Efficiency measures of emergency departments: an Italian systematic literature review

Evolution of Hospitalisation Due to Stroke in Italy Before and After the Outbreak of the COVID-19 Epidemic: A Population-Based Study Using Administrative Data

Background/Objectives: Stroke is a leading cause of mortality and disability worldwide, ranking as the second most common cause of death and the third in disability-adjusted life-years lost. Ischaemic stroke, which constitutes the majority of cases, poses significant public health and economic challenges. This study evaluates trends in ischaemic stroke hospitalisations in Italy from 2008 to 2022, focusing on differences before and after the COVID-19 pandemic. Methods: We analysed ischaemic stroke hospitalisations among individuals admitted through emergency services using Italian hospital discharge records from 2008 to 2022. Poisson Inverse Gaussian regression was employed to assess hospitalisation trends, accounting for age, sex, and geographic variations. Results: Among 1,689,844 ischaemic stroke hospitalisations, there was a marked age-related increase, particularly among individuals aged 74 and older, with males consistently showing higher rates. Hospitalisation trends demonstrated a 20% reduction over 15 years, suggesting improvements in stroke prevention and treatment. However, there was a slight increase in rates during the COVID-19 period, despite the overall declining trend, highlighting the potential healthcare challenges experienced during the pandemic. Multivariable analysis confirmed age and male sex as significant risk factors. Conclusions: This study underscores the age-related increase in stroke hospitalisation rates, emphasising the need for targeted prevention strategies for elderly populations. The overall reduction in stroke hospitalisation rates reflects advancements made in healthcare, although the impact of COVID-19 on access to stroke care is evident. Future policies must address the pandemic's effects on stroke care continuity and prioritise interventions tailored to age and sex.

Configurations associated with the efficiency of the ophthalmology departments in public hospitals of Central South China

BACKGROUND

Improving the efficiency of ophthalmology service is a global challenge to fight vision impairment, yet there is little concrete evidence of the current efficiency status. This study aimed to examine the efficiency of ophthalmology departments in the Hunan Province, China, and determine the associating factors of low-efficiency and high-efficiency ophthalmology departments.

METHODS

This cross-sectional study included a province-level survey of ophthalmology departments of public hospitals. All the ophthalmology departments of public hospitals in Hunan Province were invited to complete an online survey on ophthalmic competence resources. Bootstrap Data Envelopment Analysis was conducted to describe the service efficiency status of the ophthalmology departments using Maxdea (version 8.0) software. Then, we employed Fuzzy Set-Qualitative Comparative Analysis to explore the recipes of low-efficiency and high-efficiency ophthalmology departments using Fs-QCA (version 3.0) software.

RESULTS

One hundred and ninety-five ophthalmology departments (87 in tertiary and 108 in secondary public hospitals) completed the survey. The mean efficiency score was 0.78 for ophthalmology departments in tertiary hospitals and 0.82 for secondary hospitals. The number of ophthalmologists and equipment positively contributed to the efficiency of ophthalmology departments in tertiary and secondary hospitals. While increasing the bed capacity was not always beneficial to improving the efficiency of ophthalmology departments in secondary hospitals. For ophthalmology departments in tertiary hospitals, simply increasing the number of nurses did not universally increase efficiency unless there were enough ophthalmologists and equipment to support the nurses' work. This study also revealed 2 configurations for ophthalmology departments in secondary hospitals and 5 configurations for those in tertiary hospitals that could guide their efficiency improvement efforts.

CONCLUSIONS

Moderate efficiency levels in ophthalmology departments at both tertiary and secondary hospitals were found. Prioritizing the number of ophthalmologists and equipment was recommended to achieve high efficiency for ophthalmology departments in tertiary and secondary hospitals. We also proposed that blindly increasing the number of beds and nurses was meaningless, and ophthalmology departments should flex the bed capacity and number of nurses after premising having high numbers of ophthalmologists and equipment.

