Classical queueing models fail to properly staff non-conventional call centers with complex internal structures. This is either due to the difficulty of finding suitable models whose underlying assumptions hold, or due to certain elements of the call center not being modeled such as caller patience times. Relay call centers, service providers that connect two different interested parties with one another through telecommunication channels, present a prime example of non-conventional call centers. Working on the study case of a relay call center for the deaf community, Erlang C, one of the most commonly used call center staffing formulae, fails to generate agent staffing that meets our target performance criteria for quality of service. We propose a machine learning-based approach leveraging an available log of historical data. Upon comparing the proposed approach’s capability of performance evaluation and agent staffing to that of the Erlang C model and a baseline data-driven model, results indicate our approach’s staffing superiority. Considering uncertainty within the system variable predictions carried out prior to the staffing phase, our approach generates agent staffing which enables us to meet our global quality of service objective. • Classical queueing models challenged with staffing relay call center. • Machine learning-based approach tested against classical queueing models. • Machine learning-based staffing superiority over classical queueing models. • Agent staffing under uncertainty with global performance criteria.

Optical Character Recognition (OCR) is extremely useful in various sectors for exploring massive archived data. This technology enables the digitization of printed and handwritten texts that are frequently present in the medical field. For instance, medication error (ME) reports were previously and still in some healthcare facilities written manually, this has led to the accumulation of numerous handwritten data that are unfortunately challenging to exploit. Their digitization through OCR allows extracting important data from these documents and using them to populate the database to implement future analysis techniques to optimize the medication error management process. This paper presents a transformer-based handwritten recognition architecture that employs the Transformer-Based Optical Character Recognition (TrOCR) model combined with image segmentation techniques. Although the TrOCR model provided by Microsoft performs reasonably well in handwritten recognition, it is limited to English text because its pretrained version was trained exclusively on English samples. This limitation is problematic for us, as our task involves digitizing French medication dictation errors. Additionally, its limitation to processing single-line text images impairs its ability to recognize paragraphs. To address these limitations, we will fine-tune the model on French handwritten data and integrate a single-line level segmentation technique, thereby overcoming these constraints. Therefore, the preliminary results from implementing our proposed architecture are promising for the digitization of medication error reports.

Because of their potentially serious consequences, Medication errors (ME) represent a major challenge for health-care facilities. To manage these errors and minimize their seriousness, healthcare professionals follow a collaborative management process that relies on reporting and then analyzing the reports filled in by them via various reporting tools, both digital and paper-based. These tools can be customized requiring specific information to be entered in multiple forms with commonly the presence of textual descriptions to fill in a free-text field. Therefore, text analysis is crucial for thoroughly understanding and effectively analyzing medication errors. Given the large volume of reports to be quickly processed, it is essential to help healthcare professionals prioritize which ME to analyze. In this context, we propose, in this work, processing ME reports with natural language processing tasks using Transformer models such as GPT. In this study, we present the extraction of key information from the reports to help structure textual descriptions of ME with the GPT-4 transformer model. The results obtained show the potential of this model to extract relevant information from ME descriptions in French language without any deep fine-tuning,

Medication errors associated with the Medication Use Process present significant risks and require effective management. However, current practices do not address these risks. There is a lack of awareness that medication errors are unintended risks, and a lack of dedicated systems to manage them. To overcome these limitations, a digital system exploiting massive medical data (big data) is proposed. By integrating various data sources, this system aims to provide adaptable medication errors’ management and continuous improvement. This article presents an overview of the system requirements and highlights the potential of Data Mining in healthcare. Implementing this system could revolutionize medication error management and improve patient safety.

A virtual mirrored replica of the real-world, has become a new trend in recent years with the advent of Industry 4.0, shows how intelligent digital twins (DT) can unlock busi-ness value. The implementation of DT requires various types of technologies, including the Internet of Things (IoT), cloud computing, artificial intelligence, and others. Today, DTs can be used in various fields, including manufacturing, smart cities and healthcare. It can be used to monitor the real environment, predict its future, control its behavior, and improve its overall performance. Despite the aforementioned advantages of DTs, if not protected, they can also be considered as an open environment for attackers. If attackers take over a DT, they may end up owning the real environment controlled by the DT. This can result in damaging consequences. This paper explores the potential security and privacy threats that may be brought in through DTs. It proposes and presents a threat meta-model of DTs that identifies key security aspects of concern.

