Greta Falavigna

Priorities for emergency department syncope research

STUDY OBJECTIVES There is limited evidence to guide the emergency department (ED) evaluation and management of syncope. The First International Workshop on Syncope Risk Stratification in the Emergency Department identified key research questions and methodological standards essential to advancing the science of ED-based syncope research. METHODS We recruited a multinational panel of syncope experts. A preconference survey identified research priorities, which were refined during and after the conference through an iterative review process. RESULTS There were 31 participants from 7 countries who represented 10 clinical and methodological specialties. High-priority research recommendations were organized around a conceptual model of ED decisionmaking for syncope, and they address definition, cohort selection, risk stratification, and management. CONCLUSION We convened a multispecialty group of syncope experts to identify the most pressing knowledge gaps and defined a high-priority research agenda to improve the care of patients with syncope in the ED.

Judicial productivity, delay and efficiency: A Directional Distance Function (DDF) approach

The aim of this paper is twofold. On the one hand, it provides a contribution to the debate on judicial efficiency by conducting an applied research on the Italian tax judiciary thanks to a database covering the activities of the Italian tax courts over a 3-year period (2009–2011). On the other hand, it also contributes to the methodological debate, as it compares results obtained with Data Envelopment Analysis (DEA) and Directional Distance Function (DDF), two related non-parametric techniques which allow evaluating the efficiency of each observation as the radial distance from the efficient frontier defined by the best observations. While DEA has already been used to assess the mere technical efficiency of judicial systems, the DDF offers a valuable additional contribution, since it makes it possible to minimize the social cost of production of adjudication in the measurement. This feature makes it particularly attractive in those sectors in which production externalities may arise, such as judicial delays in the case investigated here. Additionally, the paper first applies the bootstrap to the DDF procedure in order to provide more robust estimates and to compare them with the DEA results.

The impact of hybrid public and market-oriented financing mechanisms on the scientific portfolio and performances of public research labs: a scientometric analysis

The scientific problem of this study is the analysis of the portfolio of outputs by public research labs in the presence of hybrid funding scheme based on public and market-oriented financing mechanisms. Research institutes are considered Decision Making Units, which produce two different kinds of scientific outputs using inputs. We consider some scientific outputs with more international visibility (High Visibility Outputs-HVOs) than others called Low Visibility Outputs (LVOs). We confront this problem by a scientometric approach applying models of the Directional Output Distance Function, which endeavours to measure and analyze the effects of hybrid financing of public research labs in terms of potential loss in high quality scientific outputs, in particular when the share of market-oriented funds is beyond a specific threshold. Results, considering R&D organizations of “hard sciences”, seem to show that a hybrid financing scheme, too market-oriented for supporting operation (and survival) of research labs, tends to affect scientific output portfolio by lowering scientific performances and HVOs. The study here also proposes a preliminary analysis of the optimal level of market financing in relation to total financial resources for a fruitful co-existence of market and public funding scheme to maximize the scientific output (publications) of R&D labs. The findings show main differences across scientific departments and some critical weaknesses points and threats by public research labs for production of scientific outputs.

Hospital organization and performance: a directional distance function approach

The present study considers the Italian healthcare system, investigating the aspects that might affect the efficiency of Italian hospitals. The authors analyze what influences a specific definition of efficiency, which is calculated maximizing healthcare production but minimizing potential financial losses. In other words, this work considers efficient each hospital which is able to maximize the production of medical treatments while complying, at the same time, with budget constraints. Hence, the results of this paper are twofold: from the organizational point of view, they underline the need for rebalancing the various administrative levels of hospitals; from the technical point of view, a more coherent model is proposed in order to account for all the aspects of the healthcare industry.

Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective.

The primary goal of Emergency Department (ED) physicians is to discriminate between individuals at low risk, who can be safely discharged, and patients at high risk, who require prompt hospitalization. The problem of correctly classifying patients is an issue involving not only clinical but also managerial aspects, since reducing the rate of admission of patients to EDs could dramatically cut costs. Nevertheless, a trade-off might arise due to the need to find a balance between economic interests and the health conditions of patients. This work considers patients in EDs after a syncope event and presents a comparative analysis between two models: a multivariate logistic regression model, as proposed by the scientific community to stratify the expected risk of severe outcomes in the short and long run, and Artificial Neural Networks (ANNs), an innovative model. The analysis highlights differences in correct classification of severe outcomes at 10 days (98.30% vs. 94.07%) and 1 year (97.67% vs. 96.40%), pointing to the superiority of Neural Networks. According to the results, there is also a significant superiority of ANNs in terms of false negatives both at 10 days (3.70% vs. 5.93%) and at 1 year (2.33% vs. 10.07%). However, considering the false positives, the adoption of ANNs would cause an increase in hospital costs, highlighting the potential trade-off which policy makers might face.

