Points to consider for prioritizing clinical genetic testing services: a European consensus process oriented at accountability for reasonableness.

Given the cost constraints of the European health-care systems, criteria are needed to decide which genetic services to fund from the public budgets, if not all can be covered. To ensure that high-priority services are available equitably within and across the European countries, a shared set of prioritization criteria would be desirable. A decision process following the accountability for reasonableness framework was undertaken, including a multidisciplinary EuroGentest/PPPC-ESHG workshop to develop shared prioritization criteria. Resources are currently too limited to fund all the beneficial genetic testing services available in the next decade. Ethically and economically reflected prioritization criteria are needed. Prioritization should be based on considerations of medical benefit, health need and costs. Medical benefit includes evidence of benefit in terms of clinical benefit, benefit of information for important life decisions, benefit for other people apart from the person tested and the patient-specific likelihood of being affected by the condition tested for. It may be subject to a finite time window. Health need includes the severity of the condition tested for and its progression at the time of testing. Further discussion and better evidence is needed before clearly defined recommendations can be made or a prioritization algorithm proposed. To our knowledge, this is the first time a clinical society has initiated a decision process about health-care prioritization on a European level, following the principles of accountability for reasonableness. We provide points to consider to stimulate this debate across the EU and to serve as a reference for improving patient management.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Education and Training in Forensic Intelligence: a New Challenge

From recent calls for positioning forensic scientists within the criminal justice system, but also policing and intelligence missions, this paper emphasizes the need for the development of educational and training programs in the area of forensic intelligence, It is argued that an imbalance exists between perceived and actual understanding of forensic intelligence by police and forensic science managers, and that this imbalance can only be overcome through education. The challenge for forensic intelligence education and training is therefore to devise programs that increase forensic intelligence awareness, firstly for managers to help prevent poor decisions on how to develop information processing. Two recent European courses are presented as examples of education offerings, along with lessons learned and suggested paths forward. It is concluded that the new focus on forensic intelligence could restore a pro-active approach to forensic science, better quantify its efficiency and let it get more involved in investigative and managerial decisions. A new educational challenge is opened to forensic science university programs around the world: to refocus criminal trace analysis on a more holistic security problem solving approach.

Repetitive Deliberate Fires: Critical Review of the Situation and Proposal of a Follow-Up Process and Systematic Analysis

Deliberate fires appear to be borderless and timeless events creating a serious security problem. There have been many attempts to develop approaches to tackle this problem, but unfortunately acting effectively against deliberate fires has proven a complex challenge. This article reviews the current situation relating to deliberate fires: what do we know, how serious is the situation, how is it being dealt with, and what challenges are faced when developing a systematic and global methodology to tackle the issues? The repetitive nature of some types of deliberate fires will also be discussed. Finally, drawing on the reality of repetition within deliberate fires and encouraged by successes obtained in previous repetitive crimes (such as property crimes or drug trafficking), we will argue that the use of the intelligence process cycle as a framework to allow a follow-up and systematic analysis of fire events is a relevant approach. This is the first article of a series of three articles. This first part is introducing the context and discussing the background issues in order to provide a better underpinning knowledge to managers and policy makers planning on tackling this issue. The second part will present a methodology developed to detect and identify repetitive fire events from a set of data, and the third part will discuss the analyses of these data to produce intelligence.

Care delivery value chains for ophthalmic clinics in Switzerland

Perceived patient value is often not aligned with the emerging expenses for health care services. In other words, the costs are often supposed as rising faster than the actual value for the patients. This fact is causing major concerns to governments, health plans, and individuals. Attempts to solve the problem have habitually been on the operational effectiveness side: increasing patient volume, minimizing costs, rationing, or closing hospitals, usually resulting in a zero-sum game. Only few approaches come from the strategic positioning side and "competition" among hospitals is still perceived rather as a danger than as a chance to create a positive-sum game and stimulate patient value. In their 2006 book, "Redefining Health Care", the renowned Harvard strategy professor Michael E. Porter and hospital management expert Professor Elizabeth Olmsted Teisberg approach the challenge from the positive-sum perspective: they propose to form Integrated Practice Units (IPUs) and manage hospitals in a modern, patient value oriented way. They argue that creating value-based competition on results should have the same effect on the health care sector like transparency and competition turned other industries with out-dated management models (like recently the inert telecommunication industry) into highly competitive and customer value creating businesses. The objective of this paper is to elaborate Care Delivery Value Chains for Integrated Practice Units in ophthalmic clinics and gather a first feedback from Swiss hospital managers, ophthalmologists, and patients, if such an approach could be a realistic way to improve health care management. First, Porter's definition of competitiveness (distinction between operational effectiveness and strategic positioning) is explained. Then, the Care Delivery Value Chain is introduced as a key element for understanding value-based management, followed by three practice examples for ophthalmic clinics. Finally, recommendations are given how the Care Delivery Value Chain can be managed efficiently and how the obstacles of becoming a patient-oriented organization can be overcome. The conclusion is that increased transparency and value-based competition on results has the potential to change the mindset of hospital managers-which will align patient value with the emerging health care expenses. Early adapters of this management approach will gain a competitive advantage. [Author, p. 6]