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study

A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, without any evaluation by a healthcare provider (Leave Without Being Seen, LWBS). This has an impact on the hospital in terms of missing income from lost opportunities to offer treatment and, in general, of negative outcomes from the ED process. Consequently, healthcare managers must be able to forecast and control patients who leave the ED without being evaluated in advance. This study is a retrospective analysis of patients registered at the ED of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) during the years 2014-2021. The goal was firstly to analyze factors that lead to patients abandoning the ED without being examined, taking into account the features related to patient characteristics such as age, gender, arrival mode, triage color, day of week of arrival, time of arrival, waiting time for take-over and year. These factors were used as process measures to perform a correlation analysis with the LWBS status. Then, Machine Learning (ML) techniques are exploited to develop and compare several LWBS prediction algorithms, with the purpose of providing a useful support model for the administration and management of EDs in the healthcare institutions. During the examined period, 688,870 patients were registered and 39188 (5.68%) left without being seen. Of the total LWBS patients, 59.6% were male and 40.4% were female. Moreover, from the statistical analysis emerged that the parameter that most influence the abandonment rate is the waiting time for take-over. The final ML classification model achieved an Area Under the Curve (AUC) of 0.97, indicating high performance in estimating LWBS for the years considered in this study. Various patient and ED process characteristics are related to patients who LWBS. The possibility of predicting LWBS rates in advance could be a valid tool quickly identifying and addressing "bottlenecks" in the hospital organization, thereby improving efficiency.

The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study

BACKGROUND

The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets.

OBJECTIVE

This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED.

METHODS

We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS.

RESULTS

The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred.

CONCLUSIONS

Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

Patient flow in emergency departments: a comprehensive umbrella review of solutions and challenges across the health system

BACKGROUND

Globally, emergency departments (EDs) are overcrowded and unable to meet an ever-increasing demand for care. The aim of this study is to comprehensively review and synthesise literature on potential solutions and challenges throughout the entire health system, focusing on ED patient flow.

METHODS

An umbrella review was conducted to comprehensively summarise and synthesise the available evidence from multiple research syntheses. A comprehensive search strategy was employed in four databases alongside government or organisational websites in March 2023. Gray literature and reports were also searched. Quality was assessed using the JBI critical appraisal checklist for systematic reviews and research syntheses. We summarised and classified findings using qualitative synthesis, the Population-Capacity-Process (PCP) model, and the input/throughput/output (I/T/O) model of ED patient flow and synthesised intervention outcomes based on the Quadruple Aim framework.

RESULTS

The search strategy yielded 1263 articles, of which 39 were included in the umbrella review. Patient flow interventions were categorised into human factors, management-organisation interventions, and infrastructure and mapped to the relevant component of the patient journey from pre-ED to post-ED interventions. Most interventions had mixed or quadruple nonsignificant outcomes. The majority of interventions for enhancing ED patient flow were primarily related to the 'within-ED' phase of the patient journey. Fewer interventions were identified for the 'post-ED' phase (acute inpatient transfer, subacute inpatient transfer, hospital at home, discharge home, or residential care) and the 'pre-ED' phase. The intervention outcomes were aligned with the aim (QAIM), which aims to improve patient care experience, enhance population health, optimise efficiency, and enhance staff satisfaction.

CONCLUSIONS

This study found that there was a wide range of interventions used to address patient flow, but the effectiveness of these interventions varied, and most interventions were focused on the ED. Interventions for the remainder of the patient journey were largely neglected. The metrics reported were mainly focused on efficiency measures rather than addressing all quadrants of the quadruple aim. Further research is needed to investigate and enhance the effectiveness of interventions outside the ED in improving ED patient flow. It is essential to develop interventions that relate to all three phases of patient flow: pre-ED, within-ED, and post-ED.

Combining simulation models and machine learning in healthcare management: strategies and applications

Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligentin-silicomodels of healthcare processes and to provide effective translation to the clinics.

A proposal for formal fairness requirements in triage emergency departments: publicity, accessibility, relevance, standardisability and accountability

This paper puts forward a wish list of requirements for formal fairness in the specific context of triage in emergency departments (EDs) and maps the empirical and conceptual research questions that need to be addressed in this context in the near future. The pandemic has brought to the fore the necessity for public debate about how to allocate resources fairly in a situation of great shortage. However, issues of fairness arise also outside of pandemics: decisions about how to allocate resources are structurally unavoidable in healthcare systems, as value judgements underlie every allocative decision, although they are not always easily identifiable. In this paper, we set out to bridge this gap in the context of EDs. In the first part, we propose five formal requirements specifically applied for ED triage to be considered fair and legitimate: publicity, accessibility, relevance, standardisability and accountability. In the second part of the paper, we map the conceptual and empirical ethics questions that will need to be investigated to assess whether healthcare systems guarantee a formally just ED triage. In conclusion, we argue that there is a vast research landscape in need of an in-depth conceptual and empirical investigation in the context of ED triage in ordinary times. Addressing both types of questions in this context is vital for promoting a fair and legitimate ED triage and for fostering reflection on formal fairness allocative issues beyond triage.

