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.

Human Activity Recognition (HAR) is essential for applications like health monitoring and fall detection using mobile sensors. However, obtaining large datasets necessary for training HAR systems is prohibitively expensive. Generative Adversarial Networks (GANs) have been proposed to generate synthetic HAR data, simplifying and enhancing the development of HAR systems. While these methods have improved accuracy, existing GAN-based approaches struggle with generating clear abnormal patterns, weakening anomaly detection capabilities. Available HAR data predominantly focuses on normal activities and does not target anomalies, making it challenging to detect anomalous situations. To address this, we propose a novel controllable GAN that generates realistic HAR data as well as distinct abnormal classes. This advancement enhances anomaly detection accuracy through more standardized activity recognition and quantification by HAR systems. We evaluate our approach on the WISDM dataset, demonstrating significant improvements in the balance and quality of synthetic data, leading to better performance in anomaly detection.

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,

The Medication Use Process (MUP) plays a vital role in ensuring the appropriate and safe use of medications. However, this process is exposed to many risks in particular medication errors. These later present a significant challenge, that may have negative consequences for patients. Implementing a medication error reporting and analyzing tool would facilitate an in-depth analysis of the causes of errors and enable the implementation of corrective measures. Additionally, a specific risk management model for medication management, which includes regular monitoring and preventive actions, is essential to prevent medication errors. By implementing these measures, We guarantee the attainment of secure and efficient management of MUP, resulting in better therapeutic results and heightened patient safety. In this work, we propose a tool that helps healthcare professionals create reports and analyze medication errors. This tool has been made and tested with healthcare professionals of the Intercommunal Hospital Center Castres-Mazamet (CHIC). This collaborative effort aimed not only to detect areas for improvement but also to instigate positive change in patient care. By leveraging our innovative tool, healthcare providers were empowered to proactively analyze and report medication errors.

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.

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.

Abstract 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.

To be competitive, transportation systems must be able to analyze and to evaluate, in real-time, critical differences between the short-term planned actions and the actual performed actions generating states of undesirable or unacceptable risk. We propose a method for monitoring the dynamic evolution of risk in the operational flow of a transportation system. It consists in an approach assessing risk associated to delays affecting the transportation operations. We use the FMEA (Failure Modes and Effects Analysis) around failure scenarios rather than failure modes, and we evaluate risk using probability and cost. A scenario probability is estimated dynamically in discrete points based on events occurrences during the process execution. The proposed approach useful to transportation managers is based on consistent and meaningful risk evaluation criteria to facilitate cost-based decisions during execution. The implementation of this method is performed by monitoring a container delivery process facing delays risks.

Image processing technology has grown significantly over the past decade. Its application on low-power mobile devices has been the interest of a wide research group related to newly emerging contexts such as augmented reality, visual search, object recognition, and so on. With the emergence of general-purpose computing on embedded GPUs and their programming models like OpenGL ES 2.0 and OpenCL, mobile processors are gaining a more parallel computing capability. Thereby, the adaptation of these advancements for accelerating mobile image processing algorithms has become actually an important topical issue. In this paper, our interest is based on reviewing recent challenging tasks related to mobile image processing using both serial and parallel computing approaches in several emerging application contexts.