The growing elderly population in developed countries highlights the critical need for preventing frailty, which poses significant challenges to health systems due to increased risks of severe health issues. This paper introduces Senselife, a framework that provides explainable service recommendations specifically tailored for frailty prevention. It begins by outlining the medical context and challenges associated with aging, followed by an overview of existing recommender systems with similar objectives. We detail the integration of three key resources-ROR, RNA, and Data Laregion-within the Senselife framework to represent service supply. The paper explains how service supply is structured and the transformation of available data for use within our recommender engine. We introduce the concept of operational activities derived from the ROR and leverage the capabilities of LLMs to incorporate RNA data into Senselife. Additionally, we illustrate how these services are ultimately compiled into recommended service packages. Finally, the paper concludes by summarizing key findings and suggesting potential directions for future research.

As the global elderly population continues to grow, social isolation emerges as a critical factor contributing to the incidence of frailty. This paper explores the integration of social interaction functionalities within the Senselife framework, a service recommendation platform designed for frailty prevention in older adults. We propose enhancements to Senselife that facilitate community-building and social engagement through technology-driven interactions. By leveraging user-centered design, the paper discusses how enhanced social features can significantly improve the efficacy of our frailty prevention strategies, offering a holistic approach to elderly care. Our methodology includes the development of social interaction modules that encourage active participation and connectivity among elderly users, ultimately aiming to enhance their quality of life and reduce the risks associated with social isolation.

The aging global population presents unique challenges, particularly in managing frailty-a condition defined by declines in physical, cognitive, and social capacities. This paper introduces Senselife, a recommender system tailored for frailty management in elderly individuals. Senselife leverages hypergraph-based knowledge models to intelligently recommend personalized services aimed at mitigating frailty and enhancing life quality. Our methodology integrates diverse data types through Heterogeneous Information Networks (HINs), allowing for nuanced userservice interactions that significantly improve recommendation accuracy and relevance. This paper details the development of these models, emphasizing the transition from conventional data handling to advanced, knowledge-driven approaches that consider both user and service complexities. By incorporating these sophisticated models, Senselife aims to provide a scalable solution for frailty prevention, offering a significant contribution to personalized elderly care.

Frailty is a clinical syndrome associated with ageing that characterizes an intermediate state between robust health and loss of autonomy. To preserve the abilities of older adults and prevent dependency, it is important to identify and evaluate their frailty. This approach is part of a dependency prevention strategy, based on a thorough understanding of their medical, social, and living environment. This understanding is usually acquired through significant data collection using standardized evaluation surveys. The obtained data is then analyzed to provide personalized recommendations for the beneficiaries’ lifestyles. Our article presents the concept of frailty and a personalized recommendation system aimed at helping citizens prevent frailty. This system uses an innovative self-assessment approach designed for older adults, without necessarily involving healthcare professionals.

Frailty is a clinical syndrome that commonly occurs in older adults and characterizes an intermediate state between robust health and the loss of autonomy. As such, it is crucial to identify and evaluate frailty to preserve the abilities of older adults and reduce the loss of their functional capabilities. This paper proposes a personalized service recommendation framework designed, to assist senior citizens in selecting appropriate services, to prevent frailty and improve autonomy. This framework is based on a multidimensional evaluation of the elderly person, considering both user's status information and service-related data. It defines a four steps recommendation process, including personal characteristics identification, needs identification, service types identification, and service identification. To support this, both knowledge-based and rule-based approaches are defined and employed to generate personalized recommendations that align with the individual's specific needs. The effectiveness of the proposal is evaluated using different representative scenarios, of which an example is described in detail that demonstrates the developed recommendation process, algorithms and relevant rules.

Frailty presents a significant global health challenge for older adults, necessitating effective interventions to support healthy aging. Technology-based solutions, particularly recommender systems, hold promise in addressing frailty prevention. This paper presents Senselife platform for service recommendation dedicated for frailty prevention.We delve into the challenges associated with developing an engaging and user-friendly recommender system specifically tailored for frailty prevention. Key challenges we address include implementing effective data collection strategies and designing user-centered interfaces. Our proposed recommendation platform leverages self-evaluation to deliver personalized recommendations, with the goal of enhancing the functional capabilities of older adults. By aligning the available services within the elderly environment with the demands they face, our solution tackles the complexities of managing frailty in this population.Throughout this study, we elucidate the construction of our surveys, the main source of data for Senselife and the design considerations behind our user interfaces, highlighting our efforts in overcoming the unique challenges associated with systems dedicated to elderly usage.

