Implementation of DMAIC Cycle to Study the Impact of COVID-19 on Emergency Department-LOS

Lean Six Sigma (LSS) is a methodological approach that originated in industry and has, over time, become increasingly popular in healthcare. Its tool-to, the DMAIC cycle, consisting of 5 main steps, offers methodological rigor that helps improve processes by comparing results quantitatively. In this study, the LSS and in particular the DMAIC cycle was used to investigate the impact of COVID-19 on patients' length of stay in the Emergency Department (ED-LOS) of the Evangelical Hospital "Betania” of Naples (Italy). The study revealed a general increase in ED-LOS due mainly to the new steps that the hospital added to the standard flow, such as those for performing screening swabs, and the reduction of treatment stations, with the exception of patients discharged home for whom there was a statistically significant reduction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions

The emergency department (ED) is the most important section in every hospital. The ED behaviour is adequately complex, because the ED has several uncertain parameters such as the waiting time of patients or arrival time of patients. To deal with ED complexities, this paper presents a simulation-based optimisation-based meta-model (S-BO-BM-M) to minimise total waiting time of the arriving patients in an emergency department under COVID-19 conditions. A full-factorial design used meta-modelling approach to identify scenarios of systems to estimate an integer nonlinear programming model for the patient waiting time minimisation under COVID-19 conditions. Findings showed that the S-BO-BM-M obtains the new key resources configuration. Simulation-based optimisation meta-modelling approach in this paper is an invaluable contribution to the ED and medical managers for the redesign and evaluates of current situation ED system to reduce waiting time of patients and improve resource distribution in the ED under COVID-19 conditions to improve efficiency. Copyright © 2023 Inderscience Enterprises Ltd.

Developing Modern Agent Technologies in Combating Covid-19 Exposure: An Application in a Healthcare Facility

This study aims to identify the impact of adherence to Non-Pharmaceutical Interventions (NPI) such as facemask type Cotton Fabric Mask and social distancing on the rate of COVID-19 exposure in waiting areas inside an emergency department. As a methodology, a Multi-Agent Simulation approach was used to model and capture the flow of patients inside the emergency department in this research. Each agent represents a physical entity, including its attributes defined. These agents will collaborate based on the defined rules to achieve the best mimic of the system being modeled. This methodology aims to quantitatively evaluate the performance of preventive measures based on the agent's proximity and exposure time. The number of infections was affected by the application of the facemask. Infections were reduced when facemask adherence and social distancing were applied. The study showed that the application of social distancing has a similar effect to a 20% adherence of agents wearing a facemask. The model also reveals that more agents adhere to the facemask, and the time required to get an agent to the state exposed increases. Waiting areas are a potentially significant contributor to transmission. © 2022 IEEE.

Patient Flow Optimization in an Emergency Department Using SimPy-Based Simulation Modeling and Analysis: A Case Study

Long waiting times and patient congestion are common problems faced by emergency departments (EDs) worldwide. During pandemics like COVID-19, EDs worldwide start to be flooded with patients and hospitals find it very challenging to provide good treatment to the large number of patients visiting the EDs with their current allocation of resources. Hospitals are in need of a decision support system (DSS) which can predict the excess demand and suggest the appropriate quantity of resources to be allocated at each point of care. The present research focuses on an ED of a large public hospital in India and explores in finding a solution for the long patient waiting time problem experienced by the hospital. This study extends the application domain of SimPy-based simulation modeling with integrated metamodeling and optimization to optimally allocate the resources in the ED. This can be used as a novel DSS which is relatively faster and needs less human interaction by the hospital management compared to the existing methods. The proposed resource allocation by this model reduced the patient waiting time by 44% in the case hospital being studied. Hospitals may use the proposed methodology to appropriately allocate their resources in times of excess demand. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Comparative Machine Learning Approaches for Patient Flow Forecasting in an Emergency Department during the COVID-19

The Corona Virus Disease 2019 (COVID-19) has impacted numerous areas of the health system. In fact, it made the world work remotely during several months and created an assorted uncertainty for medical service recipients. Thus, anticipating novel everyday patient income in relation to the COVID-19 has become pivotal for clinical, political, and different authorities who handle on a daily basis, COVID-19 related planned operations. Current machine learning draws near, in an attempt to get dynamic results. This work intends to demonstrate the wayan Emergency Department (ED) is able to use machine-learning approaches during the daily patient flow forecasting for better management in an emergency department. Thus, it is essential to test five different supervised machine-learning approaches by evaluating their coefficient of determination (R2) to figure the everyday patient flow income for better management. © 2022 IEEE.