Emergency department crowding: an overview of reviews describing measures causes, and harms

Crowding in Emergency Departments (EDs) has emerged as a global public health crisis. Current literature has identified causes and the potential harms of crowding in recent years. The way crowding is measured has also been the source of emerging literature and debate. We aimed to synthesize the current literature of the causes, harms, and measures of crowding in emergency departments around the world. The review is guided by the current PRIOR statement, and involved Pubmed, Medline, and Embase searches for eligible systematic reviews. A risk of bias and quality assessment were performed for each review, and the results were synthesized into a narrative overview. A total of 13 systematic reviews were identified, each targeting the measures, causes, and harms of crowding in global emergency departments. Key among the results is that the measures of crowding were heterogeneous, even in geographically proximate areas, and that temporal measures are being utilized more frequently. It was identified that many measures are associated with crowding, and the literature would benefit from standardization of these metrics to promote improvement efforts and the generalization of research conclusions. The major causes of crowding were grouped into patient, staff, and system-level factors; with the most important factor identified as outpatient boarding. The harms of crowding, impacting patients, healthcare staff, and healthcare spending, highlight the importance of addressing crowding. This overview was intended to synthesize the current literature on crowding for relevant stakeholders, to assist with advocacy and solution-based decision making.

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

Caesarean section (CS) rate has seen a significant increase in recent years, especially in industrialized countries. There are, in fact, several causes that justify a CS; however, evidence is emerging that non-obstetric factors may contribute to the decision. In reality, CS is not a risk-free procedure. The intra-operative, post-pregnancy risks and risks for children are just a few examples. From a cost point of view, it must be considered that CS requires longer recovery times, and women often stay hospitalized for several days. This study analyzed data from 12,360 women who underwent CS at the “San Giovanni di Dio e Ruggi D’Aragona” University Hospital between 2010 and 2020 by multiple regression algorithms, including multiple linear regression (MLR), Random Forest, Gradient Boosted Tree, XGBoost, and linear regression, classification algorithms and neural network in order to study the variation of the dependent variable (total LOS) as a function of a group of independent variables. We identify the MLR model as the most suitable because it achieves an R-value of 0.845, but the neural network had the best performance (R = 0.944 for the training set). Among the independent variables, Pre-operative LOS, Cardiovascular disease, Respiratory disorders, Hypertension, Diabetes, Haemorrhage, Multiple births, Obesity, Pre-eclampsia, Complicating previous delivery, Urinary and gynaecological disorders, and Complication during surgery were the variables that significantly influence the LOS. Among the classification algorithms, the best is Random Forest, with an accuracy as high as 77%. The simple regression model allowed us to highlight the comorbidities that most influence the total LOS and to show the parameters on which the hospital management must focus for better resource management and cost reduction.

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

Background: In recent years, the length of hospital stay (LOS) following endarterectomy has decreased significantly from 4 days to 1 day. LOS is influenced by several common complications and factors that can adversely affect the patient’s health and may vary from one healthcare facility to another. The aim of this work is to develop a forecasting model of the LOS value to investigate the main factors affecting LOS in order to save healthcare cost and improve management. Methods: We used different regression and machine learning models to predict the LOS value based on the clinical and organizational data of patients undergoing endarterectomy. Data were obtained from the discharge forms of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy). R² goodness of fit and the results in terms of accuracy, precision, recall and F1-score were used to compare the performance of various algorithms. Results: Before implementing the models, the preliminary correlation study showed that LOS was more dependent on the type of endarterectomy performed. Among the regression algorithms, the best was the multiple linear regression model with an R² value of 0.854, while among the classification algorithms for LOS divided into classes, the best was decision tree, with an accuracy of 80%. The best performance was obtained in the third class, which identifies patients with prolonged LOS, with a precision of 95%. Among the independent variables, the most influential on LOS was type of endarterectomy, followed by diabetes and kidney disorders. Conclusion: The resulting forecast model demonstrates its effectiveness in predicting the value of LOS that could be used to improve the endarterectomy surgery planning.

