System Engineering for dependency analysis - a Bayesian approach: application to obsolescence study

International audienceThroughout its life cycle, systems undergo several modifications in their architecture. These changes target at remaining competitive and responding quickly to new customer requirements. However, any entity change (i.e. component, function or functionality) can produce unexpected consequences, propagated throughout the whole system architecture. It is then necessary to model, predict and control them. System engineering tools and techniques allow dealing with complex systems design. That is why we have developed a novel methodology to analyze changes using a system engineering methodology called ARCADIA, developed by Thales and its associated software Capella. The obtained models allow mapping various kinds of dependencies within a system architecture. The method, presented in this paper, shows how these models are used to integrate change propagation and transform them into Bayesian networks. A set of experiments allows then to obtain insightful pieces of knowledge about the changes propagation. An illustrative case is developed with a focus of particular changes caused by obsolescence of component, function or functionality

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Obsolescence, rarefaction and their propagation

International audienceAll systems are likely to be affected by obsolescence. A component is considered obsolete when it is no longer manufactured or requested. Obsolescence must be taken into account, analysed and resolved effectively as it can lead to temporary or permanent stoppage of a system or make it impossible to repair it. The consequences of an obsolete entity must be minimised as they may have impacts, among others, on system characteristics such as availability or maintainability. These consequences can cascade through the system architecture. Understanding the propagation mechanisms can greatly contribute to obsolescence resilient design, but also to obsolescence management enabling a more effective determination of the scope of impact of actual or predicted obsolescence. The objective of this research work is to propose models to describe such propagation and to understand its principles. In fact, even if the obsolescence or rarefaction propagation is real and commonly accepted, there are no models describing its mechanisms. This paper focuses on two main points. First, it contributes to the concepts of obsolescence and rarefaction. Second, we propose descriptive and mathematical models of obsolescence and rarefaction propagation which allow to describe the likelihood of this propagation through the architecture of the system via two types of identified links, namely dependency and jump-up/jump-downs. These proposals are then illustrated through an example of a monitoring unit for a perfume bottle packaging line. After a discussion of the results obtained, the paper ends with the definition of future research pathways to ease the use of probabilistic inference tools for the exploitation of the proposed models