Prediction of Length of Stay in the Emergency Department for COVID-19 Patients: A Machine Learning Approach

The coronavirus disease (COVID-19) outbreak has become a global public health threat. The influx of COVID-19 patients has prolonged the length of stay (LOS) in the emergency department (ED) in the United States. Our objective is to develop a reliable prediction model for COVID-19 patient ED LOS and identify clinical factors, such as age and comorbidities, associated with LOS within a “4-hour target.”Data were collected from an urban, demographically diverse hospital in Detroit for all COVID-19 patients’ED presentations from March 16 to December 29, 2020. We trained four machine learning models, namely logistic regression (LR), gradient boosting (GB), decision tree (DT), and random forest (RF), across different data processing stages to predict COVID-19 patients with an ED LOS of less than or greater than 4 hours. The analysis is inclusive of 3,301 COVID-19 patients with known ED LOS, and 17 significant clinical factors were incorporated. The GB model outperformed the baseline classifier (LR) and tree-based classifiers (DT and RF) with an accuracy of 85% and F1-score of 0.88 for predicting ED LOS in the testing data. No significant accuracy gains were achieved through further splitting. This study identified key independent factors from a combination of patient demographics, comorbidities, and ED operational data that predicted ED stay in patients with prolonged COVID-19. The prediction framework can serve as a decision-support tool to improve ED and hospital resource planning and inform patients about better ED LOS estimations. Author

BPMN-Based Simulation Analysis of the COVID-19 Impact on Emergency Departments: A Case Study in Italy

The COVID-19 outbreak, which has been recognized as a pandemic in March 2020, has brought the need to timely face an extraordinary demand of health-related resources and medical assistance. The objective of this work is to analyze the structural and procedural changes that have been enacted in an emergency department (ED), according to guidelines provided by national authorities. Specifically, guidelines deal with how to manage the access of COVID-19 patients, ensure the isolation of suspected cases, execute a proper triage, and identify the appropriate treatment path for all patients. The paper describes a process modeling and simulation-based approach to analyze the treatment of patients accessing the ED of an Italian hospital. The approach makes use of the Business Process Model and Notation standard to specify ED treatment processes before and during the pandemic, so to evaluate different scenarios and effectively support process improvement activities by use of simulation-based what-if analysis. © 2021 IEEE.

Overcrowding in emergency department: A comparison between indexes

The overcrowding of the Emergency Department (ED) represents one of the main problems to be faced with the aim to enhance the services offered in emergency situations and their quality. As a consequence, to assist the patients and the professionals involved, the hospital structures must put in place corrective and preventive actions. Overcrowding has a variety of effects, including poor care and longer hospital stays;consequently, mortality rises and so does the average length of hospitalization in intensive care units. A variety of indices have been exploited in the literature to assess the ED congestion. In this work a comparison between the EDWIN Index and the NEDOCS one was made in order to evaluate their effectiveness. © 2021 ACM.

Towards A Fuzzy Rule-Based Approach for Patient Flow Management in Emergency Department during the COVID-19: Sahloul Hospital Case Study, Tunisia

The Corona Virus Disease 2019 (COVID-19) epidemic has touched many sectors of the health system. It was remote around the world during months and generated a diverse doubt for healthcare beneficiaries. Most hospitals have been opposing constraints in the treatment of the COVID-19 patients, and there is a need to improve patients flow waiting time so that the health staff is less menaced, and more patients can be handled. In this paper, we present a new fuzzy rule-based system for patient flow management in the emergency department, in the case of Sahloul University Hospital one of a big Tunisian Hospitals, in order to contribute reducing the patient flow waiting time and presenting a better management of patient flow during the COVID-19 epidemic phase. © 2021 IEEE.

Effects of Relaxed Lockdown on Pediatric ER Visits during SARS-CoV-2 Pandemic in Italy.

