Medical malpractice claim risk in emergency departments

Medical malpractice claims are a major problem for emergency physicians and for the health system which must be addressed in a rational and effective fashion: claim analysis seems the best way to identify risk factors and risk areas and to elaborate risk management recommendations. The Emergency Department (ED) is one of the areas at higher risk. Medical diagnoses associated with the highest number of claims are acute myocardial infarction, fractures, appendicitis, abdominal/pelvic symptoms, aortic aneurism and open wounds to fingers. The present paper emphasizes the necessity for ED emergency physicians to pay special attention when facing these health conditions and seeks to provide indications in order to reduce litigation

MecWilly 3D: supporto all’orientamento topografico nei bambini in età scolare attraverso un serious game per la raccolta differenziata

I robot e gli applicativi per smartphone e tablet assumono un ruolo sempre più attivo a supporto dello sviluppo e potenziamento delle abilità nei bambini. A partire dagli ultimi anni della scuola dell'infanzia (4-5 anni) una delle abilità più importanti che affrontano i bambini è l'orientamento topografico, con particolare riferimento alla relatività delle posizioni (destra e sinistra) degli oggetti rispetto ad un determinato punto. L'utilizzo delle ICT e della robotica, associate al conflitto socio-cognitivo offrono molteplici soluzioni per supportare al meglio l'acquisizione e il potenziamento di tali abilità di orientamento spaziale a partire già dai 5 anni. In questo contesto entra in gioco MecWilly, il quale funge da mezzo scatenante di questo processo. MecWilly è un robot umanoide alto 1 metro e 20 cm, realizzato con materiali economici e di recupero, grazie alla collaborazione con il Dipartimento di Psicologia dell'Università di Bologna e il Comune di Rimini. Il robot MecWilly viene utilizzato per studiare il conflitto socio-cognitivo nei bambini in età pre-scolare e scolare. L'obiettivo di questa tesi è realizzare una applicazione 3D che ha la finalità di sviluppare e migliorare le abilità nell'orientamento topografico (mappe) e nell'orientamento spaziale (contesto) dei bambini della scuola primaria. L’applicazione 3D, che riproduce fedelmente l’esperimento originale, offre la possibilità di poter mettere a confronto l’efficacia dell’applicazione utilizzata su tablet con quella in ambiente reale, in quanto il bambino è posizionato frontalmente rispetto al robot e, dunque, ha lo stesso punto di vista creato nella situazione reale. Il progetto è la realizzazione di un videogioco 3D che simuli il funzionamento e le dinamiche dell'esperimento con il robot reale

Analisi, progettazione e sviluppo di un software mobile per il turismo

Analisi, progettazione e sviluppo di un’applicazione che fornisca agli utenti la possibilità di consultare le informazioni riguardanti i punti d’interesse della città fornendo un’esperienza di tour guidato

Joint modeling of claim frequencies and behavioral signals in motor insurance

Telematics devices installed in insured vehicles provide actuaries with new risk factors, such as the time of the day, average speeds, and other driving habits. This paper extends the multivariate mixed model describing the joint dynamics of telematics data and claim frequencies proposed by Denuit et al. (2019a) by allowing for signals with various formats, not necessarily integer-valued, and by replacing the estimation procedure with the Expected Conditional Maximization algorithm. A numerical study performed on a database related to Pay-How-You-Drive, or PHYD motor insurance illustrates the relevance of the proposed approach for practice

Detection of silica particles in lung tissue by environmental scanning electron microscopy

For pathologists, pneumologists, and occupational and environmental physicians it is relevant to know silica levels in lung tissue to better define limits of exposure. Environmental Scanning Electron Microscopy (ESEM) has been employed to detect silica particles and to compare silica levels in subjects with and without Lung Cancer (LC). We investigated 25 paraffin-embedded tissue samples of patients with LC adenocarcinoma, and 20 fresh samples of subjects without LC deceased for extra-pulmonary diseases. Silica levels were quantified considering the Number of Spots of silica particles (NS), and the Number of Positive Zones (NPZ) in which there was at least one spot. Levels of NS and NPZ were assessed with Poisson-type regression models, and in two samples of silica-exposed workers with LC the performance of models were evaluated. LC patients displayed higher silica levels, as compared to controls; smoking, age and gender had no significant effects on this relationship. Values of NS and NPZ for the exposed workers were in agreement with model estimates. The fitted model between NS and NPZ might be useful in evaluating new observations and in the development of threshold limit values of silica in biological tissues. ESEM is a rapid, simple and valid tool for the determination of silica levels in lung tissues

Algorithm for Individual Prediction of COVID-19–Related Hospitalization Based on Symptoms: Development and Implementation Study

BackgroundThe COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. ObjectiveThis study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. MethodsA predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. ResultsThe predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept –0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. ConclusionsA simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic