtual implementation approach enabled testing of the method at a more detailed level, thereby revealed interesting findings difficult to achieve theoretically, such as discharging extra two medical inpatients, rather than surgical inpatients, a day earlier on days when R is above the threshold, would bring more benefits in terms of congestion reduction for the hospital.

When patients get stuck: A systematic literature review on throughput barriers in hospital-wide patient processes

Hospital productivity is of great importance to policymakers, and previous research demonstrates that improved hospital productivity can be achieved by directing more focus towards patient throughput at healthcare organizations. There is also a growing body of literature on patient throughput barriers hampering the flow of patients. These projects rarely, however, encompass complete hospitals. Therefore, this paper provides a systematic literature review on hospital-wide patient process throughput barriers by consolidating the substantial body of studies from single settings into a hospital-wide perspective. Our review yielded a total of 2207 articles, of which 92 were finally selected for analysis. The results reveal long lead times, inefficient capacity coordination and inefficient patient process transfer as the main barriers at hospitals. These are caused by inadequate staffing, lack of standards and routines, insufficient operational planning and a lack in IT functions. As such, this review provides new perspectives on whether the root causes of inefficient hospital patient throughput are related to resource insufficiency or inefficient work methods. Finally, this study develops a new hospital-wide framework to be used by policymakers and healthcare managers when deciding what improvement strategies to follow to increase patient throughput at hospitals.

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Process improvement approaches in emergency departments: a review of the current knowledge

Purpose

The healthcare system has been under pressure to provide timely and quality healthcare. The influx of patients in the emergency departments (EDs) is testing the capacity of the system to its limit. In order to increase EDs' capacity and performance, healthcare managers and practitioners are adopting process improvement (PI) approaches in their operations. Thus, this study aims to identify the main PI approaches implemented in EDs, as well as the benefits and barriers to implement these approaches.

Design/methodology/approach

The study is based on a rigorous systematic literature review of 115 papers. Furthermore, under the lens of thematic analysis, the authors present the descriptive and prescriptive findings.

Findings

The descriptive analysis found copious information related to PI approaches implemented in EDs, such as main PIs used in EDs, type of methodological procedures applied, as well as a set of barriers and benefits. Aiming to provide an in-depth analysis and prescriptive results, the authors carried out a thematic analysis that found underlying barriers (e.g. organisational, technical and behavioural) and benefits (e.g. for patients, the organisation and processes) of PI implementation in EDs.

Originality/value

The authors contribute to knowledge by providing a comprehensive review of the main PI methodologies applied in EDs, underscoring the most prominent ones. This study goes beyond descriptive studies that identify lists of barriers and benefits, and instead the authors categorize prescriptive elements that influence these barriers and benefits. Finally, this study raises discussions about the behavioural influence of patients and medical staff on the implementation of PI approaches.

A hybrid analytical model for an entire hospital resource optimisation

Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

Implementation and validation of a new method to model voluntary departures from emergency departments. Running Title: Modeling Voluntary departures from emergency departments

In the literature, several organizational solutions have been proposed for determining the probability of voluntary patient discharge from the emergency department. Here, the issue of self-discharge is analyzed by Markov theory-based modeling, an innovative approach diffusely applied in the healthcare field in recent years. The aim of this work is to propose a new method for calculating the rate of voluntary discharge by defining a generic model to describe the process of first aid using a "behavioral" Markov chain model, a new approach that takes into account the satisfaction of the patient. The proposed model is then implemented in MATLAB and validated with a real case study from the hospital "A. Cardarelli" of Naples. It is found that most of the risk of self-discharge occurs during the wait time before the patient is seen and during the wait time for the final report; usually, once the analysis is requested, the patient, although not very satisfied, is willing to wait longer for the results. The model allows the description of the first aid process from the perspective of the patient. The presented model is generic and can be adapted to each hospital facility by changing only the transition probabilities between states.

Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion

BACKGROUND

Many management tools, such as Discrete Event Simulation (DES) and Lean Healthcare, are efficient to support and assist health care quality. In this sense, the study aims at using Lean Thinking (LT) principles combined with DES to plan a Canadian emergency department (ED) expansion and at meeting the demand that comes from small care centers closed. The project's purpose is reducing the patients' Length of Stay (LOS) in the ED. Additionally, they must be assisted as soon as possible after the triage process. Furthermore, the study aims at determining the ideal number of beds in the Short Stay Unit (SSU). The patients must not wait more than 180 min to be transferred.

METHODS

For this purpose, the hospital decision-makers have suggested planning the expansion, and it was carried out by the simulation and modeling method. The emergency department was simulated by the software FlexSim Healthcare®, and, with the Design of Experiments (DoE), the optimal number of beds, seats, and resources for each shift was determined. Data collection and modeling were executed based on historical data (patients' arrival) and from some databases that are in use by the hospital, from April 1st, 2017 to March 31st, 2018. The experiments were carried out by running 30 replicates for each scenario.

