Implementation and validation of a new method to model 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

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson’s disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson’s disease

Agile six sigma in healthcare: Case study at santobono pediatric hospital

Healthcare is one of the most complex systems to manage. In recent years, the control of processes and the modelling of public administrations have been considered some of the main areas of interest in management. In particular, one of the most problematic issues is the management of waiting lists and the consequent absenteeism of patients. Patient no-shows imply a loss of time and resources, and in this paper, the strategy of overbooking is analysed as a solution. Here, a real waiting list process is simulated with discrete event simulation (DES) software, and the activities performed by hospital staff are reproduced. The methodology employed combines agile manufacturing and Six Sigma, focusing on a paediatric public hospital pavilion. Different scenarios show that the overbooking strategy is effective in ensuring fairness of access to services. Indeed, all patients respect the times dictated by the waiting list, without “favouritism”, which is guaranteed by the logic of replacement. In a comparison between a real sample of bookings and a simulated sample designed to improve no-shows, no statistically significant difference is found. This model will allow health managers to provide patients with faster service and to better manage their resources. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

A six sigma DMAIC methodology as a support tool for health technology assessment of two antibiotics

Health Technology Assessment (HTA) and Six Sigma (SS) have largely proved their reliability in the healthcare context. The former focuses on the assessment of health technologies to be introduced in a healthcare system. The latter deals with the improvement of the quality of services, reducing errors and variability in the healthcare processes. Both the approaches demand a detailed analysis, evidence-based decisions, and efficient control plans. In this paper, the SS is applied as a support tool for HTA of two antibiotics with the final aim of assessing their clinical and organizational impact in terms of postoperative Length Of Stay (LOS) for patients undergoing tongue cancer surgery. More specifically, the SS has been implemented through its main tool, namely the DMAIC (Define, Measure, Analyse, Improve, Control) cycle. Moreover, within the DMAIC cycle, a modelling approach based on a multiple linear regression analysis technique is introduced, in the Control phase, to add complementary information and confirm the results obtained by the statistical analysis performed within the other phases of the SS DMAIC. The obtained results show that the proposed methodology is effective to determine the clinical and organizational impact of each of the examined antibiotics, when LOS is taken as a measure of performance, and guide the decision-making process. Furthermore, our study provides a systematic procedure which, properly combining different and well-assessed tools available in the literature, demonstrated to be a useful guidance for choosing the right treatment based on the available data in the specific circumstance

A rare case of solitary fibrous tumour of the pelvis in an 18-year-old young man: Ct and mri features with pathologic correlations

Solitary fibrous tumors (SFTs) are mesenchymal neoplasms of fibroblastic origin, even if commonly seen in the pleura, they can occur anywhere in the body. SFT presents as a slow growing, often asymptomatic mass, generally affecting middle-aged adults regardless of the sex. We report a rare case of an 18-year-old man referred to our institution to perform computed tomography (CT) and magnetic resonance imaging (MRI), to investigate a pelvic mass incidentally discovered at abdominal ultrasound examination. A well circumscribed, heterogenous and hypervascular lesion was described at imaging, with absence of calcifica-tions, hemorrhage, necrosis nor cystic degeneration. The mass removal was performed via the Da Vinci-assisted robotic surgery. Histopathological evaluation confirmed the diagnosis of SFT. CT and MRI can aid the identification of SFT, providing useful information which needs to be supported by histopathological analysis

Healthcare professional and manager perceptions on drivers, benefits, and challenges of telemedicine: results from a cross-sectional survey in the Italian NHS

