The ERM experience of the Lombardy Region as a tool for improving the safety of the regional health care system.

The Regional Center for Healthcare Risk Management and Patient Safety of the Lombardy Region, with the technical partnership of Aon, designed an innovative Healthcare Enterprise Risk Management Model (hereafter HERM) to meet the following objectives: 1) Improve the safety of the Regional Healthcare System through the implementation of methods and tools aimed to identify, analyze and mange in an integrated way all the risks to which are exposed the healthcare companies. 2) Preserve the creation of social value in the medium-long term and the sustainable achievement of strategic and operational objectives. 3) Optimize risk management costs. 4) Reduce/mitigate adverse events in all business processes. 5) Enable the ability to anticipate and react to changes. 6) Establish sound long-term and risk-based strategies. This paper describes the structuring of the overall HERM Model Framework, and the related information flows, the tools supporting the Healthcare Enterprise Risk Management Methodology (such as the Risk Model and the Assessment Metrics) and presents the preliminary result of first experience of Healthcare ERM in Italy.

A modeling study of the impact of treatment policies on the evolution of resistance in sea lice on salmon farms.

Salmonid aquaculture is an important source of nutritious food with more than 2 million tonnes of fish produced each year (Food and Agriculture Organisation of the United Nations, 2019). In most salmon producing countries, sea lice represent a major barrier to the sustainability of salmonid aquaculture. This issue is exacerbated by widespread resistance to chemical treatments on both sides of the Atlantic. Regulation for sea lice management mostly involves reporting lice counts and treatment thresholds, which depending on interpretation may encourage preemptive treatments. We have developed a stochastic simulation model of sea lice infestation including the lice life-cycle, genetic resistance to treatment, a wildlife reservoir, salmon growth and stocking practices in the context of infestation, and coordination of treatment between farms. Farms report infestation levels to a central organisation, and may then cooperate or not when coordinated treatment is triggered. Treatment practice then impacts the level of resistance in the surrounding sea lice population. Our simulation finds that treatment drives selection for resistance and coordination between managers is key. We also find that position in the hydrologically-derived network of farms can impact individual farm infestation levels and the topology of this network can impact overall infestation and resistance. We show how coordination and triggering of treatment alongside varying hydrological topology of farm connections affects the evolution of lice resistance, and thus optimise salmon quality within socio-economic and environmental constraints. Network topology drives infestation levels in cages, treatments, and hence treatment-driven resistance. Thus farmer behaviour may be highly dependent on hydrologically position and local level of infestation.

Automated Segmentation of the Mouse Body Language to Study Stimulus-Evoked Emotional Behaviors.

Understanding the neural basis of emotions is a critical step to uncover the biological substrates of neuropsychiatric disorders. To study this aspect in freely behaving mice, neuroscientists have relied on the observation of ethologically relevant bodily cues to infer the affective content of the subject, both in neutral conditions or in response to a stimulus. The best example of that is the widespread assessment of freezing in experiments testing both conditioned and unconditioned fear responses. While robust and powerful, these approaches come at a cost: they are usually confined within selected time windows, accounting for only a limited portion of the complexity of emotional fluctuation. Moreover, they often rely on visual inspection and subjective judgment, resulting in inconsistency across experiments and questionable result interpretations. To overcome these limitations, novel tools are arising, fostering a new avenue in the study of the mouse naturalistic behavior. In this work we developed a computational tool [stimulus-evoked behavioral tracking in 3D for rodents (SEB3R)] to automate and standardize an ethologically driven observation of freely moving mice. Using a combination of machine learning-based behavioral tracking and unsupervised cluster analysis, we identified statistically meaningful postures that could be used for empirical inference on a subsecond scale. We validated the efficacy of this tool in a stimulus-driven test, the whisker nuisance (WN) task, where mice are challenged with a prolonged and invasive whisker stimulation, showing that identified postures can be reliably used as a proxy for stimulus-driven fearful and explorative behaviors.