RESULTS

The results show that the emergency department cannot meet expected demand in the initial planning scenario. Only 17.2% of the patients were completed treated, and LOS was 2213.7 (average), with a confidence interval of (2131.8-2295.6) min. However, after changing decision variables and applying LT techniques, the treated patients' number increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7-468.7) min, about 79.0%. The time to be attended after the triage decrease from 404.3 min to 20.8 (19.8-21.8) min, around 95.0%, while the time to be transferred from bed to the SSU decreased by 60.0%. Moreover, the ED reduced human resources downtime, according to Lean Thinking principles.

An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector

Emergency Care Networks (ECNs) were created as a response to the increased demand for emergency services and the ever-increasing waiting times experienced by patients in emergency rooms. In this sense, ECNs are called to provide a rapid diagnosis and early intervention so that poor patient outcomes, patient dissatisfaction, and cost overruns can be avoided. Nevertheless, ECNs, as nodal systems, are often inefficient due to the lack of coordination between emergency departments (EDs) and the presence of non-value added activities within each ED. This situation is even more complex in the public healthcare sector of low-income countries where emergency care is provided under constraint resources and limited innovation. Notwithstanding the tremendous efforts made by healthcare clusters and government agencies to tackle this problem, most of ECNs do not yet provide nimble and efficient care to patients. Additionally, little progress has been evidenced regarding the creation of methodological approaches that assist policymakers in solving this problem. In an attempt to address these shortcomings, this paper presents a three-phase methodology based on Discrete-event simulation, payment collateral models, and lean six sigma to support the design of in-time and economically sustainable ECNs. The proposed approach is validated in a public ECN consisting of 2 hospitals and 8 POCs (Point of Care). The results of this study evidenced that the average waiting time in an ECN can be substantially diminished by optimizing the cooperation flows between EDs.

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rates.

An integrated HFMEA-DES model for performance improvement of general hospitals

Purpose

Healthcare failure mode and effect analysis (HFMEA) identifies potential risks and defines preventive actions to reduce the effects of risks. In addition, a discrete event simulation (DES) could evaluate the effects of every improvement scenario. Consequently, a proposed integrated HFMEA-DES model is presented for quality improvement in a general hospital.

Design/methodology/approach

In the proposed model, HFMEA is implemented first. As any risk in the hospital is important and that there are many departments and different related risks, all defined risk factors are evaluated using the risk priority number (RPN) for which related corrective actions are defined based on experts' knowledge. Then, a DES model is designed to determine the effects of selected actions before implementation.

Findings

Results show that the proposed model not only supports different steps of HFMEA but also is highly in accordance with the determination of real priorities of the risk factors. It predicts the effects of corrective actions before implementation and helps hospital managers to improve performances.

Practical implications

This research is based on a case study in a well-known general hospital in Iran.

Originality/value

This study takes the advantages of an integrated HFMEA-DES model in supporting the limitation of HFMEA in a general hospital with a large number of beds and patients. The case study proves the effectiveness of the proposed approach for improving the performances of the hospital resources.

Assessing the state of lean and six sigma practices in healthcare in Mexico

PURPOSE

The purpose of this study is to assess the state at which lean and six sigma (LSS) are used as a management system to improve the national health system national health system of Mexico.

DESIGN/METHODOLOGY/APPROACH

Cross-sectional survey-research. The survey was administered at 30 different hospitals across six states in Mexico. These were selected using convenience sampling and participants (N = 258) were selected through random/snowball sampling procedures, including from top managers down to front-line staff.

FINDINGS

Only 16 per cent of respondents reported participation in LSS projects. Still, these implementations are limited to using isolated tools, mainly 5s, failure mode and effects analysis (FMEA) and Fishbone diagram, with the lack of training/knowledge and financial resources as the top disabling factors. Overall, LSS has not become systematic in daily management and operations.

RESEARCH LIMITATIONS/IMPLICATIONS

The sampling procedure was by convenience; however, every attempt was made to ensure a lack of bias in the individual responses. If still there was a bias, it is conjectured that this would likely be in overestimating the penetration of LSS.

PRACTICAL IMPLICATIONS

The penetration of LSS management practices into the Mexican health system is in its infancy, and the sustainability of current projects is jeopardized given the lack of systematic integration. Hence, LSS should be better spread and communicated across healthcare organizations in Mexico.

ORIGINALITY/VALUE

This is the first research work that evaluates the use of LSS management practices in a Latin American country, and the first journal paper that focuses on LSS in healthcare in Mexico.