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.

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study.

Background: Recently, crowding in emergency departments (EDs) has become a recognised critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds available in inpatients units and EDs. The length of stay in the ED (ED-LOS) has been found to be a significant indicator of ED bottlenecks. The time a patient spends in the ED is quantified by measuring the ED-LOS, which can be influenced by inefficient care processes and results in increased mortality and health expenditure.

Predicting In-Hospital Mortality During the COVID-19 Pandemic in Patients with Heart Failure: A Single-Center Exploratory Study.

The aim of this study was to investigate whether exposure to the pandemic was associated with increased in-hospital mortality for health failure. We collected data from patients hospitalized between 2019 and 2020 and we assessed the likelihood of in-hospital death. Although the positive association of exposure to the COVID period with an increased in-hospital mortality is not statistically significant, this may underscore other factors that may influence mortality.

COVID-19 in Eye Surgery: The Case of a University Hospital.

Coronavirus epidemic has quickly become a global health threat. The ophthalmology department, like all other departments, have adopted resource management and personnel adjustment maneuvers. The aim of this work was to describe the impact of covid on the Ophthalmology Department of University Hospital "Federico II" of Naples.

Prediction of Hospital Length Stay for Patients Undergoing Mastectomy.

The prolonged length of stay is an important aspect to be considered for the healthcare management since this affect both the health-related expenditure of the hospital and the quality of the offered service. In the light of these consideration is important for hospitals to be able to predict the LOS of patients and to work on the principal aspect affecting it in order to reduce LOS as much as possible. In this work we focus on patients undergoing mastectomy.

Modeling Cholecystectomy Hospital Stay Through a Linear Approach.

Cholecystectomy is among the most frequent procedures in general surgery. In the healthcare facility organization, it is important to evaluate all interventions and procedures that have a great impact on health management and that have a clear effect on the Length of Stay (LOS). The LOS represents, in fact, an indicator of performance and measure the goodness of a health process.

The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery.

The revolutions of recent years in health care have involved several areas ranging from patient treatment to resource management. Therefore, several strategies have been put in place to increase patient value while trying to reduce spending. Several indicators have arisen to evaluate the performance of healthcare processes.

Effect of Clinical and Demographic Variables on the Hospital Stay of Patients Undergoing Total Knee Arthroplasty.

The knee is the joint most affected by osteoarthritis and in its severe form can significantly affect people's physical and functional abilities. The increased demand for surgery leads to greater attention by health care management to be able to keep costs down. A major expense item for this procedure is Length of Stay (LOS).

A Regressive Model to Study the Hospitalization for Laparoscopic Appendectomy: A Multicenter Study.

Appendicitis is a most common abdominal condition worldwide, and appendectomy especially laparoscopic appendectomy is among the most commonly performed general surgeries. In this study, data were collected from patients who underwent laparoscopic appendectomy surgery at the Evangelical Hospital "Betania" in Naples, Italy. Linear multiple regression was used to obtain a simple predictor that can also assess which of the independent variables considered to be a risk factor.