Background: The Covid-19 pandemic provided new challenges and opportunities for patients and healthcare providers while accelerating the trend of digital healthcare transformation. This study explores the perspectives of healthcare professionals and managers on (i) drivers to the implementation of telemedicine services and (ii) perceived benefits and challenges related to the use of telemedicine across the Italian National Health Service. Methods: An online cross-sectional survey was distributed to professionals working within 308 healthcare organisations in different Italian regions. Quantitative and qualitative data were collected through a self-administered questionnaire (June-September 2021). Responses were analysed using summary statistics and thematic analysis. Results: Key factors driving the adoption of telemedicine have been grouped into (i) organisational drivers (reduce the virus spread-80%; enhance care quality and efficiency-61%), (ii) technological drivers (ease of use-82%; efficacy and reliability-64%; compliance with data governance regulations-64%) and (iii) regulatory drivers (regulations’ semplification-84%). Nearly all respondents perceive telemedicine as useful in improving patient care (96%). The main benefits reported by respondents are shorter waiting lists, reduced Emergency Department attendance, decreased patient and clinician travel, and more frequent patient-doctor interactions. However, only 7% of respondents believe that telemedicine services are more effective than traditional care and 66% of the healthcare professionals believe that telemedicine can’t completely substitute in-person visits due to challenges with physical examination and patient-doctor relationships. Other reported challenges include poor quality and interoperability of telemedicine platforms and scarce integration of telemedicine with traditional care services. Moreover, healthcare professionals believe that some groups of patients experience difficulties in accessing and using the technologies due to socio-cultural factors, technological and linguistic challenges and the absence of caregivers. Conclusions: Respondents believe that telemedicine can be useful to complement and augment traditional care. However, many challenges still need to be overcome to fully consider telemedicine a standard of care. Strategies that could help address these challenges include additional regulations on data governance and reimbursements, evidence-based guidelines for the use of telemedicine, greater integration of tools and processes, patient-centred training for clinicians, patient-facing material to assist patients in navigating virtual sessions, different language options, and greater involvement of caregivers in the care process

Data for: CLOD laboratory

This model is a computational agent-based laboratory, implemented in Netlogo language.The file (.nlogo extension) includes the model interface, an information guide, and the code.The model can be simulated using different configurations of model's parameters through Netlogo 5.3.

Comparison of machine learning algorithms to predict length of hospital stay in patients undergoing heart bypass surgery

Patients affected by coronary artery obstruction, generally undergo aortocoronary bypass, an open-heart surgery that considerably affect health care expenditure. Since that, the monitoring and government of Aortocoronary bypass performance may be of help in health care management. In this work we compare various machine learning-based classification algorithms, to determine the length of stay for aortocoronary bypass. Data were collected on a group of 116 patients of the "San Giovanni di Dio e Ruggi D'Aragona"University Hospital of Salerno (Italy). Different socio-demographic, clinical, and organizational factors were taken into consideration as input parameters of the model for carrying out the classification analysis. The predictive capability of each of the tested machine learning algorithms was assessed in terms of accuracy and error percentages in the classification and obtained results were compared. Among the adopted algorithms, the Random Forest showed far better performances than the other ones, with an accuracy level of around 97%, thus potentially suggesting the Random Forest as a reliable predictive tool in the determination of the length of hospital stay of healthcare data for patients undergoing coronary artery bypass surgery

Regional Innovation Systems as Complex Adapative Systems: the case of lagging European regions

Although a large stream of literature is focusing on Regional Innovation Systems and on how to support their development and competitiveness (e.g. Asheim et al., 2011; Doloreux and Parto, 2004; Foray et al., 2011), we are witnessing a strong discrepancy among theoretical frameworks, adopted innovation policies, and related regional performances. In fact, numerous studies have shown that RISs with similar industrial structures and characteristics can strongly differ from each other even in terms of innovation and competitive performance. This gap is more evident in the case of the so-called lagging regions (characterized by moderate and modest level of innovativeness) (Regional Innovation Scoreboard, 2009; 2012; 2014), notwithstanding the adoption of specific policies and incentives. Evidently, there is something deeper than the failure of an innovation policy devoted to support the innovation and economic growth. The emergent viewpoint is that regional performances are affected by powerful inertial mechanisms and dimensions, which are undervalued by both researchers and policy-makers (Egbetokun et al., 2017). Discovering the virtuous mechanisms of most innovative regions, and the vicious ones of lagging regions, should be the key goal of every regional innovation policy. According to this, the paper aims to address the following research questions: What are the resources, competencies and mechanisms able to support the so-called lagging regions to trigger virtuous innovation and economic growth processes? What are the main barriers which hinder the development of effective innovation processes notwithstanding the public incentives