Keywords:Business process management Enterprise risk management Risk-aware business process management Model-driven engineering Meta-modeling Medication-use process a b s t r a c t Enterprise engineering deals with the design of processes which aim to improve the structure and efficiency of business organizations. It develops approaches based on modeling techniques, particularly on business process modeling, to ensure the quality and the global consistency of enterprise strategies and expectations. Nowadays, risk consideration in enterprise engineering is a growing concern since the business environment is becoming more and more competitive, complex, and unpredictable. To face this concern, a paradigm named risk-aware business process management (R-BPM) has recently emerged. It seeks to integrate the two traditionally isolated fields of risk management and business process management. Despite the significant benefits that can arise from the use of R-BPM, it suffers from a lack of solid scientific foundations and dedicated tooling. This present research work contributes to bridging that gap in a twofold way: (i) by establishing the BPRIM Business Process-Risk Integrated Method framework, and (ii) by designing a dedicated tool, named adoBPRIM which supports the efficient application of the BPRIM framework. This paper first comprehensively presents the foundation of BPRIM which is based on three main components and, secondly, its dedicated tool adoBPRIM which was designed using the ADOxx meta-modeling platform. An evaluation with a real case study in the health care domain shows the relevance of the methodological framework.
Hospitals are starting to move away from traditional based-systems to the information technology based-systems. Today, Internet of Things (IoT), Body Sensor Network (BSN), Modeling, Simulation, and Artificial Intelligence (AI) are core technology elements that will be used in hospital of the future to improve the quality of patient care. Collecting the patient's data and monitoring their states and behavior became mandatory to improve their care. This paper proposes a novel framework for supporting the hospital of the future named HospiT'Win. This framework uses the core technology elements mentioned above to create a digital twin, that is a virtual replica of the hospital, allowing the health care providers to trace the patient's pathways data, monitor their behaviors, and predict their near future outcomes. So that, they can provide the right care in a right place, and in a right time. The paper explains in details the main components, the structure, and the way to synchronize the state and the behavior of the digital twin with the patients pathways in the real hospital. In case of unexpected events, HospiT'Win predicts the near future to see their impact on the real hospital. Moreover, it provides the possible solutions to minimize the impact of these events to preserve the quality of health care inside the hospital.Index Terms-Digital Twin, Hospital of the Future, Internet of Things, Modeling, Simulation.
Part 10: Performance and OptimizationInternational audienceEach Emergency Medical Assistance Centre in France (SAMU), includes an emergency call service. It provides an adequate and immediate response to medical problems. The processing of the incoming calls can be seen as a collaborative process involving several stakeholders. The control of such a process is crucial. Indeed, the effectiveness of the response to these incoming calls strongly impacts the quality of service of these centres, which is the main information which the government relies for their funding. The aim of this paper is to analyse such a collaborative process, regarding the performance targets requested by the French government. To this end, we suggest applying a new approach, based on the combination of two well-known engineering techniques, in consecutive manner. We will first use process mining techniques to obtain meaningful knowledge about the studied collaborative processes, relying on real data from a French Emergency Medical Assistance Centre. Secondly, we will use a Discrete Event Simulation approach as an effective tool to assess the efficiency of the current management of this emergency call centre and to ask (and answer) some ‘what if?’ questions to identify possible ways of improving their effectiveness
As the number of elderly people increases, so does the need for innovative technology enabling to keep these old persons in their own home, as long as they so desire and as is technically and medically feasible. In the framework of a regional research project we are working on an information system aiming at managing the various processes involved in caring for elderly persons in their home.We present an information system that improves the management of the workflows involved in the homecare activity. Homecare workflows are modeled using the BPMN (Business Process Modeling Notation). An electronic version of the liaison logbook serves as the user interface of the workflow system. The alarms and information that are relevant to the carer's activity (nurse, doctor) are readily available when he/she logs into the system upon his/her arrival. The tasks to be performed are indicated and the carer has the opportunity to indicate whether and how these tasks have been performed. Each of the carers also has the opportunity to transmit relevant information to the other carers. The transmission of information from the home to the homecare association and back is also handled by the system. Finally, the instantiation of new workflows is made easier by the definition of patients' profiles that define typical workflows that might be useful for a specific type of patient.
Process mining results can be enhanced by adding semantic knowledge to the derived models. Information discovered due to semantic enrichment of the deployed process models can be used to lift process analysis from syntactic level to a more conceptual level. The work in this paper corroborates that semantic-based process mining is a useful technique towards improving the information value of derived models from the large volume of event logs about any process domain. We use a case study of learning process to illustrate this notion. Our goal is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved process analysis of the captured data. The approach involves mapping of the resulting learning model derived from mining event data about a learning process by semantically annotating the process elements with concepts they represent in real time using process descriptions languages, and linking them to an ontology specifically designed for representing learning processes. The semantic analysis allows the meaning of the learning objects to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are used to determine useful learning patterns by means of the Semantic Learning Process Mining (SLPM) algorithm -technically described as Semantic-Fuzzy Miner. To this end, we show how data from learning processes are being extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns through further semantic analysis of the discovered models.
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