Home Health Care Routing and Scheduling Problem (HHCRSP) has been widely investigated in operations research. In this paper, a model based on the Constraint Satisfaction Problem (CSP) is proposed, which is able to deal with daily HHCRSPs. Human factors are considered in our formulation of the problem and we seek a balance between the different stakeholders’ satisfaction criteria. The considered temporal constraints are soft and controlled by the stakeholders’ personalised tolerance and satisfaction rates. We will explain how this new Satisfaction-Oriented HHCRSP (SOH2CRSP) model is built and solved by using an open-source solver: the OptaPlanner. In order to examine the impact of human factors, a study will estimate the added value provided when satisfaction is considered in the problem formulation. The comparison is based on a use case derived from the dataset of an existing HHC organisation. The numerical results will show the benefits of our approach.

Currently in our highly connected society, there is a strong requirement for decision-makers in organizations to coordinate and schedule their activities. Frequently, there are various uncertain factors, multiple objectives, many business knowledge and requirements, which heavily increase the difficulty of decision-making process regarding these issues. Therefore, a decision-maker will appreciate having control over the formulation of decision-making models and being able to adapt to highly dynamic situation. In this paper, we study a Model Driven Engineering (MDE) approach to link the business requirement defined by a model with solution-oriented logical models, which are codes that could be submitted to a combinatorial optimization solver. The design of our proposal follows the principles of three-levels Model Driven Architecture (MDA) and is based on a cognitive process for decision-making systems. Then, several transformation rules between models are explained to realize automatic Model to Model Transformation (M2M) with a special emphasis on the Platform Independent Model (PIM) to Platform Specific Model (PSM) part. To make a proof of our model transformation chain efficiency, a classical Travelling Salesman Problem (TSP) is chosen as a use case.

Improving patient safety and quality of care has become a priority for healthcare organizations, given the frequency and potential severe clinical consequences of medication errors and adverse drug events during the Medication-use Process. Medication error reporting can be one most effective strategies to achieve these goals. Indeed, high error reporting rates indicate a positive safety culture rather than an unsafe healthcare environment. It is through the identification of these errors safety barriers can be put in place to prevent a similar event from occurring in the future. However, current healthcare organizations still suffer from a lack of attention in this context, especially in establishing a digital tool dedicated to medication error reporting, which is the motivation for the work described in this paper. The latter proposes a novel tool to make the local reporting of medication errors easier and encourage reporting these errors with the healthcare professionals' confidentially. The overall response to the tool provided was positive from the staff participating.

Healthcare organizations are environments of high management complexity and are subject to risk. Indeed, risk management is one of the most relevant aspects put forward in the literature which highlights the necessity to perform comprehensive analyses intended to uncover the root causes of risks. However, the healthcare sector still suffers from a lack of attention in this context, especially with regard to the establishment of risk management and process-oriented management, which is the motivation for the study described in this paper. In light of these observations, it would be essential for healthcare organizations to explore new risk management approaches. Contributing to this field, the present paper applies a risk-aware business process management method to work out a systemic methodology to study risks impacting healthcare processes. This framework aims to improve healthcare organizations’ maturity towards risk management. A case study related to the management of potential risks in a given healthcare process shall illustrate the usage of the developed framework.

Les circuits de decision, le rythme de prise de decision, autant que le poids de celles-ci, prennent de nouvelles dimensions dans les organisations. Nous nous interessons a la capacite du decideur a inclure les caracteristiques propres a son ecosysteme, son contexte et ses enjeux, souvent changeants, dans ses pratiques. Les decideurs sont souvent des experts metiers a qui nous essayons de procurer la capacite de concevoir et adapter a moindre effort leurs outils de prise de decision. A cette fin, la creation d’un environnement d’aide a la prise de decision utilisant une approche d’Ingenierie Dirige par les Modele (IDM) est proposee. Ce travail exploratoire cherche a lier des modeles de besoin de prise de decision a des modeles logiques d’appel a un solveur en optimisation combinatoire. Les architectures fonctionnelles et logiques de cet environnement sont decrites sur un cas d’etudes, le probleme du voyageur de commerce. Nous expliquons les hypotheses et les connaissances vehiculees par les modeles peuplant la chaine d’ingenierie en suivant les principes de l’architecture MDA. Plusieurs regles de transformation entre modeles y sont definies. Un prototype de cet environnement a ete developpe a l’aide d’un outil de meta-modelisation. La preuve de concept est sa capacite a reformuler des problemes en ligne.