In this study, we address the performance evaluation of a multi-model call center. We provide an in-depth statistical data analysis to understand the dynamics of waiting times, service times and arrivals. Afterwards, we present our methodology on how to better size and schedule the agents in order to maintain a better service quality, namely more efficient utilization of resources and shorter waiting times.

Today, a majority of the elderly want to live longer in autonomy and comfort. Since there is not enough space available in specialized institutions, Home Health Care Services (HHCS) constitute an important addition that reinforces the traditional system. With the increase in HHCS demands, the main challenge in this distributed system is the organization of care services in an HHC institution. This organizational problem is widely studied as a Home Health Care Scheduling and Routing Problem (HHCSRP) in the Operations Research (OR) community. Facing the diversity and complexity of demands, as done in OR, the formulated solution-oriented model aims at providing decision making support for the decision maker in HHC systems. This leads obstacles in the dataset / benchmark based experimental environment and critical analysis such as the degree of the optimality regarding generated solutions and the comprehensive visualization of solutions. In this paper, based on a systemic analysis of the existing benchmark and the solution approach for HHCSRP, a prototype named BLP AD. Core is introduced. It provides a full decision support system to the decision-maker in the HHC system. The functionality consists of an instance generation based on the real dataset, operational planning generation and solution visualization. This research highlights the scientific challenges and attempts to respond to the complicated expectations of HHC systems in a comprehensive manner.

Nowadays, the majority of elderly people want to live longer in autonomy and well-being. As there are not enough places available in specialized institutions, Home Health Care Services are an alternative. With the fast rise of the demand, the organizations become aware of both existing limitations and of this potential for Research and Development (R&D). They manifest two types of need: one is to be able to identify their own HHC coordination problem, the other is to find solutions for this specific need. In this paper, we propose a systemic analysis of these two aspects to identify the complexity of the relationship between the offer and the demand for HHC services. Then, we present one prototype BLPAD for decision-making support of the coordination in HHC. This research work reveals scientific obstacles and attempts to define a comprehensive response to this expectation of the HHC services ecosystem.

To offer high quality services to patients, hospitals try to get a perception of patients' processes. This research work aims at identifying a type of processes known as patients' pathways and to devise a framework which could support the task of discovering patients' pathways. Existing modeling languages for discovering patients' processes fail at extracting the value and nature of each activity relevant to the whole process. To fill the identified gap, this paper uses a new approach for visualizing patients' pathways as Operational Process Charts (OPC). To do so, Real-Time Location Systems (RTLS) have been used to extract the primary event logs which contain the data related to movements of patients. Next, we have designed a meta-model which can filter and analyze the RTLS event logs and identify the different elements for the construction of OPC's. This paper focuses on presenting the DIAG meta-model for interpretation of event logs and the transformation of these data into opc's.

A hospital is an ecosystem that includes real-time services that require high human interaction on both resources level (doctor, nurses, etc.) and entities level (patients). Designing, planning, improving and controlling this system can be very challenging due to the system complexity governed by several subjective factors that affect the hospital interrelated functions or services. However, continuously changing health care needs that consistently face hospitals require them to keep continuously improving the efficiency of these services as demand increases and as new services are added. This paper proposes a new methodology that uses the concept of Digital Twin (DT) of hospital services based on Discrete Event Simulation (DES) integrated with health care information systems and Internet of things (IoT) devices. It develops a predictive decision support model that employs real-time services data drawn from these systems and devices. This model enables assessing the efficiency of existing health care delivery systems and evaluating the impact of changes in services without disrupting daily activities of the hospital. The developed model, a digital twin (or a virtual replica of the hospital), simulates a number of key hospital health delivery services, based on relevant data retrieved in real-time. Although the model simulates four key services, initially as a proof of concept, but it proposes a general framework, which can be expanded to include other services. The demonstrated proof-of-concept shows that it achieves better planning and improvement of usage of resources, and thus enabling both practitioners and management to examine any model changes to foresee the effectiveness or efficiency of services before they are applied in reality.