The role of GP’s compensation schemes in diabetes care: evidence from panel data

The design of incentive schemes that improve quality of care is a central issue for the healthcare sector. Nowadays we observe many pay-for-performance programs, where payment is contingent on meeting indicators of provider effort, but also other alternative strategies have been introduced, for example programs rewarding physicians for participation in diseases management plans. Although it has been recognised that incentive-based remuneration schemes can have an impact on GP behaviour, there is still weak empirical evidence on the extent to which such programs influence health outcomes. We investigate the impact of financial incentives in Regional and Local Health Authority contracts for primary care in the Italian Region Emilia Romagna for the years 2003-05. We focus on avoidable hospitalisations (Ambulatory Care Sensitive Conditions) for patients affected by type 2 diabetes mellitus, for which the assumption of responsibility and the adoption of clinical guidelines are specifically rewarded. We estimate a panel count data model using a Negative Binomial distribution to test the hypothesis that, other things equal, patients under the responsibility of GPs receiving a higher share of their income through these programs are less likely to experience avoidable hospitalisations. Our findings support the hypothesis that financial transfers may contribute to improve quality of care, even when they are not based on the ex-post verification of performances.

Predict Cellular network traffic with markov logic

Forecasting spatio-temporal data is a challenging task in transportation scenarios involving agents. In this paper, we propose a statistical relational learning approach to cellular network traffic forecasting, that exploits spatial relationships between close cells in the network grid. The approach is based on Markov logic networks, a powerful framework that combines first-order logic and graphical models into a hybrid model capable of handling both uncertainty in data, and background knowledge of the problem. Experimental results conducted on a real-world data set show the potential of using such information. The proposed methodology can have a strong impact in mobility demand forecasting and in transportation applications

Poka Yoke Meets Deep Learning: A Proof of Concept for an Assembly Line Application

In this paper, we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The “as-is” line was highly inefficient due to several issues, including the age of the machines, a non-optimal arrangement of the shop floor, and the absence of process standards. The assembly line issues were analysed with Lean Manufacturing tools, identifying irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the “to-be” line included actions to update the department layout, modify the assembly process, and design the line feeding system in compliance with the concepts of Golden Zone (i.e., the horizontal space more ergonomically and easily accessible by the operator) and Strike Zone (i.e., the vertical workspace setup in accordance to ergonomics specifications). The re-engineering process identified a critical problem in the incorrect assembly of the oil seals, mainly caused by the difficulty in visually identifying the correct side of the component, due to different reasons. Convolutional neural networks were used to address this issue. The proposed solution resulted to be a Poka Yoke. The whole re-engineering process induced a productivity increase that is estimated from 46% to 80%. The study demonstrates how Lean Manufacturing tools together with deep learning technologies can be effective in the development of smart manufacturing lines

Remembering Eliahu de Luna Montalto (1567-1616)

Born in Portugal and the son of Marranos (Christianized Jews from Spain), Eliahu de Luna Montalto lived during a particularly harsh period for the Jewish people. Throughout Europe, the situation for Jews was unfavorable; laws had been passed forbidding them to live in England for the past 300 years, and for the past 200 years in France. Additionally, in France, while Jews were permitted to study at some universities, the practice of medicine was forbidden to them. It is within this context that Eliahu de Luna Montalto, who had returned to his original faith (Judaism), was recruited to the French court. This paper pays tribute to Montalto’s life and medical practice—so exemplary that the Queen of France would ask Montalto to serve at the court and receive Papal permission for Montalto openly to observe his faith as a Jew, this despite the objections of the King of France