An analysis of the key-variables of default risk using complex systems

This paper presents a method for the definition of the variables that determine the default. The choice of key-variables in default risk analysis is very important for banks and firms. Indeed, when a firm goes bankrupt, serious problems arise for all the stakeholders of the firm, as well as, for all subjects having relations with the failed subject. A feed-forward neural network with back propagation is used in this paper to classify firms and to divide them according to two groups: failed and not failed firms. The analysis of the model results shows that 13 variables, out of the 41 variables introduced in the network, are those that determine the default. This result is validated by using different techniques, such as the cluster analysis. Moreover, this study also finds that, by reducing the data introduced in the model, neural network models provide a very good performance. The conclusion is that Artificial Neural Network (ANN) models are able to identify key-variables for default risk.

New contents and perspectives in the risk analysis of enterprises

This paper presents a survey of techniques used for default risk analysis and it illustrates the reasons why a large number of researchers study the insolvency of firms. Firstly, there is an introduction on Basel II focusing on the first pillar and the new standards dictated by the New Basel Capital Accord (as reference, see: International Convergence of Capital Measurement and Capital Standards, Basel Committee on Banking Supervision, June 2004, Bank for International Settlements). This is followed by brief remarks about default definition and the following sections analyse different methods used for the study of default risk focusing on artificial neural network methodologies. The goal of this work is to understand if it is possible to use complex systems for the analysis of default risk and which model is the best.

The Economic Impact of Clinical Research in an Italian Public Hospital: The Malignant Pleural Mesothelioma Case Study

Background: The current economic constraints cause hospital management to use the available public resources as rationally as possible. At the same time, there is the necessity to improve current scientific knowledge. This is even more relevant in the case of patients with malignant pleural mesothelioma (MPM), given the severity of the disease, its dismal prognosis, and the cost of chemotherapy drugs. This work aims to evaluate the standard cost of patients with MPM, supporting physicians in their decision-making process in relation to budget constraints, as well as policy-makers with respect research policy. Methods: The authors conducted a retrospective cost analysis on all the patients with MPM who were first admitted to a reference hospital specialized in MPM care between 2014 and 2015, collecting data on their diagnostic pathways and active treatments, as well as on the related official fees for each procedure. Then, using a multiple regression model, we estimated the overall expected cost of a patient with MPM treated in our hospital, to be born by the Regional Healthcare System based on the chosen clinical pathway. Results: According to results, the economic impact of caring for a patient with MPM is mostly related to the selected active treatments, with drug and hospitalization costs as main drivers. Our analysis suggests that the expected reimbursed fee to care for a patient with MPM is equal to € 18 214.99, with chemotherapy and monitoring costs equal to € 12 861.43 and hospitalization cost equal to € 5353.55. This cost decreases to € 320.18 in the case of enrollment in an experimental trial of first-line treatment. In the other cases (second-line or third-line trials), the expected cost borne by the healthcare system for treating patients grows exponentially (€ 40,124.18 and € 59 839.94, respectively). Conclusion: Experimental trials might be a solution to decrease the economic burden for the public healthcare system only in the case of first-line treatments, where the cost of chemotherapy is relevant. Nevertheless, policy-makers have to accept the sharing of this economic burden between society and the pharmaceutical industry to broaden the current scientific knowledge.

Judicial Efficiency and Immigrant Entrepreneurs

Extensive research exists on the individual determinants of being an immigrant entrepreneur, concerning both social environment and human capital. However, the role of the judiciary has not been investigated yet. Analyzing more than 160,000 new micro enterprises owned by immigrants, our paper aims to fill this gap by focusing on the relation between justice and immigrant entrepreneurship. Results show that judicial efficiency is one of the determinants of self‐employment, although some differences among immigrant groups are identified. Therefore, the study confirms the key role of judicial enforcement in promoting not only growth but also the integration of these new citizens.

A rating model simulation for risk analysis

This study analyses the situation of a bank that wants to create an Internal Rating System (IRB). A credit institute can decide to simulate rating judgements from an external rating agency, like Standard and Poors or Moodys or Fitch Rating. This research compares different frameworks of neural networks, hybrid neuro-fuzzy model and logit/probit model, used to simulate the rating of an external agency. Initially, the models are divided into eight rating classes but the mean percentage error is big. Hence, a two-stage hybrid neuro-fuzzy framework is built, in which the model correctly distinguishes the firms into three macroclasses and then, for each macroclass, a hybrid model divides the firms into eight different classes. This two-stage framework provides good results.