Previously, we demonstrated an 81% reduction in pediatric Emergency Room (ER) visits in Italy during the strict lockdown due to the SARS-CoV-2 pandemic. Since May 2020, lockdown measures were relaxed until 6 November 2020, when a strict lockdown was patchily reintroduced. Our aim was to evaluate the impact of the relaxed lockdown on pediatric ER visits in Italy. We performed a retrospective multicenter study involving 14 Italian pediatric ERs. We compared total ER visits from 24 September 2020 to 6 November 2020 with those during the corresponding timeframe in 2019. We evaluated 17 ER specific diagnoses grouped in air communicable and non-air communicable diseases. We recognized four different triage categories: white, green, yellow and red. In 2020 total ER visits were reduced by 51% compared to 2019 (16,088 vs. 32,568, respectively). The decrease in air communicable diseases was significantly higher if compared to non-air communicable diseases (-64% vs. -42%, respectively). ER visits in each triage category decreased in 2020 compared to 2019, but in percentage, white and red codes remained stable, while yellow codes slightly increased and green codes slightly decreased. Our results suggest that preventive measures drastically reduced the circulation of air communicable diseases even during the reopening of social activities but to a lesser extent with regard to the strict lockdown period (March-May 2020).

Reliability of Body Temperature Measurements Obtained with Contactless Infrared Point Thermometers Commonly Used during the COVID-19 Pandemic.

During the COVID-19 pandemic, there has been a significant increase in the use of non-contact infrared devices for screening the body temperatures of people at the entrances of hospitals, airports, train stations, churches, schools, shops, sports centres, offices, and public places in general. The strong correlation between a high body temperature and SARS-CoV-2 infection has motivated the governments of several countries to restrict access to public indoor places simply based on a person's body temperature. Negating/allowing entrance to a public place can have a strong impact on people. For example, a cancer patient could be refused access to a cancer centre because of an incorrect high temperature measurement. On the other hand, underestimating an individual's body temperature may allow infected patients to enter indoor public places where it is much easier for the virus to spread to other people. Accordingly, during the COVID-19 pandemic, the reliability of body temperature measurements has become fundamental. In particular, a debated issue is the reliability of remote temperature measurements, especially when these are aimed at identifying in a quick and reliable way infected subjects. Working distance, body-device angle, and light conditions and many other metrological and subjective issues significantly affect the data acquired via common contactless infrared point thermometers, making the acquisition of reliable measurements at the entrance to public places a challenging task. The main objective of this work is to sensitize the community to the typical incorrect uses of infrared point thermometers, as well as the resulting drifts in measurements of body temperature. Using several commercial contactless infrared point thermometers, we performed four different experiments to simulate common scenarios in a triage emergency room. In the first experiment, we acquired several measurements for each thermometer without measuring the working distance or angle of inclination to show that, for some instruments, the values obtained can differ by 1 °C. In the second and third experiments, we analysed the impacts of the working distance and angle of inclination of the thermometers, respectively, to prove that only a few cm/degrees can cause drifts higher than 1 °C. Finally, in the fourth experiment, we showed that the light in the environment can also cause changes in temperature up to 0.5 °C. Ultimately, in this study, we quantitatively demonstrated that the working distance, angle of inclination, and light conditions can strongly impact temperature measurements, which could invalidate the screening results.

SARS-CoV-2 Pandemic Impact on Pediatric Emergency Rooms: A Multicenter Study.

From 9 March to 3 May 2020, lockdown was declared in Italy due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Our aim was to evaluate how the SARS-CoV-2 pandemic and related preventive strategies affected pediatric emergency rooms (ERs) during this period. We performed a retrospective cohort multicenter study, comparing the lockdown period to the corresponding period in 2019. We examined 15 Italian pediatric ERs in terms of visit rates, specific diagnoses (grouped as air communicable diseases and non-air communicable diseases), and triage categories. During the lockdown period, ER admissions decreased by 81% compared to 2019 (52,364 vs. 10,112). All ER specific diagnoses decreased in 2020 and this reduction was significantly higher for air communicable diseases (25,462 vs. 2934, p < 0.001). Considering the triage category, red codes remained similar (1% vs. 1%), yellow codes increased (11.2% vs. 22.3%), and green codes decreased (80.3% vs. 69.5%). We can speculate that social distancing and simple hygiene measures drastically reduced the spread of air communicable diseases. The increase in yellow codes may have been related to a delay in primary care and, consequently, in ER admissions.