With aging populations and increasing life expectancy, Home Health Care (HHC) has become an alternative treatment modality for the elderly who want autonomy and well-being as they live longer and longer. Facing this rise in demand, the organisation and coordination of HHC institutions has become increasingly complex and difficult. This paper addresses a short-term Home Health Care Routing and Scheduling Problem (HHCRSP), formulated by Mixed Integer Linear Programming (MILP). The proposed model considers tolerance-based soft constraints in view of both patients and caregivers, with the aim of maximising the total satisfaction of all HHC stakeholders. The problem is solved by the commercial solver CPLEX. We adopt a real dataset extracted from an existing HHC institution. The numerical result on 10 generated instances shows the efficient computing performance for an optimal solution for small and medium-large sizes.

Healthcare processes, such as sterilization, are extremely dynamic, complex, and multidisciplinary, making risk management in healthcare facilities particularly challenging. Risk-aware business process management is a new paradigm for better understanding such processes by identifying and evaluating the risks that go along with them. This paper focuses on analyzing the vulnerability of a hospital sterilization service through the use of a new framework, called e-BPRIM, which consists of the digitalization of the Business Process-risk management - Integrated Method (BPRIM). The e-BPRIM framework promotes and supports risk-aware process management with AdoBPRIM, a modeling environment using the ADOxx meta-modeling platform. The main e-BPRIM components will be introduced and then used to study the robustness of a given sterilization process taking into consideration several potential risks.

Nowadays, Business continuity management (BCM) has become an important topic in most sectors of activity, for government organizations as well as for business owners and their stakeholders, especially after the pandemic of COVID-19. For years, companies and organizations have been working on their BCM strategies to deal with disruptive events like floods, terrorist attacks, and pandemics. As the business environment is becoming increasingly competitive, having a well-established and clear BCM tool is a priority rather than merely providing verbose documents of business continuity plans(BCP) in which management rules and instructions are specified in a textual manner that makes their implementation very difficult. This present research sets out to fulfill this need by designing a dedicated tool, named "BECARE", which supports the efficient application of the BCM framework. Therefore, a literature analysis was conducted to investigate the scientific foundations of BCM in order to point out the main components and features needed to be implemented and supported by this dedicated tool. "BECARE" tool was implemented using the ADOxx meta-modeling platform which is openly available, allowing its adoption and use in BCM practice. Copyright (C) 2021 The Authors.

Risk-aware Business Process Management (R-BPM) has been addressed in research since more than a decade. However, the integration of the two independent research streams is still ongoing with a lack of research focusing on the conceptual modeling perspective. Such an integration results in an increased meta-model complexity and a higher entry barrier for modelers in creating conceptual models and for addressees of the models in comprehending them. Multi-view modeling can reduce this complexity by providing multiple interdependent viewpoints that, all together, represent a complex system. Each viewpoint only covers those concepts that are necessary to separate the different concerns of stakeholders. However, adopting multi-view modeling discloses a number of challenges particularly related to managing consistency which is threatened by semantic and syntactic overlaps between the viewpoints. Moreover, usability and efficiency of multi-view modeling have never been systematically evaluated. This paper reports on the conceptualization, implementation, and empirical evaluation of e-BPRIM, a multi-view modeling extension of the Business Process-Risk Management-Integrated Method (BPRIM). The findings of our research contribute to theory by showing, that multi-view modeling outperforms diagram-oriented modeling by means of usability and efficiency of modeling, and quality of models. Moreover, the developed modeling tool is openly available, allowing its adoption and use in R-BPM practice. Eventually, the detailed presentation of the conceptualization serves as a blueprint for other researchers aiming to harness multi-view modeling.