A case study to investigate the impact of overcrowding indices in emergency departments

Background

Emergency department (ED) overcrowding is widespread in hospitals in many countries, causing severe consequences to patient outcomes, staff work and the system, with an overall increase in costs. Therefore, health managers are constantly looking for new preventive and corrective measures to counter this phenomenon. To do this, however, it is necessary to be able to characterize the problem objectively. For this reason, various indices are used in the literature to assess ED crowding. In this work, we explore the use of two of the most widespread crowding indices in an ED of an Italian national hospital, investigate their relationships and discuss their effectiveness.

Methods

In this study, two of the most widely used indices in the literature, the National Emergency Department Overcrowding Scale (NEDOCS) and the Emergency Department Working Index (EDWIN), were analysed to characterize overcrowding in the ED of A.O.R.N. “A. Cardarelli” of Naples, which included 1678 clinical cases. The measurement was taken every 15 minutes for a period of 7 days.

Results

The results showed consistency in the use of EDWIN and NEDOCS indices as measures of overcrowding, especially in severe overcrowding conditions. Indeed, in the examined case study, both EDWIN and NEDOCS showed very low rates of occurrence of severe overcrowding (2–3%). In contrast, regarding differences in the estimation of busy to overcrowded ED rates, the EDWIN index proved to be less sensitive in distinguishing these variations in the occupancy of the ED. Furthermore, within the target week considered in the study, the results show that, according to both EDWIN and NEDOCS, higher overcrowding rates occurred during the middle week rather than during the weekend. Finally, a low degree of correlation between the two indices was found.

Conclusions

The effectiveness of both EDWIN and NEDOCS in measuring ED crowding and overcrowding was investigated, and the main differences and relationships in the use of the indices are highlighted. While both indices are useful ED performance metrics, they are not always interchangeable, and their combined use could provide more details in understanding ED dynamics and possibly predicting future critical conditions, thus enhancing ED management.

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

The proximal fracture of the femur and hip is the most common reason for hospitalization in orthopedic departments. In Italy, 115,989 hip-replacement surgeries were performed in 2019, showing the economic relevance of studying this type of procedure. This study analyzed the data relating to patients who underwent hip-replacement surgery in the years 2010-2020 at the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno. The multiple linear regression (MLR) model and regression and classification algorithms were implemented in order to predict the total length of stay (LOS). Lastly, using a statistical analysis, the impact of COVID-19 was evaluated. The results obtained from the regression analysis showed that the best model was MLR, with an R² value of 0.616, compared with XGBoost, Gradient-Boosted Tree, and Random Forest, with R² values of 0.552, 0.543, and 0.448, respectively. The t-test showed that the variables that most influenced the LOS, with the exception of pre-operative LOS, were gender, age, anemia, fracture/dislocation, and urinary disorders. Among the classification algorithms, the best result was obtained with Random Forest, with a sensitivity of the longest LOS of over 89%. In terms of the overall accuracy, Random Forest and Gradient-Boosted Tree achieved a value of 71.76% and an error of 28.24%, followed by Decision Tree, with an accuracy of 71.13% and an error of 28.87%, and, finally, Support Vector Machine, with an accuracy of 65.06% and an error of 34.94%. A significant difference in cardiovascular disease, fracture/dislocation, and post-operative LOS variables was shown by the chi-squared test and Mann-Whitney test in the comparison between 2019 (before COVID-19) and 2020 (in full pandemic emergency conditions).

Lean Management Approach for Reengineering the Hospital Cardiology Consultation Process: A Report from AORN "A. Cardarelli" of Naples

Background: Consultations with specialists are essential for safe and high-quality care for all patients. Cardiology consultations, due to a progressive increase in cardiology comorbidities, are becoming more common in hospitals prior to any type of treatment. The appropriateness and correctness of the request, the waiting time for delivery and the duration of the visit are just a few of the elements that can affect the quality of the process. Methods: In this work, a Lean approach and Telemedicine are used to optimize the cardiology consultancy process provided by the Cardiology Unit of “Antonio Cardarelli” Hospital of Naples (Italy), the largest hospital in the southern Italy. Results: The application of corrective actions, with the introduction of portable devices and telemedicine, led to a reduction in the percentage of waiting for counseling from 29.6% to 18.3% and an increase in the number of patients treated. Conclusions: The peculiarity of the study is to apply an innovative methodology such as Lean Thinking in optimizing the cardiology consultancy process, currently little studied in literature, with benefits for both patients and medical staff.