Search

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.

Background. The Covid-19 pandemic has deeply impacted the oncology community. The European Society for Medical Oncology (ESMO) suggests strengthening telemedicine services, reducing clinic visits and switching to subcutaneous or oral, rather than intravenous, therapies whenever possible. Methods. This study was conducted at the Oncology Complex Operating Unit by collecting data on all patients who accessed in 2019-2020. The aim was to understand how Covid-19 affected hospital admissions. Statistical tests and Logistic Regression were implemented. Results. The statistical analysis carried out showed that between 2019 and 2020 there was less use of emergency admission and voluntary discharge, while highlighting the increase in the "Protected with integrated home care"discharge mode. Conclusion. The results show how the European guidelines have improved the health process, from admission with the reduction of emergencies to discharge.

Background: Valvular heart diseases are diseases that affect the valves by altering the normal circulation of blood within the heart. In recent years, the use of valvuloplasty has become recurrent due to the increase in calcific valve disease, which usually occurs in the elderly, and mitral valve regurgitation. For this reason, it is critical to be able to best manage the patient undergoing this surgery. To accomplish this, the length of stay (LOS) is used as a quality indicator. Methods: A multiple linear regression model and four other regression algorithms were used to study the total LOS function of a set of independent variables related to the clinical and demographic characteristics of patients. The study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) in the years 2010–2020. Results: Overall, the MLR model proved to be the best, with an R2 value of 0.720. Among the independent variables, age, pre-operative LOS, congestive heart failure, and peripheral vascular disease were those that mainly influenced the output value. Conclusions: LOS proves, once again, to be a strategic indicator for hospital resource management, and simple linear regression models have shown excellent results to analyze it.

Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision.

Cesarean section (CS) is one of the main causes of hospitalization in developed countries. Although no benefits have been shown for the mother and baby, the frequency of CS has increased over the past few decades. The control of the length of stay (LOS) for such a widespread procedure therefore becomes strategic for any healthcare facility. The aim of this study is to investigate causes and factors that determine an increase in the LOS in the case of CS delivery. Multiple linear regression analysis and machine learning algorithms are used to build and compare different models for LOS prediction, with the purpose of offering a potential support tool for the planning and programming of CS procedures in healthcare facilities.

Knee arthroplasty is one of the most commonly performed procedures within a hospital. The progressive aging of the population and the spread of clinical conditions such as obesity will lead to an increasing use of this procedure. Therefore, being able to make the process related to this procedure more effective and efficient becomes strategic within hospitals, subject to increasingly stringent clinical and financial pressures. A useful parameter for this purpose is the length of stay (LOS), whose early prediction allows for better bed management and resource allocation, models patient expectations and facilitates discharge planning. In this work, the data of 124 patients who underwent knee surgery in the two-year period 2019-2020 at the San Giovanni di Dio and Ruggi d’Aragona university hospital were studied using multiple linear regression and machine learning algorithms in order to evaluate and predict how patient data affect LOS.

Biomedical technologies are the basis of a functioning health system, in particular, medical devices are essential for the prevention, diagnosis, treatment of diseases. However, while developed country hospitals are renewing their fleet of machines by divesting large quantities of biomedical equipment annually, there is a chronic lack of biomedical technology in developing countries to support clinical activities, which could be met by the reuse of used equipment, adapted to the new hospital environment. However, even if the donations of biomedical technologies are generally made with good intentions and not-profit making as in the case under study, obtained results are not what we expected also due to a not perfect communication between donors and recipients and a lack of culture about technology maintenance in the developing countries. At the moment, there is little documented evidence to support these statements. For this reason, the aim of this paper is to quantify the donated medical equipment that are out of service in two different hospitals in Benin. The information was collected on the type of communication existing between donors and beneficiaries and on the type of support that donors provide in terms of staff training, manuals and maintenance. It was observed that more than 50% of the donated equipment is not functional. In addition in more than 70% of the cases the donors do not support the beneficiaries nor training sessions and staff formation are provided. An in-depth assessments of beneficiary structures should be carried out and all donations must be accompanied by initial user training and monitoring by donors regarding the functionality of the system. Donors-beneficiaries communication results as a key elements in the management of health technologies in low-income countries.

The economic cuts suffered by public health have in many cases led to the reduction of beds. In order to optimize the available resources, the length of stay (LOS) can be used as an efficiency parameter. The objective of this study is to predict the value of LOS using the clinical information that is generally supplied by a patient who is hospitalized following a fracture of the neck of the femur and to make a comparison with results obtained after the implementation of the new diagnostic-therapeutic-assistance pathway (DTAP). The analysis was conducted on data extrapolated from the information system of the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno (Italy). The results show promising outcome in the use of the proposed prediction models as a tool for determining an estimate of the LOS and support the decision making process and the management of hospital resources in advance. In addition, the comparison of between the two models can be used as an indicator to assess the efficiency of the implemented DTAP.